Acknowledgements

Many friends and colleagues provided encouragement and assistance in the development of different versions of MEGA  since 1993.  Test versions of MEGA2 were used in the research laboratories of the authors and in the classroom teaching by Sudhir Kumar at the Arizona State University and by Masatoshi Nei at the Pennsylvania State University.  These provided excellent opportunities to test the early alpha and beta versions of the program, and the students drew our attention to many inconsistencies in the user-interface and the online help manual.  Three different beta versions of MEGA2 were made available in the public domain from June 2000 – February 2001 and were downloaded by over 2500 different users.  We thank these volunteers for donating their time in testing MEGA2 and providing us with feedback on the user-interface design, software bugs, and documentation inconsistencies.  Almost all facets of design and implementation benefited from their comments and suggestions.

We are particularly grateful to Dr. Sudhindra Gadagkar (Arizona State University) for reading the entire online help and making countless useful writing and stylistic suggestions for improving the help content and the user-interface.  We also thank Ms. Laural Casler (Media Specialist) for assistance in creating and managing the MEGA2 website, generating the on-line helping, and capturing screen snapshots for use in the help-content.  MEGA software project is supported by research grants NIH (HG-02096), NSF (DBI-9983133) and Burroughs-Wellcome Fund (1001311) to S.K. and NIH (GM20293), NSF (DEB-9520832) and NASA (NCC2-1057) to M.N.


System Requirements

MEGA2 is developed for use on Microsoft Windows® operating systems: Windows 95/98, NT, ME, 2000, XP, or later.  We recommend a computer with at least 32 MB of RAM, 10 MB hard disk space, and an entry- level Pentium processor or equivalent.  Our tests show that MEGA2 runs well even on computers with an 80486 CPU.  However, for analysis of large datasets, you should have a faster processor and larger amount of physical memory (RAM) for quick computation.  MEGA2 can also be run on other operating systems for which Windows emulators are available.

Platform	Software
Macintosh	SoftWindows98 or VirtualPC
Sun Workstation	SoftWindows95
Linux	Windows by VMWare


Installing MEGA

The most convenient and preferred way to install MEGA2 is to install it directly from the website (www.megasoftware.net).  In this case, a specially designed Installation program automatically downloads MEGA2 to your computer and Installs it in the location (directory) you specify during the installation procedure.

If you are unable to install MEGA2 directly from the website, the second option is to download the MEGA2 program as a single compressed ZIP file to your computer.  You will need to use a program to uncompress this ZIP file (e.g., WinZip software) in a temporary directory.  This will produce a number of files.  Click on the SETUP.EXE file to install MEGA2 on your computer automatically.

Finally, you may install MEGA2 from a CD or floppy disks obtained from the authors.  In these cases, insert the media (CD or the floppy diskette #1) in the computer and then click on SETUP.EXE to install MEGA2.

We recommend that you install MEGA2 in one of the three ways described above.  Please do not simply copy MEGA2-related files from one computer to another; MEGA2 may not work properly if installed in this manner.


Uninstalling MEGA

In Windows the preferred way to uninstall programs is to use the Add/Remove Programs option in the Control Panel, which is accessible from the Start button on the lower left corner of your computer desktop.  A dialog box (usually named Add/Remove programs) will display a list of programs.  Scroll down to MEGA2 so that it is highlighted.  Click on the Add/Remove button to uninstall MEGA2.


Technical Support and Updates

All minor (bug fix) and major updates of MEGA will be made available at the website www.megasoftware.net.  We will also send e-mail to all MEGA users whenever an updated version of the MEGA program and/or the online help manual is made available.

If you encounter any problems in installing and using MEGA2, please report the problem to us (see Reporting Bugs).


Reporting Bugs

If you encounter technical problems such as unexplained errors, documentation inconsistencies, or program crashing, please report them to us by e-mail at bugs@megasoftware.net  For further information on reporting problems, please consult the bug report page on MEGA web site (www.megasoftware.net).  Please note that telephone inquiries will not be accepted.

Please include the following information in your report: (1) Your name and address, (2) MEGA version number, (3) Windows version, (4) copy of the data file [if possible], (5) description of the problem, and (6) the sequence of events that led to that problem [this is often crucial to understanding and remedying the problem quickly].


Guide to Notations Used

Item	Convention	Example
Directory & file names	Small Cap + Bold	INSTALL.TXT
File name extensions	Small Cap + Bold	.TXT, .DOC, .MEG
Email address/URLs	Underlined	www.megasoftware.net
Pop-up help links	Dotted Underlined + Green	statement
Help Jumps	Underlined + Green	set of rules
Menu/Screen Items	Italic	Data Menu
User-Entered Text	Monospace font	!Title


Disclaimer

Although utmost care has been taken to ensure the correctness of the software, the software is provided "as is" without any warranty of any kind.  In no event shall the authors and their employers be considered liable for any damages, including but not limited to special, consequential, or other damages.  Authors specifically disclaim all other warranties, expressed, or implied, including but not limited to the determination of suitability of this product for a specific purpose, use or application.

Note that brand and product names (e.g., Windows and Delphi) are trademarks or registered trademarks of their respective holders.


Using MEGA in the Classroom

Because MEGA includes many statistical methods for the study of molecular evolution in an interactive framework, it is suitable for classroom teaching.  If you are interested in using MEGA for classroom teaching, you may ask your students to download a copy of MEGA from the web site www.megasoftware.net.  Alternatively, you may install copies of MEGA on multiple computers in a common computing area.  For use of MEGA in these ways, there are no restrictions.  However, if you want to use MEGA in any other form, please contact the authors by e-mail (kumar@megasoftware.net or s.kumar@asu.edu).

If you are using MEGA2 in classroom teaching, we would much appreciate if you will send us the following information by e-mail (kumar@megasoftware.net) for our records.  (1) Your name and position with the institution, (2) Course number and title, (3) Number of students, and (4) year and semester.


Copyright

Copyright © 1993, 1994, 2000, 2001.

This software is protected under the copyright law.  No part of this manual or the program design may be reproduced without written permission from copyright holders.  Please e-mail all enquires to kumar@megasoftware.net or s.kumar@asu.edu.


Preface

Genome sequencing and individual laboratory projects are generating vast amounts of DNA sequence data from diverse organisms. As a result, gene sequence databases are now growing at a rapid pace.  In order to conduct efficient analytical analyses of these sequence data, there is a need to develop easy-to-use computer programs, fast computational algorithms, and useful statistical methods.

The objective of the MEGA2 software is to provide tools for exploring, discovering, and analyzing DNA and protein sequences from evolutionary perspectives.  The first version of MEGA made many methods of evolutionary analysis easily accessible to the scientific community for research and education.  However, it was developed keeping in mind the limited computational resources available on the average personal computer (DOS-based IBM PC-compatibles) in early 1990s.  The situation has changed dramatically in recent years.  The computing power of the average desktop computer has increased manifold, graphical interfaces have become commonplace, and molecular sequence data sets have grown enormously.  The development of MEGA2 was, therefore, undertaken to harness the expanded computing power to fulfill the fast growing need for extensive biological sequence analysis and exploration software.  It expands the scope of its predecessor from a single gene analysis to genome wide analyses. MEGA2 vastly extends the capabilities of MEGA version 1 by (1) facilitating the analysis of datasets of virtually unlimited size, (2) enabling creation of groups of sequences for molecular phylogenetic analysis at different taxonomic levels, (3) enabling specification of domains (a domain is a block of nucleotide or amino acid sites) and genes (collection of domains) for genomic analyses, (4) expanding the repertoire of statistical methods for computing evolutionary distances, reconstructing phylogenetic trees, and conducting tests of selection, and (5) including new modules for visual representation of input data and output results.

MEGA2 is specifically designed to reduce the amount of time needed for day-to-day mundane non-technical tasks in data analysis and to provide statistical methods of molecular evolutionary genetic analysis in an easy-to-use computing workbench.  While MEGA2 is re-written completely afresh, we have made a special effort to retain the user-friendly interface that researchers have come to identify with MEGA.  The user interface of the new version is designed such that the information to conduct analysis is obtained from the user only on a need-to-know basis.  Furthermore, the data subsets and output results are no longer required to be stored in files for viewing unless the user specifically needs to do so.  These features required the development of different input data and output explorers, which have further enhanced the user-friendliness of the interface.

As for future developments, we plan to expand the set of evolutionary methods included in this version and the types input data that can be analyzed.  Some of the major upcoming additions in the next few sub-releases are modules for converting data files in other formats to the MEGA format, ability to read and use user-specified tree topologies in phylogenetic analyses, and the two-cluster and phylogenetic tests of molecular clocks.  We also plan to include statistical methods for (1) inferring the ancestral nucleotide and amino acid sequences for a set of present-day sequences, (2) detecting selection that is likely to have occurred in the ancestral sequences, (3) estimating the gamma parameter, (4) estimating phylogenetic trees and fixation indices from allele frequency data, and (5) estimating nucleotide diversities from RFLPs.  Currently, computer programs for these methods are available from http://mep.bio.psu.edu, but they are a collection of different programs written from different authors.


Input Data and Formats

Input file formats for different kinds of data are discussed in this section. Note that there is no limit on the amount of molecular sequence or distance matrix data that can be analyzed in MEGA; the size of the data set is constrained only by the physical (RAM) and virtual memory available on your computer.

Also, note that the best way to learn about the input data formats is to examine the example data files (see EXAMPLES subdirectory in the directory where MEGA is installed).


MEGA Format

Your data should be formatted according to a set of rules so it can be read and interpreted correctly in MEGA.  All input data files are basic ASCII-text files, which may contain DNA sequence, protein sequence, evolutionary distance, or phylogenetic tree data.  Most word processing packages (e.g., MicrosoftWord, WordPerfect, Notepad, WordPad) allow you to edit and save ASCII text files.  These are usually marked with a .TXT extension.  It is preferable if you change this extension to .MEG after creating the file, so you can distinguish between your data files and the other text files.  Because the organizational details vary for different types of data, we discuss the data formats for molecular sequences, distances, and phylogenetic trees separately.  However, there are a number of features that are common to all MEGA data files, which are as follows.

Common Features

The first line must contain the keyword #MEGA to indicate that the data file is in the MEGA format.

The data file may optionally contain a succinct description of the data (called Title) included in the file on the second line.  The Title statement is written according to a set of rules.  This title is copied to every output file from MEGA.  In the long run, an informative title will allow you to easily recognize the past work.

The data file may also contain a more descriptive multi-line account of the data in the Description statement, which is written after the Title statement.  The Description statement is also written according to a set of rules.  Unlike the Title statement, the description statement is not copied to every output file from MEGA.

In addition, the data file may also contain a Format statement, which includes information on the type of data present in the file and some of its attributes.  The Format statement should be generally written after the Title or the Description statement.  Writing a format statement requires knowledge of keywords used to identify different types of data and data attributes.

All taxa names must be written according to a set of rules.

Comments can be written anywhere in the data file (and can span multiple lines).  They must always be enclosed in square brackets ([ and ]) brackets and can be nested.


Key Words

In addition to the MEGA and TITLE keywords, there are a number of keywords supported by MEGA to write instructions in the format statements and the command statements. These key words can be written in any combination of lower- and upper-case letters.  For writing instructions, you follow the style given in the examples along with the keyword description for different types of data.


Rules for Taxa Names

Distance matrices as well as sequence data may come from species, populations, or individuals. These evolutionary entities are designated as OTUs (Operational Taxonomic Units) or taxa. Each taxon must have an identification tag, i.e., a taxon Iabel. In the input files prepared for use in MEGA, these labels should be written according to the following conventions.

‘#’ Sign

Every Iabel must be written on a new line, and a '#' sign must proceed the label.  There is no restriction on the length of the Iabels in the datafile, but MEGA2 will truncate all labels longer than 40 characters. These labels are not required to be unique, but identical labels may result in ambiguities and they should be avoided if possible.

 Characters to use in labels

Taxa labels must start with an alphanumeric characters (0-9, a-z, and A-Z) or a special character: dash (-), plus (+) or period (.).  After the first character, the taxon label may contain an the following additional special characters: underscore (_), asterisk (*), colon (:), round open and close brackets, vertical line (|),back slash (\), and forward slash (/).

For multiple word labels, an underscore can be used to represent a blank space. All underscores are converted into blank spaces, and subsequent displays of the labels show this change. For example, E._coli becomes E. coli.


Rules for Title Statement

A Title statement must be written on the line following the #mega.  It always begins with !Title and ends with a semicolon.

#mega
!Title  This is an example title;

A title statement may not occupy more than one line of text, and it must not contain a semicolon inside the statement (but must contain one at the end of the statement).


Rules for Description Statement

A Description statement is written after the Title statement. It always begins with !Description and ends with a semicolon.

#mega

!Title  This is an example title;
!Description  This is detailed information the data file;

A description statement may occupy multiple lines of text, but it must not contain a semicolon inside the statement (but must contain one at the end of the statement).


General Considerations (Sequence Data)

The sequence data must consist of two or more sequences of equal length. All sequences must be aligned (MEGA does not include an alignment program yet).  Nucleotide and amino acid sequences should be written in IUPAC single-letter codes.  Sequences can be written in any combination of upper- and lower-case letters.  Special symbols for alignment gaps, missing data, and identical sites can also be included in the sequences.

Special Symbols

Blank spaces and Tabs are frequently used to format data files, so they are simply ignored by MEGA. ASCII characters such as the period (.), dash (-), and question mark (?), are generally used as special symbols to represent identity to the first sequence, alignment gaps, and missing data, respectively.


General Considerations (Distance Data Formats)

For a set of m sequences (or taxa), there are m(m-1)/2 pairwise distances. These distances can be arranged either in the lower-left or in the upper-right triangular matrix (see below). After writing the #mega,!Title,!Description, and !Format commands (some of which are optional), you need to write all the taxa names first (see below).  Taxa names are followed by the distance matrix (as shown below).

#one

#two

#three

#four

#five

1.0	2.0	3.0	4.0

  	3.0	2.5	4.6

1.3	3.6

4.2

In the above example, pairwise distances are written in the upper triangular matrix (upper-right format).  Two alternate distance matrix formats are:

Lower-left matrix	Upper-right matrix
d12				d12	d13	d14	d15
d13	d23				d23	d24	d25
d14	d24	d34				d34	d35
d15	d25	d35	d45				d45


MEGA Tree Format

This format will be described in the on-line manual of MEGA version 2.1, in which we plan to provide the user tree reader and analysis system.


Distance Data Subset Selection

In MEGA, taxa can be selected to construct a distance matrix containing a subset of taxa.  You may select Select Taxa (sequences) or Groups of taxa through the Setup/Select Taxa & Groups dialog box.  You can also select sequences in the Distance Data Explorer by clicking on check marks next to the taxa names.


Tree Data

This section of the online help will be completed in MEGA version 2.1.


Rules for Format Statement
Rules for the Format Statement

A format statement contains one or more command statements.  A command statement contains a command and a valid setting keyword (command=keyword format).  For example, the command statement DataType=Nucleotide tells MEGA that nucleotide sequence data is contained in the file.  Based on the DataType setting, different types of keywords are valid.

Keywords for Sequence DataRH_Keywords_for_Format_Statement_Sequence_data

Keywords for Distance DataRH_Keywords_for_Format_Statement_Distance_data

Keywords for Tree Data


Keywords for Format Statement (Sequence data)

Command	Setting	Remark	Example
DataType	DNA, RNA, nucleotide, protein	Specifies the type of data in the file	DataType=DNA
NSeqs	A count	Number of sequences	NSeqs=85
NTaxa	A count	Synonymous with NSeqs	NTaxa=85
NSites	A count	Number of nucleotides or amino acids	NSites=4592
Property	Exon, Intron, Coding, Noncoding, and End.	specifies whether or not a domain is protein coding.   Exon and Coding are synonymous, as are the Intron and Noncoding keywords.  End specifies that the domain with the given name ends at this point..	Property=cyt_b
Indel	single character	Use dash (-) to identify insertion/deletions in sequence alignments	Indel=-
Identical	single character	Use period (.) to show identify with the first sequence.	Identical=.
MatchChar	single character	Synonymous with the Identical keyword.	MatchChar=.
Missing	single character	Use a question mark (?) to indicate missing data.	Missing=?
CodeTable	A name	This instruction gives the name of the code table for the protein coding domains of the data	CodeTable=Standard


Keywords for Format Statement (Distance data)

Command	Setting	Remark	Example
DataType	Distance	Specifies that the distance data is in the file	DataType=distance
NSeqs	A count	Number of sequences	NSeqs=85
NTaxa	A count	Same as NSeqs	NTaxa=85
DataFormat	Lowerleft, upperright	Specifies whether the data is in lower left triangular matrix or the upper right triangular matrix	DataFormat=lowerleft

Examples showing the lower-left and the upper-right formats for a five sequence dataset.  Note the different order in which distances are organized in the two cases.

Lower-left matrix	Upper-right matrix
d12				d12	d13	d14	d15
d13	d23				d23	d24	d25
d14	d24	d34				d34	d35
d15	d25	d35	d45				d45


IUPAC single letter codes

Nucleotide or amino acid sequences should be written in IUPAC single-letter codes. Single-letter codes supported in MEGA are as follows.

Symbols	Name	Remarks
DNA/RNA
A	Adenine	Purine
G	Guanine	Purine
C	Cytosine	Pyrimidine
T	Thymine	Pyrimidine
U	Uracil	Pyrimidine
R	Purine	A or G
Y	Pyrimindine	C or T/U
M		A or C
K		G or T
S	Strong	C or G
W	Weak	A or T
H	Not G	A or C or T
B	Not A	C or G or T
V	Not U/T	A or C or G
D	Not C	A or G or T
N	Ambiguous	A or C or G or T
Protein
A	Alanine	Ala
C	Cysteine	Cys
D	Aspartic Acid	Asp
E	Glutamic Acid	Glu
F	Phenylalanine	Phe
G	Glycine	Gly
H	Histidine	His
I	Isoleucine	Ile
K	Lysine	Lys
L	Leucine	Leu
M	Methionine	Met
N	Asparagine	Asn
P	Proline	Pro
Q	Glutamine	Gln
R	Arginine	Arg
S	Serine	Ser
T	Threonine	Thr
V	Valine	Val
W	Tryptophan	Trp
Y	Tyrosine	Tyr
*	Termination	*


Writing Comments

Comments can be placed in the data file anywhere as long as they are contained within a pair of square brackets ([ and ]).  Nested comments are allowed.


Writing Command Statements for Defining Genes and Domains

MEGA format is particularly designed to make it easy to designate genes and domains in the molecular sequence data.  In this format, attributes of different sites (and groups of sites: domains) are specified within the data "on the spot", rather than in an attributes block before or after the actual data (as is the case in some other data formats).  For example, a three-sequence data set with three genes written in MEGA format is shown below.  It contains sequence information of genes named FirstGene, SecondGene, and ThirdGene for two groups of organisms Setup/Select Genes/Domain (Mammals and Birds).  (Note that the Genes and Domains can also be defined interactively through a dialog box.)

!Gene=FirstGene  Domain=Exon1  Property=Coding;

#Human_{Mammal}  ATGGTTTCTAGTCAGGTCACCATGATAGGTCTCAAT

#Mouse_{Mammal}  ATGGTTTCTAGTCAGGTCACCATGATAGGTCCCAAT

#Chicken_{Aves}  ATGGTTTCTAGTCAGCTCACCATGATAGGTCTCAAT

!Gene=SecondGene  Domain=Intron Property=Noncoding;

#Human     ATTCCCAGGGAATTCCCGGGGGGTTTAAGGCCCCTTTAAAGAAAGAT

#Mouse     GTAGCGCGCGTCGTCAGAGCTCCCAAGGGTAGCAGTCACAGAAAGAT

#Chicken   GTAAAAAAAAAAGTCAGAGCTCCCCCCAATATATATCACAGAAAGAT

!Gene=ThirdGene  Domain=Exon2  Property=Coding;

#Human      ATCTGCTCTCGAGTACTGATACAAATGACTTCTGCGTACAACTGA

#Mouse      ATCTGATCTCGTGTGCTGGTACGAATGATTTCTGCGTTCAACTGA

#Chicken    ATCTGCTCTCGAGTACTGCTACCAATGACTTCTGCGTACAACTGA


Writing Command Statements for Defining Groups of Taxa

MEGA format allows you to assign different taxa to groups in sequence as well as distance data files.  In this case, the name of the group is written in a set of curly brackets following the taxa name.  The group name can be attached to the taxa name using an underscore or just appended.  It is important to note that there should be no spaces between the taxa name and group name (enclosed in curly brackets).  (Note that the Groups of taxa can also be defined interactively through a dialog box.)  In the following we show an example in which Human and Mouse are designated as the members of the group Mammal and chicken belongs to group Aves.

!Gene=FirstGene  Domain=Exon1  Property=Coding;

#Human_{Mammal}  ATGGTTTCTAGTCAGGTCACCATGATAGGTCTCAAT

#Mouse_{Mammal}  ATGGTTTCTAGTCAGGTCACCATGATAGGTCCCAAT

#Chicken_{Aves}  ATGGTTTCTAGTCAGCTCACCATGATAGGTCTCAAT

!Gene=SecondGene  Domain=Intron Property=Noncoding;

#Human     ATTCCCAGGGAATTCCCGGGGGGTTTAAGGCCCCTTTAAAGAAAGAT

#Mouse     GTAGCGCGCGTCGTCAGAGCTCCCAAGGGTAGCAGTCACAGAAAGAT

#Chicken   GTAAAAAAAAAAGTCAGAGCTCCCCCCAATATATATCACAGAAAGAT

!Gene=ThirdGene  Domain=Exon2  Property=Coding;

#Human      ATCTGCTCTCGAGTACTGATACAAATGACTTCTGCGTACAACTGA

#Mouse      ATCTGATCTCGTGTGCTGGTACGAATGATTTCTGCGTTCAACTGA

#Chicken    ATCTGCTCTCGAGTACTGCTACCAATGACTTCTGCGTACAACTGA


Keywords for Command Statements (Genes/Domains)

Command	Setting	Remark	Example
Domain	A name	This instruction defines a domain with the given name	Domain=first_exon
Gene	A name	This instruction defines a gene with the given name	Gene=cytb
Property	Exon, Intron, Coding, Noncoding, and End.	This instruction specifies the protein- coding attribute for a domain. Keywords Exon and Coding are synonymous; similarly Intron and Noncoding are synonymous.  End specifies that the domain with the given name has ended at that point.	Property=cytb
CodonStart	A number	This instruction specifies the site where the next 1st-codon position will be found in a protein-coding domain.	CodonStart=2


Built-in Genetic Codes

MEGA2 contains four commonly used genetic code tables: (1) Standard, (2) Vertebrate mitochondrial, (3) Drosophila mitochondrial, and (4) Yeast mitochondrial.  They can be used as templates to create additional genetic code tables using the Genetic Code Selector.  Genetic codes for these four built-in tables in one letter code are given below.

      Code Table              Code Table

Codon 1  2  3  4        Codon 1  2  3  4

UUU   F  F  F  F        AUU   I  I  I  I

UUC   F  F  F  F        AUC   I  I  I  I

UUA   L  L  L  L        AUA   I  M  M  I

UUG   L  L  L  L        AUG   M  M  M  M

UCU   S  S  S  S        ACU   T  T  T  T

UCC   S  S  S  S        ACC   T  T  T  T

UCA   S  S  S  S        ACA   T  T  T  T

UCG   S  S  S  S        ACG   T  T  T  T

UAU   Y  Y  Y  Y        AAU   N  N  N  N

UAC   Y  Y  Y  Y        AAC   N  N  N  N

UAA   *  *  *  *        AAA   K  K  K  K

UAG   *  *  *  *        AAG   K  K  K  K

UGU   C  C  C  C        AGU   S  S  S  S

UGC   C  C  C  C        AGC   S  S  S  S

UGA   *  W  W  W        AGA   R  *  S  R

UGG   W  W  W  W        AGG   R  *  S  R

CUU   L  L  L  T        GUU   V  V  V  V

CUC   L  L  L  T        GUC   V  V  V  V

CUA   L  L  L  T        GUA   V  V  V  V

CUG   L  L  L  T        GUG   V  V  V  V

CCU   P  P  P  P        GCU   A  A  A  A

CCC   P  P  P  P        GCC   A  A  A  A

CCA   P  P  P  P        GCA   A  A  A  A

CCG   P  P  P  P        GCG   A  A  A  A

CAU   H  H  H  H        GAU   D  D  D  D

CAC   H  H  H  H        GAC   D  D  D  D

CAA   Q  Q  Q  Q        GAA   E  E  E  E

CAG   Q  Q  Q  Q        GAG   E  E  E  E

CGU   R  R  R  R        GGU   G  G  G  G

CGC   R  R  R  R        GGC   G  G  G  G

CGA   R  R  R  R        GGA   G  G  G  G

CGG   R  R  R  R        GGG   G  G  G  G


Adding/Modifying Genetic Code Tables

You may add new genetic code tables and/or edit existing code tables in the Genetic Code Selector.  All changes made will be remembered by MEGA for all future analyses.


Computing Statistical Attributes (Genetic Code)

There is a significant amount of redundancy in the genetic code because most of the amino acids are encoded by multiple codons.  Therefore, it is interesting to know the degeneracy of each codon position in all codons.  This information can be computed for any code table in MEGA2 in the Genetic Code Selector. In addition to the degeneracy of the codon positions, MEGA2 also writes the number of synonymous sites and the number of nonsynonymous sites for each codon using the Nei and Gojobori (1986) method.  An example of the results obtained for the Standard genetic code is given below.

 Code Table: Standard

Method:     Nei-Gojobori (1986) methodology

S = No. of synonymous sites

N = No. of nonsynonymous sites

---------------------------------

-----    No of Sites   Redundancy

-----     for codon   Pos Pos Pos

Codon      S     N    1st 2nd 3rd

-----    ----- -----  --- --- ---

UUU (F)  0.333 2.667   0   0   2

UUC (F)  0.333 2.667   0   0   2

UUA (L)  0.667 2.333   2   0   2

UUG (L)  0.667 2.333   2   0   2

UCU (S)  1.000 2.000   0   0   4

UCC (S)  1.000 2.000   0   0   4

UCA (S)  1.000 2.000   0   0   4

UCG (S)  1.000 2.000   0   0   4

UAU (Y)  1.000 2.000   0   0   2

UAC (Y)  1.000 2.000   0   0   2

UAA (*)  0.000 3.000   0   0   0

UAG (*)  0.000 3.000   0   0   0

UGU (C)  0.500 2.500   0   0   2

UGC (C)  0.500 2.500   0   0   2

UGA (*)  0.000 3.000   0   0   0

UGG (W)  0.000 3.000   0   0   0

CUU (L)  1.000 2.000   0   0   4

CUC (L)  1.000 2.000   0   0   4

CUA (L)  1.333 1.667   2   0   4

...

...

---------------------------------


Importing Data From Other Formats

MEGA2 supports conversions from several different file formats into MEGA2 format.  Each format is indicated by the file extension used.  Supported formats include (with sample extensions):

File type

.aln	CLUSTAL

.nexus	PAUP, MacClade

.phylip	PHYLIP Interleaved

.phylip2	PHYLIP Noninterleaved

.gcg	GCG format

.fasta	FASTA format

.pir	PIR format

.nbrf	NBRF format

.msf	MSF format

.ig	IG format

.xml	Internet (NCBI) XML format

The following sections describe each of these formats briefly and how Mega handles their conversion.

COMMON FILE CONVERSION ATTRIBUTES

The default input formats are determined by a file’s extension (e.g., a file with the extension of ".ig" will be initially assumed to be in the "IG" input format).   However, you have the option to specify any format for any file; therefore, the file extension is simply used as an initial guide.  Note that the specification of an incorrect file format will most often result in erroneous conversion or other unexpected errors.

All input file types can include any of the following characters in their sequence data:

The letters: a-z,A-Z for DNA and protein sequences

Peroid (.)

Hyphen (-)

The space character

Question mark (?).

Depending on their context, all other characters encountered in input files are either ignored or are interpreted as specific non-sequence data, such as comments, headers, etc.

The first line of all converted files is always: #Mega

The second line of all converted file is always: !Title: <filename>

where <filename> is the name of the input file.

The third line of all converted files is blank.

Many formats have a way of specifying the length of the sequences contained in them.  The Mega conversion utility ignores this data and does not check to see if the sequences are, indeed, as long as they are purported to be.


Converting IG Format Files

IG  Format

These files consist of one or more groups of non-blank lines separated by one or more blank lines; the non-blank lines look similar to this:

;G028uaah  240 bases

G028uaah

CATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATTAAAGACTTGTT

TAAACACAAAATTTAGACTTTTACTCAACAAAAGTGATTGATTGATTGAT

Each group begins with a line that starts with a semicolon.  This line is ignored by MEGA2.  The following line (e.g., G028uaah above) is treated as the name of the sequence.  The lines following it are taken as the sequence until the next semicolon is found.  MEGA2 will recognize the letters (a-z and A-Z for DNA and protein sequences) and only a few special characters (period [.], hyphen [-], space, and question mark [?]).  Depending on their context, all other characters encountered in input files are either ignored or are interpreted as specific non-sequence data, such as comments, headers, etc.

The example above would be converted to the MEGA file format as follows:

#mega

!Title: filename

#G019uabh

ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAA

GTCTTGCTTGAATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTAC


Converting NBRF Format

Converting NBRF Format

These files consist of one or more groups of non-blank lines separated by one or more blank lines; the non-blank lines look similar to this:

>P1;Chloroflex

Chloroflex  428 bases

 MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV

 NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC

 DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG

 VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS

 VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR

 REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV

 KLEVDGHRGE LTLARA-ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD

 PLETQD---- ---DAVRIER AVEEVLVP-- ---------- ----------

 ---------- ---------- --------*

Each group begins with a line that starts with a greater-than symbol (‘>’).  This line is ignored.  The first word in the following line (e.g., Chloroflex above) is treated as the name of the sequence, and the rest of that line is ignored.  The lines following it are taken as the sequence.  This example would be converted to the MEGA file format as follows:

  #mega

  !Title: filename

  #Chloroflex

  MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV

  NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC

  DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG

  VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS

  VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR

  REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV

  KLEVDGHRGE LTLARA-ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD

  PLETQD---- ---DAVRIER AVEEVLVP-- ---------- ----------

  ---------- ---------- --------


Converting XML format

Converting XML Format

These files consist of a group of XML tags and attribute values.  A DOCTYPE header may or may not be present.  This is a relatively new format and is subject to revision.  The MEGA input converter for XML file formats does not implement a full parser.  In fact, it only looks for a few specific tags that might be present.  For example, an XML file might contain the following data:

<Bioseq-set>

  <Bioseq>

    <name>G019uabh</name>

    <length>240</length>

    <mol>DNA</mol>

    <cksum>302C447C</cksum>

    <seq-data>ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATT

AAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGATTGATGGTT

TACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACATTGCTGCTTAGAGT

CAAAGCATGTACTTAGAGTT</seq-data>

  </Bioseq>

</Bioseq-set>

The MEGA format converter only looks for the following two tags:

<name>G019uabh</name>

<seq-data>ATACATCATAACACTAC. . .</seq-data>

If it finds these tags, it uses the text between <name>. . .</name> tags as the sequence’s name, and the text between the <seq-data>. . .</seq-data> tags as the sequence data corresponding to that name. The conversion of the above XML block into Mega format would look like this:

#Mega

Title: filename.xml

#G019uabh

ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATT

AAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGATTGATGGTT

TACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACATTGCTGCTTAGAGT


Converting PIR Format

Converting PIR Format

These files consist of groups of non-blank lines that look similar to this:

ENTRY           G006uaah

TITLE           G019uabh 400 bp 240 bases

SEQUENCE

                5        10        15        20        25        30

      1 A C A T A A A A T A A A C T G T T T T C T A T G T G A A A A

     31 T T A A C C T A N N A T A T G C T T T G C T T A T G T T T A

     61 A G A T G T C A T G C T T T T T A T C A G T T G A G G A G T

     91 T C A G C T T A A T A A T C C T C T A A G A T C T T A A A C

    121 A A A T A G G A A A A A A A C T A A A A G T A G A A A A T G

    151 G A A A T A A A A T G T C A A A G C A T T T C T A C C A C T

    181 C A G A A T T G A T C T T A T A A C A T G A A A T G C T T T

    211 T T A A A A G A A A A T A T T A A A G T T A A A C T C C C C

The Mega format converter looks for the "ENTRY" tag and treats the string that follows it as the sequence’s name, e.g., G006uaah above.  The remaining lines have their digits and spaces removed, and then if they contain any non-sequence characters, they are deleted also.  Mega would convert the above sequence as follows:

#mega

Title: filename.pir

#G006uaah

ACATAAAATAAACTGTTTTCTATGTGAAAA

TTAACCTANNATATGCTTTGCTTATGTTTA

AGATGTCATGCTTTTTATCAGTTGAGGAGT

TCAGCTTAATAATCCTCTAAGATCTTAAAC

AAATAGGAAAAAAACTAAAAGTAGAAAATG

GAAATAAAATGTCAAAGCATTTCTACCACT

CAGAATTGATCTTATAACATGAAATGCTTT

TTAAAAGAAAATATTAAAGTTAAACTCCCC


Convert GCG Format

Converting GCG Format

These files consist of one or more groups of non-blank lines separated by one or more blank lines; the non-blank lines look similar to this:

Chloroflex

    Chloroflex  Length: 428  Mon Sep 25 17:34:20 MDT 2000  Check: 0  ..

   1 MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV

  51 NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC

 101 DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG

 151 VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS

 201 VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR

 251 REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV

 301 KLEVDGHRGE LTLARA.ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD

 351 PLETQD.... ...DAVRIER AVEEVLVP.. .......... ..........

 401 .......... .......... ........

The line containing the "Check" tag near the end of the line is used to signify the first line in a new sequence expression.  The sequence’s name is obtained from the line preceeding this one, and the following lines are accepted as the sequence, up to the next blank line.  The leading digits are stripped off, and the rest of the line is used as the sequence data.  The above sequence is converted into a format that appears as follows.

#m4ega

Title: infile.gcg

#Chloroflex

MSKEHVQTIA TDDVSKNGHT PPTNASTPPY PFVAIVGQAE LKLALLLCVV

NPTIGGVMVM GHRGTAKSTA VRALAAMLPP IKAVAGCPYS CAPDRTAGLC

DQCRALEQQS GKTKKPAVIN IPVPVVDLPL GATEDRVCGT LDIERALTQG

VQAFAPGLLA RANRGFLYID EVNLLEDHLV DVLLDVAASG VNVVEREGVS

VRHPARFVLV GSGNPEEGDL RPQLLDRFGL HARITTITDV SERVEIVKRR

REYDADPFAF VEKWAKETQK LQRKIKQAQR RLPEVILPDP VLYKIAELCV

KLEVDGHRGE LTLARA.ATA LAALEGRNEV TVQDVRRIAV LALRHRLRKD

PLETQD.... ...DAVRIER AVEEVLVP.. .......... ..........

.......... .......... ........


Converting MSF Format

Converting MSF format

The MSF format is an interleaved format that is designed to simplify the comparison of sequences with similar lengths.

G006uaah  MSF: 240  Type: N  Wed Sep 20 12:57:06 MDT 2000  Check: 0 ..

 Name: G019uabh         Len:   400  Check:    0  Weight:  1.00

 Name: G028uaah         Len:   268  Check:    0  Weight:  1.00

 Name: G022uabh         Len:   257  Check:    0  Weight:  1.00

 Name: G023uabh         Len:   347  Check:    0  Weight:  1.00

 Name: G006uaah         Len:   240  Check:    0  Weight:  1.00

//

       G019uabh ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA

       G028uaah CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT

       G022uabh TATTTTAGAG ACCCAAGTTT TTGACCTTTT CCATGTTTAC ATCAATCCTG

       G023uabh AATAAATACC AAAAAAATAG TATATCTACA TAGAATTTCA CATAAAATAA

       G006uaah ACATAAAATA AACTGTTTTC TATGTGAAAA TTAACCTANN ATATGCTTTG

       G019uabh GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC

       G028uaah TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT

       G022uabh TAGGTGATTG GGCAGCCATT TAAGTATTAT TATAGACATT TTCACTATCC

       G023uabh ACTGTTTTCT ATGTGAAAAT TAACCTAAAA ATATGCTTTG CTTATGTTTA

       G006uaah CTTATGTTTA AGATGTCATG CTTTTTATCA GTTGAGGAGT TCAGCTTAAT

       G019uabh TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG

       G028uaah TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC

       G022uabh CATTAAAACC CTTTATGCCC ATACATCATA ACACTACTTC CTACCCATAA

       G023uabh AGATGTCATG CTTTTTATCA GTTGAGGAGT TCAGCTTAAT AATCCTCTAC

       G006uaah AATCCTCTAA GATCTTAAAC AAATAGGAAA AAAACTAAAA GTAGAAAATG

       G019uabh GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG GCCAGCCTTT

       G028uaah TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA GAGTCAAAGC

       G022uabh GCTCCTTTTA ACTTGTTAAA GTCTTGCTTG AATTAAAGAC TTGTTTAAAC

       G023uabh GATCTTAAAC AAATAGGAAA AAAACTAAAA GTAGAAAATG GAAATAAAAT

       G006uaah GAAATAAAAT GTCAAAGCAT TTCTACCACT CAGAATTGAT CTTATAACAT

       G019uabh AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT GGTATGATTT

       G028uaah ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT ATAGCCTCCT

       G022uabh ACAAAATTTA GACTTTTACT CAACAAAAGT GATTGATTGA TTGATTGATT

       G023uabh GTCAAAGCAT TTCTACCACT CAGAATTGAT CTTATAACAT GAAATGCTTT

       G006uaah GAAATGCTTT TTAAAAGAAA ATATTAAAGT TAAACTCCCC

       G019uabh ATCTTTTTGG TCTTCTATAG CCTCCTTCCC CATCCCCATC AGTCTTAATC

       G028uaah TCCCCATCCC ATCAGTCT

       G022uabh GATTGAT

       G023uabh TTAAAAGAAA ATATTAAAGT TAAACTCCCC TATTTTGCTC GTTTTTGCTT

       G019uabh AGTCTTGTTA CGTTATGACT AATCTTTGGG GATTGTGCAG AATGTTATTT

       G023uabh ATCTAAAATA CATTCTGCAC AATCCCCAAA GATTGATCAT ACGTTAC

       G019uabh TAGATAAGCA AAACGAGCAA AATGGGGAGT TACTTATATT TCTTTAAAGC

Using the string following each "Name:" flag at the top of the file, the Mega format converter "unravels" the interleaved data from this format by extracting each line beginning with the first name, then those beginning with the second name, and so on, and ultimately produces a corresponding file that looks like this:

#mega

Title: thisfile.msf

#G019uabh

ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA

GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC

TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG

GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG GCCAGCCTTT

AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT GGTATGATTT

ATCTTTTTGG TCTTCTATAG CCTCCTTCCC CATCCCCATC AGTCTTAATC

AGTCTTGTTA CGTTATGACT AATCTTTGGG GATTGTGCAG AATGTTATTT

TAGATAAGCA AAACGAGCAA AATGGGGAGT TACTTATATT TCTTTAAAGC

#G028uaah

CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT

TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT

TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC

TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA GAGTCAAAGC

ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT ATAGCCTCCT

TCCCCATCCC ATCAGTCT

#G022uabh

TATTTTAGAG ACCCAAGTTT TTGACCTTTT CCATGTTTAC ATCAATCCTG

TAGGTGATTG GGCAGCCATT TAAGTATTAT TATAGACATT TTCACTATCC

CATTAAAACC CTTTATGCCC ATACATCATA ACACTACTTC CTACCCATAA

GCTCCTTTTA ACTTGTTAAA GTCTTGCTTG AATTAAAGAC TTGTTTAAAC

ACAAAATTTA GACTTTTACT CAACAAAAGT GATTGATTGA TTGATTGATT

GATTGAT

#G023uabh

AATAAATACC AAAAAAATAG TATATCTACA TAGAATTTCA CATAAAATAA

ACTGTTTTCT ATGTGAAAAT TAACCTAAAA ATATGCTTTG CTTATGTTTA

AGATGTCATG CTTTTTATCA GTTGAGGAGT TCAGCTTAAT AATCCTCTAC

GATCTTAAAC AAATAGGAAA AAAACTAAAA GTAGAAAATG GAAATAAAAT

GTCAAAGCAT TTCTACCACT CAGAATTGAT CTTATAACAT GAAATGCTTT

TTAAAAGAAA ATATTAAAGT TAAACTCCCC TATTTTGCTC GTTTTTGCTT

ATCTAAAATA CATTCTGCAC AATCCCCAAA GATTGATCAT ACGTTAC

#G006uaah

ACATAAAATA AACTGTTTTC TATGTGAAAA TTAACCTANN ATATGCTTTG

CTTATGTTTA AGATGTCATG CTTTTTATCA GTTGAGGAGT TCAGCTTAAT

AATCCTCTAA GATCTTAAAC AAATAGGAAA AAAACTAAAA GTAGAAAATG

GAAATAAAAT GTCAAAGCAT TTCTACCACT CAGAATTGAT CTTATAACAT

GAAATGCTTT TTAAAAGAAA ATATTAAAGT TAAACTCCCC


Converting Nexus Format

Format: nexus

The NEXUS file format consists of a header with lines that identify the name of each of the sequences contained in the file, followed by lines of data that begin with the sequence name and some data for that sequence.  An example of part of an input file would be:

#NEXUS

BEGIN DATA;

 DIMENSIONS NTAX=17 NCHAR=428;

 FORMAT DATATYPE=PROTEIN INTERLEAVE MISSING=-;

[Name: Chloroflex       Len:   428  Check:    0]

[Name: Rcapsulatu       Len:   428  Check:    0]

MATRIX

          Chloroflex MSKEHVQTIATDDVSKNGHT PPTNASTPPYPFVAIVGQAE

          Rcapsulatu ---------MTTAVARLQPS ASGAKTRPVFPFSAIVGQED

          Chloroflex DQCRALEQQSGKTKKPAVIN IPVPVVDLPLGATEDRVCGT

          Rcapsulatu DWATVLS-----TN---VIR KPTPVVDLPLGVSEDRVVGA

The MEGA2 conversion function looks for the lines that start with the "[Name:" flag.  The word following this is taken as a sequence name.  After all these names have been located, it scans through the data looking for all lines that start with each of the identified names and places them on the output.  This would appear as follows:

#mega

Title: infile.nexus

#Chloroflex

MSKEHVQTIATDDVSKNGHT PPTNASTPPYPFVAIVGQAE

DQCRALEQQSGKTKKPAVIN IPVPVVDLPLGATEDRVCGT

#Rcapsulatu

---------MTTAVARLQPS ASGAKTRPVFPFSAIVGQED

DWATVLS-----TN---VIR KPTPVVDLPLGVSEDRVVGA


Converting FASTA format

Converting Fasta format

The FASTA file format is very simple and is quite similar to the Mega file format.  For example, here is a sample input file:

>G019uabh 400 bp

ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTG

AATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTG

ATTGATTGATTGATTGATGGTTTACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGC

AGTATAACTGGCCAGCCTTTAATACATTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTT

GGTATGATTTATCTTTTTGGTCTTCTATAGCCTCCTTCCCCATCCCCATCAGTCTTAATC

AGTCTTGTTACGTTATGACTAATCTTTGGGGATTGTGCAGAATGTTATTTTAGATAAGCA

AAACGAGCAAAATGGGGAGTTACTTATATTTCTTTAAAGC

>G028uaah 268 bp

CATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATTAAAGACTTGTTTAAACACAAA

ATTTAGACTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGATTGATGGTTTACA

GTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACA

TTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTTGGTATGATTTATCTTTTTGGTCTTCT

ATAGCCTCCTTCCCCATCCCATCAGTCT

The Mega file converter looks for lines that begin with a greater-than sign (‘>’), replaces it with a pound sign (‘#’), takes the word following it as the sequence name, deletes the rest of the line, and takes the lines that follow (up to the next line beginning with a ‘>’) as the sequence’s data.  The above would convert as follows:

#mega

Title: infile.fasta

#G019uabh

ATACATCATAACACTACTTCCTACCCATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTG

AATTAAAGACTTGTTTAAACACAAAAATTTAGAGTTTTACTCAACAAAAGTGATTGATTG

ATTGATTGATTGATTGATGGTTTACAGTAGGACTTCATTCTAGTCATTATAGCTGCTGGC

AGTATAACTGGCCAGCCTTTAATACATTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTT

GGTATGATTTATCTTTTTGGTCTTCTATAGCCTCCTTCCCCATCCCCATCAGTCTTAATC

AGTCTTGTTACGTTATGACTAATCTTTGGGGATTGTGCAGAATGTTATTTTAGATAAGCA

AAACGAGCAAAATGGGGAGTTACTTATATTTCTTTAAAGC

#G028uaah

CATAAGCTCCTTTTAACTTGTTAAAGTCTTGCTTGAATTAAAGACTTGTTTAAACACAAA

ATTTAGACTTTTACTCAACAAAAGTGATTGATTGATTGATTGATTGATTGATGGTTTACA

GTAGGACTTCATTCTAGTCATTATAGCTGCTGGCAGTATAACTGGCCAGCCTTTAATACA

TTGCTGCTTAGAGTCAAAGCATGTACTTAGAGTTGGTATGATTTATCTTTTTGGTCTTCT

ATAGCCTCCTTCCCCATCCCATCAGTCT


Converting PHYLIP (interleaved) Format

Converting the PHYLIP interleaved file format

The PHYLIP format is an interleaved format similar to the MSF format.  It consists of a line of numeric data that is ignored by MEGA2, followed by a group of one or more lines of text that begin with a sequence name in column one followed by the initial part of each sequence; this initial group is terminated by a blank line.  Subsequent groups of data contain a similar number of lines as the first group, as each line a continuation of the sequence identified and begun in the same position in the first group of data.  For instance, the following might be observed at the beginning of a PHYLIP data file:

2 2000 I

G019uabh     ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA

G028uaah     CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT

             GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC

             TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT

             TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG

             TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC

MEGA2 would convert this data as follows:

#mega

Title: cap-data.phylip

#G019uabh

ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA

GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC

TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG

#G028uaah

CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT

TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT

TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC


Converting PHYLIP (Noninterleaved) Format

Converting PHYLIP non-interleaved format

This format is similar to the PHYLIP interleaved format, except that it isn’t interleaved.

0 0 I

G019uabh     ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA

             GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC

             TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG

             GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG GCCAGCCTTT

             AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT

G028uaah     CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT

             TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT

             TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC

             TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA GAGTCAAAGC

             ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT

This file would be converted to Mega format as follows:

#mega

Title: infile.phylip2

#G019uabh

ATACATCATA ACACTACTTC CTACCCATAA GCTCCTTTTA ACTTGTTAAA

GTCTTGCTTG AATTAAAGAC TTGTTTAAAC ACAAAAATTT AGAGTTTTAC

TCAACAAAAG TGATTGATTG ATTGATTGAT TGATTGATGG TTTACAGTAG

GACTTCATTC TAGTCATTAT AGCTGCTGGC AGTATAACTG GCCAGCCTTT

AATACATTGC TGCTTAGAGT CAAAGCATGT ACTTAGAGTT

#G028uaah

CATAAGCTCC TTTTAACTTG TTAAAGTCTT GCTTGAATTA AAGACTTGTT

TAAACACAAA ATTTAGACTT TTACTCAACA AAAGTGATTG ATTGATTGAT

TGATTGATTG ATGGTTTACA GTAGGACTTC ATTCTAGTCA TTATAGCTGC

TGGCAGTATA ACTGGCCAGC CTTTAATACA TTGCTGCTTA GAGTCAAAGC

ATGTACTTAG AGTTGGTATG ATTTATCTTT TTGGTCTTCT


Converting CLUSTAL Format

Converting CLUSTAL Format

The sequence alignment outputs from CLUSTAL software are often given the default extension .ALN.    It is an interleaved format that shows the sequence name in column one of each line and a part of the sequence’s data right-justified in a page-wide arrangement.  For example, see the following excerpt.

CLUSTAL X (1.8) multiple sequence alignment

Q9Y2J0_Hsa                     ------------MTDTVFSNSSNRWMYPSDRPLQSNDKEQLQAGWSVHPG

Q06846_RP3A_BOVIN              ------------MTDTVFSSSSSRWMCPSDRPLQSNDKEQLQTGWSVHPS

JX0338_rabphilin-3A-mouse      ------------MTDTVVN----RWMYPGDGPLQSNDKEQLQAGWSVHPG

Q9Y2J0_Hsa                     GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM

Q06846_RP3A_BOVIN              GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM

JX0338_rabphilin-3A-mouse      AQTDRQRKQEELTDEEKEIINRVIARAEKMEAMEQER--IGRLVDRLETM

A file written in this way would be converted by MEGA2 into the following format:

#mega

Title: Bigrab2.aln

#Q9Y2J0_Hsa

------------MTDTVFSNSSNRWMYPSDRPLQSNDKEQLQAGWSVHPG

GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM

RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVET-NNRLH

#Q06846_RP3A_BOVIN

------------MTDTVFSSSSSRWMCPSDRPLQSNDKEQLQTGWSVHPS

GQPDRQRKQEELTDEEKEIINRVIARAEKMEEMEQER--IGRLVDRLENM

RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVETSNNRPH

#JX0338_rabphilin-3A-mouse

------------MTDTVVN----RWMYPGDGPLQSNDKEQLQAGWSVHPG

AQTDRQRKQEELTDEEKEIINRVIARAEKMEAMEQER--IGRLVDRLETM

RKNVAGDGVNRCILCGEQLGMLGSACVVCEDCKKNVCTKCGVETSNNRPH


Basic Sequence Statistics

In the study of molecular evolution it is often necessary to know some basic statistical quantities such as nucleotide frequencies, codon frequencies, and transition/transversion ratios. The statistical quantities that can be computed by MEGA are discussed in this section.


Nucleotide and Amino Acid Compositions

The relative frequencies of the four nucleotides (nucleotide composition) or of the twenty amino acid residues (amino acid composition) can be computed for a specific sequence or for all the sequences used. For coding regions of DNA, additional columns are presented for the nucleotide compositions at first, second, and third codon positions.  All results are presented domain-by-domain, if multiple domains exist in the dataset.  Results for the amino acid composition are also presented in a similar tabular form.


Nucleotide Pair Frequencies

When two nucleotide sequences are compared, the frequencies of 10 or 16 different types of nucleotide pairs can be computed. In MEGA these frequencies are tabulated in the following form.


Codon Usage

There are 64 (43) possible codons that code for 20 amino acids (and stop signals), so an amino acid may be encoded by several codons (e.g., serine is encoded by six codons in nuclear genes). It is therefore interesting to know the codon usage for each amino acid. In MEGA the numbers of the 64 codons used in a gene can be computed either for a specific sequence or for all sequences examined.  In addition to the codon frequencies, MEGA also writes the Sharp et al.'s relative synonymous codon usage (RSCU) statistic (see Nei and Kumar 2000, page 11).


Alignment Gaps and Sites with Missing Information

Gaps are often inserted during the alignment of homologous regions of sequences and represent deletions or insertions (indels). These gaps introduce some complications in distance estimation. Furthermore, sites with missing information can sometimes occur because of experimental difficulties, and they create the same problems as that for gaps. In the following discussion both of these sites are treated in the same way.

In MEGA, gap sites are ignored in distance estimation, but there are two different ways to treat these sites. One way to deal with this problem is to delete all of these sites from data analysis. This option, which is called the Complete-Deletion option in MEGA, is generally desirable because different regions of DNA or amino acid sequences often evolve under different evolutionary forces. However, if the number of nucleotides involved in a gap is small and gaps are distributed more or less at random, one may compute a distance for each pair of sequences ignoring only those gaps that are involved in the comparison. This option is called the Pairwise-Deletion option. The following table illustrates the effect of these options on distance estimation with the following three sequences:

         1       10        20

seq1     A-AC-GGAT-AGGA-ATAAA

seq2     AT-CC?GATAA?GAAAAC-A

seq3     ATTCC-GA?TACGATA-AGA    	Total sites = 20.

Here, the alignment gaps are indicated with a hyphen (-) and the missing information sites are denoted by a question mark (?).

Complete-Deletion and Pairwise-Deletion options

		Differences/Comparisons
Option	Sequence Data	(1,2)	(1,3)	(2,3)
Complete deletion	1.  A  C  GA  A GA A A A 2.  A  C  GA  A GA A C A 3.  A  C  GA  A GA A A A	1/10	0/10	1/10
Pairwise Deletion	1.  A-AC-GGAT-AGGA-ATAAA 2.  AT-CC?GATAA?GAAAAC-A 3.  ATTCC-GA?TACGATA-AGA	2/12	3/13	3/14
In the above table, the number of sites compared varies with pairwise comparison in the Pairwise-Deletion option, but it remains the same for all pairwise comparisons in the Complete-Deletion option. In this particular data set, more information can be obtained by using the Pairwise-Deletion option. In practice, however, different regions of nucleotide or amino acid sequences often evolve differently. In this case the Complete-Deletion option is preferable.


Phylogenetic Inference

Reconstruction of the evolutionary history of genes and species is one of the most important subjects in the current study of molecular evolution. If reliable phylogenies are produced, they will shed light on the sequence of evolutionary events that generated present day diversity of genes and species and help us to understand the mechanisms of evolution as well as the history of organisms.

Phylogenetic relationships of genes or organisms are usually presented in a treelike form with a root. This type of tree is called a rooted tree. It is also possible to draw a tree without a root, which is called an unrooted tree. The branching pattern of a tree is called a topology.

There are numerous methods for constructing phylogenetic trees from molecular data (Nei and Kumar 2000). They can be classified into Distance methods, Parsimony methods, and Likelihood methods.  These methods are explained in Swofford et al. 1996, Li (1997), Page and Holmes (1998), and Nei and Kumar (2000).


UPGMA

This method assumes that the rate of nucleotide or amino acid substitution is the same for all evolutionary lineages. An interesting aspect of this method is that it produces a tree that mimics a species tree, the branch lengths for two OTUs being the same after their separation.  Because of the assumption of a constant rate of evolution, this method produces a rooted tree, though it is possible to remove the root for certain purposes. Algorithm for UPGMA is discussed in detail in Nei and Kumar (2000, page 87).


Neighbor-Joining (NJ) Method

This method (Saitou and Nei 1987) is a simplified version of the minimum evolution (ME) method (Rzhetsky and Nei 1992). In the ME method, distance measures that correct for multiple hits at the same sites are used, and a topology showing the smallest value of the sum of all branches (S) is chosen as an estimate of the correct tree.  However, construction of a minimum evolution tree is time-consuming, because in principle the S values for all topologies have to be evaluated. The number of possible topologies (unrooted trees) rapidly increases with the number of taxa. Therefore, it becomes very difficult to examine all topologies.

In the case of the NJ method, the S value is not computed for all or many topologies, but the examination of different topologies is embedded in the algorithm, so that only one final tree is produced. The algorithm of the NJ method is somewhat complicated and is explained in detail in Nei and Kumar (2000, page 103).

NJ method produces an unrooted tree, because it does not require the assumption of a constant rate of evolution.   Therefore, it requires an outgroup taxon to find the root.  In the absence of outgroup taxa, the root is sometimes given at the midpoint of the longest route connecting two taxa in the tree. This is referred to as the mid-point rooting.


Minimum Evolution

In the ME method, distance measures that correct for multiple hits at the same sites are used, and a topology showing the smallest value of the sum of all branches (S) is chosen as an estimate of the correct tree.  However, construction of a minimum evolution tree is time-consuming, because in principle the S values for all topologies have to be evaluated. The number of possible topologies (unrooted trees) rapidly increases with the number of taxa. Therefore, it becomes very difficult to examine all topologies.  In this case, one may use the neighbor-joining method.  While the NJ tree is usually the same as the ME tree when the number of taxa is small; the difference between the NJ and ME trees can be substantial (reviewed in Nei and Kumar 2000).  In this case, the ME tree is preferable if a long DNA or amino acid sequence is used.  When the number of number of nucleotides or amino acids used is relatively small, the NJ method generates the correct toplogy more often than the ME method (Nei et al. 1998, Takahashi and Nei 2000). In MEGA, we have provided the close-neighbor-interchange search to examine the neighborhood of the NJ tree to find the potential ME tree.


Maximum Parsimony (MP) Method

Maximum parsimony (MP) methods were originally developed for morphological characters, and there are many different versions (see Nei and Kumar [2000] for a review).  In MEGA, we consider both the methods for nucleotide and amino acid sequence data (Eck and Dayhoff 1966; Fitch 1971).

For constructing an MP tree, only sites at which there are at least two different kinds of nucleotides or amino acids, each represented at least twice, are used (parsimony-informative sites). Other variable sites are not used for constructing an MP tree, though they are informative for distance and maximum-likelihood methods.

MEGA implements estimation of the MP tree branch lengths by using the average pathway method for unrooted trees  (see Nei and Kumar [2000], page 132).

To search for the MP Trees, MEGA provides three different types of searches: the max-mini branch-and-bound search,  min-mini heuristic search, and close-neighbor-interchange heuristic search.  Only the branch-and-bound search guarantees to find all the MP trees, but it often takes prohibitive amount of time if the number of sequences is large (>15).  For details, please see chapter 7 in Nei and Kumar (2000)


Alignment Gaps and Sites with Missing Information

In MEGA, gap sites are ignored in the MP analysis, but there are two different ways to treat these sites. One way to deal with this problem is to delete all of these sites from data analysis. This option, which is called the Complete-Deletion option in MEGA, is generally desirable because different regions of DNA or amino acid sequences often evolve under different evolutionary forces. However, if the number of nucleotides (or amino acids) involved in a gap is small and gaps are distributed more or less at random, one may include all such sites in the data and treat them as missing data. Therefore, gap and missing data are never used in computing tree lengths in MEGA2.


Condensed Trees

When a phylogenetic tree has low statistical support(PC or PB) values for several interior branches, it is often useful to produce a multifurcating tree by assuming that all such interior branches have a branch length equal to 0. We call this multifurcating tree a condensed tree. In MEGA, this condensed tree can be produced for any level of PC or PB value. For example, if there are several branches with PC or PB values of less than 50% , a condensed tree with the 50% PC or PB level will have a multifurcating tree with all these branch lengths reduced to 0.

Since the branches of low significance are eliminated to form a condensed tree, this tree gives emphasis on reliable portions of branching patterns. However, this tree has one drawback. That is, since some branches are reduced to 0, it is difficult to draw a tree with proper branch lengths for the remaining portion. We have therefore decided to give our attention only to the topology. Thus, the branch lengths of a condensed tree in MEGA are not proportional to the number of nucleotide or amino acid substitutions.

Note that condensed trees are different from consensus trees mentioned earlier, though they may look similar in practice. A consensus tree is produced from many equally parsimonious trees, whereas a condensed tree is merely a simplified version of a tree. A condensed tree can be produced for any type of tree (NJ, ME, UPGMA, MP, or maximum-likelihood tree).

See also Nei and Kumar (2000) page 175.


General Comments on Statistical Tests

There are two different types of methods for testing the reliability of a tree obtained. One is to test the topological difference between the tree and its closely related tree by using certain quantity such as the sum of all branch lengths in the minimum evolution method.  This type of test is supposed to examine the reliability of every interior branch of the tree, and it is generally a conservative test as compared to other tests included in MEGA.

The other type of test is to examine the reliability of each interior branch whether it is significantly different from 0 or not.  If a particular interior branch is not significantly different from 0, we cannot exclude the possibility of trifurcation of the branches associated or even the other types of bifurcating trees that can be generated by changing the splitting order of the three branches involved.  In MEGA we implement the bootstrap procedure for estimating the standard error of the interior branch and test the deviation of the branch length from 0 (Dopazo 1994).

The other test is the bootstrap test in which the reliability of a given branch pattern is ascertained by examining its frequency of occurrence in large number of trees, each based on the resampled dataset.

Details of these procedures are given in Nei and Kumar (2000, chapter 9).


Finding number of sites in pairwise comparisons

If you want to find the number of sites between pairs of sequences or average number of sites, then go to the Distance menu, select the desired distance type, and then in Substitutions to include select the desired option about the number of sites.


Writing only 4-fold degenerate sites to an output file

All sequence data subset facilities are accessible through the Sequence Data Explorer's Export Data command.  To write 4-fold degenerate sites to a file, simply highlight the 4-fold degenerate sites on the screen and then issue the Export Data command.  In that command, choose to write only the highlighted sites.  In fact, if you wish, you can select to write only the third codon positions (among other options).  In this case, all 4-fold degenerate sites found in the third codon positions will be written to the file.


Computing statistics on only highlighted sites in Data Explorer

Go to Statistics menu in the Sequence Data Explorer, and click on Use highlighted sites only.  Now all statistical quantities computed using the Statistics menu will be based only on the highlighted sites.


Get more information about the codon based Z-test for selection

In MEGA, the codon based Z-test for selection can be done in two places.  First, by using the Tests | Codon Based tests of selection | Z-test (large sample) option.   In this case, the probability that the null hypothesis is rejected is given, in addition to the actual value of the Z-statistic.  If you want to know the difference between s and n (synonymous and nonsynonymous substitutions) and its variance, then you should go to the Distances | Pairwise menu option and in the distance computation dialog, you should select an appropriate method (e.g., Nei-Gojobori method) and then choose s-n (or n-s depending on your need) from the Substitutions to include menu.  Also, choose to compute standard error.


Menus in MEGA are so short, where are all the options?

The philosophy behind the objective-driver user-interface of MEGA is to ask the user required information on a need-to-know basis.  Also, we have made a concerted effort to construct a clutter-free work environment in the MEGA2 interface.  The result is a simple looking modular power-packed analysis tool.  Behind each menu item is a wide range of useful options and tools.  Consider the Sequence Data Explorer.  This unique module is the central point for viewing the data in various ways, exporting data subsets, and computing many important basic statistical quantities.  It is always working behind the scenes, and is hidden away when you don't want it.  Another interesting little module is invoked when you invoke the Genetic Code selector.  In addition to selecting a desired code table, it allows you to add new and edit the existing code table, view the selected code table in a conventional format, compute the degeneracy for each site in every codon, and compute the number of potentially synonymous and nonsynonymous sites for each codon.  Therefore, you get to choose how much depth you want to go into, and you can always find how to do something in MEGA2 by checking up on the help index.  You will find that almost all modules in MEGA2 are intelligent and come with enhancements that are designed to reduce the amount of time needed for mundane non-technical tasks.


Alignment Gaps

Phylogenetic analysis on two or more DNA or amino acid sequences requires that the sequences be aligned properly so that the substitutions can be accurately enumerated.  During the process of alignment, gaps have to be introduced in sequences that have undergone deletions  or insertions.  These gaps are known as alignment gaps.


Bifurcating Tree

A tree in which each ancestral lineages gives rise to exactly two descendent lineages.  A tree with only bifurcating nodes is called a bifurcating tree.


Branch

A branch in a phylogenetic tree is a line connecting either two internal nodesto each other or an external node to an internal node.  The length of a branch, where appropriate, denotes the genetic distance (e.g., number of substitutions per unit time) between the two taxa it connects.


Branch-and-Bound algorithm

Branch-and-bound algorithm is used to find all MP trees.  It guarantees to find all the MP trees without conducting an exhaustive search.  In MEGA, we have implemented the Max-mini branch-and-bound search, which is described in detail in Kumar et al. (1993) and in Nei and Kumar (2000, page 123).


Close-Neighbor-Interchange (CNI)

Examining all possible topologies in any method is a very time consuming process.  This algorithm reduces the searching time by first producing a temporary tree, (e.g., an NJ tree when an ME tree is being sought), and then examining all topologies that are different from this temporary tree by topological distance dT = 2 and 4.  If this is repeated many times, avoiding all the topologies previously examined, one can usually obtain the tree being sought.

For the MP method, the CNI search can start with a tree generated by random addition of sequences.  This process can be repeated multiple times to find the MP tree.

See Nei & Kumar (2000) for details.


Codon

A triplet of nucleotides that codes for a specific amino acid.


Complete-Deletion Option

In the complete-deletion option, sites containing missing data or alignment gaps are removed before the analysis begins.  This is in contrast to the pairwise-deletion option in which sites are removed as the need arises during the analysis (e.g., pairwise distance computation).


Compress/Uncompress

The cursor changes to the 'Compress/Uncompress' icon when this command is selected.  Upon selecting this command if the user clicks on an interior branch, MEGA then prompts the user to give a name to the group that will be formed.  It then compresses all the lineages defined by this branch into a solid elongated triangle, whose thickness is proportional to the number of taxa condensed.   {to explain the height and width of the resulting triangle.}  Clicking on the branch again uncompresses it.

The cursor may be reverted to the arrow by clicking on the arrow icon on the left hand side of the Tree Explorer.


Condensed Tree

When interior branches in a phylogenetic tree do not have statistically significant lengths, then choosing this command condenses the tree by showing tree topology in which each branch with less than the desired statistical significance is collapsed.


Consensus Tree

The MP method often produces many equally parsimonious trees.  Choosing this command then produces a composite tree that is a consensus among all such trees, for example either as a strict consensus (where all conflicting branching patterns among the trees are resolved by making those nodes multifurcating) or a Majority-Rule consensus tree (where conflicting branching patterns are resolved by selecting the pattern seen in more than 50% of the trees).

 (Details are given in Nei and Kumar [2000], page 130).


Constant Site

If a site contains the same nucleotide or amino acid in all sequences, it is referred to as a constant site.  MEGA will identify a site as a constant site only if at least two sequences contain unambiguous nucleotides or amino acids at that site.


Degeneracy

0-fold degenerate sites are those at which all changes are nonsynonymous.

2-fold degenerate sites are those at which one out of three changes is synonymous.  (All sites at which two out of three changes are synonymous are also included in this category.)

4-fold degenerate sites are those at which all changes are synonymous.


Domains

A domain is a continuous block of sites in a sequence alignment.  A domain can be free-standing or assigned to genes.  They can be protein-coding (e.g., exons) or non-coding (e.g., introns). Domains can be defined in the input data, and can be defined and edited in the Setup Genes Domains dialog.


Exon

A protein-coding gene typically consists of multiple coding regions, known as exons, interspersed with non-coding DNA (introns)


Extant Taxa

The taxa whose sequences/other genetic information/morphological characters, etc. are being used in order to do a phylogenetic analysis, are known as the extant taxa, irrespective of whether the individuals/species to whom the sequences/other information belong to, are extant or extinct.


Flip

The cursor changes to the 'Flip' icon when this command is selected. Upon selecting this command if the user clicks on an interior branch, MEGA then reverses the order of the lineages defined by this branch.

The cursor may be reverted to the arrow by clicking on the arrow icon on the left hand side of the Tree Explorer.


Format command

A format command in a data file begins with !Format, and it contains at least the data type included in the file.


Gamma parameter

The substitution rate often varies from site to site within a sequence, according to the Gamma distribution.  The shape of this distribution is determined by the value of a parameter known as the Gamma parameter, also known as the shape parameter.


Gene

A gene is a collection of domains. The domains included in a gene need not be consecutive or of the same type.  Genes and domain can be defined in the input data, and can be defined and edited in the Setup Genes and Domains dialog.  Genes can be selected/unselected from an analysis.  When a gene is unselected, all its domains are automatically unselected. However, a gene can be selected, with some of its domains unselected.


Genetic Codes

A genetic code table specifies the amino acid residues encoded by the various codons.  Vertebrate mitochondria, Drosophila mitochondria, and Yeast mitochondria, all have their own genetic code tables that are slightly different from the most common table, the Standard Genetic code table.


Groups of taxa

A group of taxa is simply a set of taxa.  A valid group consists of one or more taxa.  They can be specified in the input data file, and created and edited in the Setup Taxa and Groups dialog.

Groups of taxa are often constructed based on the evolutionary relatedness.  For example, sequences may be grouped based on the geographic origin of the source individual, or sequences from a multigene family may be arranged into grouped consisting of orthologous sequences.


Indels

Phylogenetic analysis on two or more DNA or amino acid sequences requires that the sequences be aligned properly so that the substitutions can be accurately enumerated.  During the process of alignment, gaps have to be introduced in sequences that have undergone deletions or insertions.  These gaps are known as alignment gaps, or indels.


Independent Sites

In a sequence alignment, all sites that have not been assigned to any gene / domain are classified as independent.


Inferred Tree

The tree reconstructed using any tree making method (such as UPGMA, NJ, ME, or MP) from the observed sequence or other appropriate data is known as the inferred or reconstructed tree.


Intron

The non-coding segments of DNA in a gene that are interspersed among the exons.


Labeled Sites

Sites in a sequence alignment can be categorized and labeled with user-defined symbols. A category is represented by a letter or a number. Each site can only be assigned to one category. Any combination of categories can be selected for analysis.

Labeled sites work independently of and in addition to genes and domains, thus allowing complex subsets of sites to be defined easily.


Max-mini branch-and-bound search

This is an algorithm for searching for the MP tree using the branch-and bound search method.  See Nei & Kumar (2000) for details.


Maximum Parsimony Principle

For a given topology, the sum of the minimum possible substitutions over all sites is known as the tree length of that topology.  The topology with the minimum tree length is known as the Maximum Parsimony tree.


Mid-point rooting

In the mid point rooting method, the root of an unrooted tree is placed at the mid-point of the longest distance between two taxa in a tree.


Min-mini algorithm

This is a heuristic search algorithm to find the MP tree.  It is somewhat similar to the branch-and bound search method.  In this heuristic search algorithm, many trees that are unlikely to have a small local tree length are ignored from computation of their L values, and thus the algorithm speeds up the search for the MP tree as compared to the branch and bound search. However, the final tree or trees obtained by this algorithm may not be the true MP tree(s).  The user can specify a search factor to control the extensiveness of the search.  MEGA adds the user specified search factor to the current local upper bound.  A larger value of search factor will lead to a slower search (as many more trees will be examined).

 (See also Nei & Kumar (2000), pages 122, 125)


Monophyletic

A cluster of taxa in a phylogenetic tree that share a common ancestor comparatively recently in the evolutionary history of the entire tree is known as a monophyletic group of taxa.  The term reflects the close relationship of the taxa with each other.


mRNA

Protein-coding genes are first transcribed into messenger RNAs (mRNA), which are in turn, translated into the amino acid sequences to make proteins.


Node

A node in a phylogenetic tree represents a taxon – all the external or terminal nodes represent the extant taxa and the internal nodes represent the ancestral taxa.


Nonsynonymous change

A nucleotide change in the codon is nonsynonymous if it changes the amino acid encoded by the original codon.  A nucleotide site where one or more changes are nonsynonymous is referred to as a nonsynonymous site.  If only one of three possible nucleotide changes at that site is nonsynonymous then the site is 1/3 nonsynonymous.  If two of three nucleotide changes are nonsynonymous then the site is 2/3 nonsynonymous.  And, if all three possible nucleotide changes are nonsynonymous, then the site is completely nonsynonymous site (3/3 nonsynonymous).


Orthologous Genes

Two genes are said to be orthologous if they are a result of a speciation event.


Outgroup

Outgroup is a sequence (or a set of sequences) that is known to be a sister taxa to all other sequences in the dataset.


Pairwise-deletion option

In the pairwise-deletion option, sites containing missing data or alignment gaps are removed from the analysis as the need arises during the analysis (e.g., pairwise distance computation). This is in contrast to the complete-deletion option in which all such sites are removed prior to the analysis.


Parsimony-informative site

A site is parsimony-informative if it contains at least two types of nucleotides (or amino acids), with at least two of them occurring with a minimum frequency of 2.


Polypeptide

A polypeptide is a chain of many amino acids.


Positive selection

At the DNA sequence level, it refers to the selection in favor of nonsynonymous substitutions.  In this case, the evolutionary distance based on the nonsynonymous substitutions is expected to be greater than the synonymous substitutions.


Protein parsimony

Maximum Parsimony analysis with protein sequences is known as Protein parsimony.


Purifying selection

At the DNA sequence level, it refers to the selection against the nonsynonymous substitutions.  In this case, the evolutionary distance based on the synonymous substitutions is expected to be greater than the nonsynonymous substitutions.


Purines

The nucleotides Adenine (A) and Guanine (G) are known as purines.


Pyrimidines

The nucleotides Cytosine (C) and Thymine (T) are known as pyrimidines.


Random addition trees

This refers to the generation of random initial trees for heuristic search to find MP trees.  In this case, a tree is generated by randomly selecting a sequence and adding it to the growing tree on a randomly selected branch.


Rooted Tree

A rooted tree is the one in which the root of the phylogenetic tree is determined by using the mid-point rooting) or outgroups sequences.


RSCU

Many amino acids are coded by more than one codon, thus making the multiple codons for a given amino acid synonymous.  However, many genes display a non-random usage of synonymous codons for specific amino acids.  A measure of the extent of this non-randomness is given by the Relative Synonymous Codon Usage (RSCU) (Sharp et al. 1986).

The RSCU for a particular codon (i) is given by

RSCUi  =  Xi / ∑∑ XI /n

where Xi is the number of times the ith codon has been used for a given amino acid, and n is the number of synonymous codons for that amino acid.


Singleton Sites

A site is called a singleton site if it contains at least two types of nucleotides (or amino acids) with at most one of them occurring multiple times.  MEGA will identify a site as a singleton site only if at least three sequences contain unambiguous nucleotides or amino acids at that site.


Site Label

The individual sites in nucleotide or amino acid data can be labeled to construct non-contiguous sets of sites.  The Setup Genes and Domains dialog can be used to assign/edit site labels, in addition to specifying them in the input data files as shown in the following example of three-sequences in which the sites in the ThirdGene are labeled with a ‘+’ mark.  An underscore is used as a symbol to mark absence of any labels.

!Gene=FirstGene  Domain=Exon1  Property=Coding;

#Human_{Mammal}  ATGGTTTCTAGTCAGGTCACCATGATAGGTCTCAAT

#Mouse_{Mammal}  ATGGTTTCTAGTCAGGTCACCATGATAGGTCCCAAT

#Chicken_{Aves}  ATGGTTTCTAGTCAGCTCACCATGATAGGTCTCAAT

!Gene=SecondGene  Domain=AnIntron Property=Noncoding;

#Human     ATTCCCAGGGAATTCCCGGGGGGTTTAAGGCCCCTTTAAAGAAAGAT

#Mouse     GTAGCGCGCGTCGTCAGAGCTCCCAAGGGTAGCAGTCACAGAAAGAT

#Chicken   GTAAAAAAAAAAGTCAGAGCTCCCCCCAATATATATCACAGAAAGAT

!Gene=ThirdGene  Domain=Exon2  Property=Coding;

#Human      ATCTGCTCTCGAGTACTGATACAAATGACTTCTGCGTACAACTGA

#Mouse      ATCTGATCTCGTGTGCTGGTACGAATGATTTCTGCGTTCAACTGA

#Chicken    ATCTGCTCTCGAGTACTGCTACCAATGACTTCTGCGTACAACTGA

!Label      +++__-+++-a-+++-L-+++-k-+++123+++-_-+++---+++;

Each site can be associated with only one label.  A label can be a letter or a number.

For analyses that require codons, MEGA includes only those codons in which all three positions are given the same label.  This site labeling system is provided to facilitate analysis of specific sites of the data, as is often required for comparing sequences of regulatory elements, intron-splice sites, and antigen recognition sites in the genes of Major Histocompatibility Complex, among other applications.


Statements in input files

All statements in MEGA files start with an exclamation mark (!) and end with a semicolon (;).  They are useful in specifying various attributes of the data and the data file.  There are three common statements for all types of data, they are: Title, Format, and Description.  There are a number of other statements that can be used in MEGA files depending on the type of data being analyzed.


Swap

The cursor changes to the 'Flip' icon when this command is selected.  Upon selecting this command if the user clicks on an interior branch, MEGA then swaps the two subtrees defined by this branch.  If the subtrees are each simply an individual taxon, then Swap is the same as Flip.

The cursor may be reverted to the arrow by clicking on the arrow icon on the left hand side of the Tree Explorer.


Synonymous change

A nucleotide change in the codon is synonymous if it does not change the amino acid encoded by the original codon.  A nucleotide site where one or more changes are synonymous is referred to as a synonymous site.  If only one of three possible nucleotide changes at that site is synonymous, then the site is 1/3 synonymous.  If two of three nucleotide changes are synonymous, then the site is 2/3 synonymous and 1/3 nonsynonymous.  And, if all three possible nucleotide changes are synonymous, then the site is completely synonymous site (3/3 synonymous).


Taxa

A taxon is the individual unit whose evolutionary relationship is being investigated.  Depending on the study, 'Taxa' may refer to species, populations, individuals, or sequences within an individual.


Topological distance

This distance quantifies the extent of topological differences between two given trees.  For unrooted bifurcating  trees, this distance is twice the number of interior branches at which the taxa are partitioned differently for the two trees being compared.


Topology

The branching pattern of a tree is known as the topology of the tree.


Transition

When a purine is substituted by a purine, or a pyrimidine by a pyrimidine, such a substitution is known as a transition.


Transition Matrix

A transition matrix is a matrix that specifies that probability of every possible substitution among the nucleotides/amino acids.


Transition/Transversion Ratio (R)

This is simply the ratio of the number of transitions to the number of transversions for a pair of sequences.  R becomes 0.5 when there is no bias towards either transitional or transversional substitution, because, when the two kinds of substitution are equally probable, there are twice as many possible transversions as transitions.   MEGA allows you to conduct analysis of your data with a specified value of R.

Note that R should not be confused with the ratio of the transition and transversion rates (k = α/β).


Translation

Translation is the term given to the process whereby each codon in the mRNA is translated into a particular amino acid, according to the genetic code specific to the species/DNA, and adds to the growing polypeptide chain.


Transversion

Change from a purine to a pyrimidine, or vice versa, is a transversional change.


Transversion Parsimony

A Weighted Maximum Parsimony method with a substitution weight matrix where all transitional changes are given a weight of 0 (i.e., they are ignored) and only transversional changes are considered (weight = 1) is called Transversional Parsimony.


Tree length

Tree length is the criterion used in searching for the best tree by the Maximum Parsimony method.  This is given by the sum of the minimum numbers of substitutions over all sites for the given topology.

To compute the tree length for the unweighted parsimony method, we use the procedure described in Fitch (1971), which is based on two simple rules described below.  This rule is applied for each node for a given site and the sum of substitutions over all nodes and over all sites is taken.  Note that the estimation of minimum number of substitutions is not affected by the position of the root.

Rule 1.  When the two descendent nodes of an ancestral node have some states (nucleotides or amino acids) in common, the ancestral node is assigned the set of common states.  In this case, the most parsimonious explanation does not require any substitutions.

Rule 2.  When the two descendant nodes have no states in common, then all states in the descendent nodes are combined to form the set of possible states at the ancestral node.  In this case, one substitution is required.


Unrooted tree

An unrooted tree is one in which no assumption is made regarding the ancestor of all the taxa in the tree.


Variable site

A variable site contains at least two types of nucleotides or amino acids.  Some variable sites can be singleton or parsimony-informative.  (If a site is not variable, then it is referred to as a constant site).


Weighted Parsimony

Weighted parsimony is not yet available.  This topic will be completed in a future release of MEGA2.


OLS branch length estimates

The ordinary least squares estimate of a branch length (b) is given by

where dij is the pairwise distance between sequences i and j.  The coefficients wij’s depend on whether the branch under consideration is internal or external.

Coefficients wij’s for an internal  branch

where, mA, mB, mC, and mC are the numbers of sequences in clusters A, B, C, and D, respectively.

Coefficients wij’s for an external branch

 where, mA and mB are the numbers of sequences in clusters A and B.


Saitou and Nei 1987

Saitou, N. and M. Nei. 1987. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4:406-425.


Jukes and Cantor 1969

Jukes, T. H. and C. R. Cantor. 1969. Evolution of protein molecules. In H. N. Munro, ed., Mammalian Protein Metabolism, pp. 21-132, Academic Press, New York.


Tajima 1993

Tajima, F. 1993. Simple methods for testing molecular clock hypothesis. Genetics 135:599-607.


Tajima and Nei 1984

Tajima, F. and M. Nei. 1984. Estimation of evolutionary distance between nucleotide sequences. Molecular Biology and Evolution 1:269-285.


Kimura 1980

Kimura, M. 1980. A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16:111-120.


Tamura 1992

Tamura, K. 1992. Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Molecular Biology and Evolution 9:678-687.


Tamura and Nei 1993

Tamura, K. and M. Nei. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution 10:512-526.


Li et al. 1985

Li, W.-H., C.-1. Wu, and C.-C. Luo. 1985. A new method for estimating synonymous and nonsynonymous rates of nucleotide substitution considering the relative likelihood of nucleotide and codon changes. Molecular Biology and Evolution 2:150-174.


Nei and Gojobori 1986

Nei, M. and T. Gojobori. 1986. Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Molecular Biology and Evolution 3:418-426.


Pamilo and Bianchi 1993

Pamilo, P. and N. O. Bianchi. 1993. Evolution of the Zfx and Zfy, genes: Rates and interdependence between the genes. Molecular Biology and Evolution 10:271-281.


Tanaka and Nei 1989

Tanaka, T. and M. Nei. 1989. Positive Darwinian selection observed at the variable-region genes of immnoglobulins. Molecular Biology and Evolution 6:447-459.


Nei and Jin 1989

Nei, M. and L. Jin. 1989. Variances of the average numbers of nucleotide substitutions within and between populations . Molecular Biology and Evolution 6:290-300.


Nei et al. 1976

Nei, M., R. Chakraborty, and P. A. Fuerst. 1976. Infinite allele model with varying mutation rate. Proceedings of National Academy of Sciences, USA 73:4164-4168.


Dayhoff 1978

Dayhoff, M. O. 1978. Survey of new data and computer methods of analysis. In M. O. Dayhoff, ed., Atlas of Protein Sequence and Structure, vol. 5, supp. 3, pp. 29, National Biomedical Research Foundation, Silver Springs, Maryland.


Goldman 1993

Goldman, N. 1993. Statistical tests of models of DNA substitution. Journal of Molecular Evolution 36:182-198.


Tamura 1994

Tamura, K. 1994. Model selection in the estimation of the number of nucleotide substitutions. Molecular Biology and Evolution 11:154-157.


Nei 1991

Nei, M. 1991. Relative efficiencies of different tree making methods for molecular data. In M. M . Miyamoto and J . L. Cracraft, eds., Recent Advances in Phylogenetic Studies of DNA Sequences, pp. 90-128. Oxford University Press, Oxford.


Sharp et al. 1986

Sharp, P. M., T. M. F. Tuohy, and K. R. Mosurski. 1986. Codon usage in yeast: Cluster analysis clearly differentiates highly and lowly expressed genes. Nucleic Acids Research 14:5125-5143.


Tajima 1983

Tajima, F. 1983. Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437-460.


Nei 1986

Nei, M. 1986. Stochastic errors in DNA evolution and molecular phylogeny. In H. Gershowitz, D. L. Rucknagel, and R. E. Tashian, eds., Evolutionary Perspectives and	the New Genetics. pp. 133-147. Alan R. Liss, New York.


Pamilo and Nei 1988

Pamilo, P. and M. Nei. 1988. Relationships between gene trees and species trees. Molecular Biology and Evolution 5:568-583.


Felsenstein 1988

Felsenstein, J. 1988. Phylogenies from molecular sequences: Inference and reliability. Annual Review of Genetics 22:521-565.


Felsenstein 1986

Felsenstein, J. 1986. Distance Methods: Reply to Farris. Cladistics 2:130-143.


Sneath and Sokal 1973

Sneath, P. H. A. and R. R. Sokal. 1973. Numerical Taxonomy. Freeman, San Francisco.


Fitch and Margoliash 1967

Fitch, W. M. and E. Margoliash. 1967. Construction of phylogenetic trees. Science 155:279 284.


Felsenstein 1993

Felsenstein, J. 1993. Phylogeny Inference Package (PHYLIP). Version 3.5. University of Washington, Seattle.


Estabrook et al. 1975

Estabrook, G. F., C. S. Johnson, and F. R. McMorris. 1975. An idealized concept of the true cladistic character. Mathematical Biosciences 23:263-272.


Lake 1987

Lake, J. A. 1987. A rate-independent technique for analysis of nucleic acid sequences: Evolutionary parsimony. Molecular Biology and Evolution 4:167-191.


Swofford 1993

Swofford, D. L. 1993. Phylogenetic Analysis Using Parsimony (PAUP), Version 3.1.1. University of Illinois, Champaign.


Tateno et al. 1982

Tateno, Y., M. Nei, and F. Tajima. 1982. Accuracy of estimated phylogenetic trees from molecular data. I. Distantly related species. Journal of Molecular Evolution 18:387-404.


Sourdis and Krimbas 1987

Sourdis, J . and C. Krimbas. 1987. Accuracy of phylogenetic trees estimated from DNA sequence data. Molecular Biology and Evolution 4:159-166.


Rzhetsky and Nei 1992

Rzhetsky, A. and M. Nei. 1992. A simple method for estimating and testing minimum evolution trees . Molecular Biology and Evolution 9:945-967.


Rzhetsky and Nei 1993

Rzhetsky, A. and M. Nei. 1993. Theoretical foundation of the minimum-evolution method of phylogenetic inference. Molecular Biology and Evolution 10:1073-1095.


Studier and Keppler 1988

Studier, J. A. and K. L. Keppler. 1988. A note on the neighbor-joining algorithm of Saitou and Nei. Molecular Biology and Evolution 5:729-731.


Maddison and Maddison 1992

Maddison, W. P. and D. R. Maddison. 1992. MacClade: Analysis of phylogeny and character evolution. Version 3. Sinauer Associates, Sunderland, Massachusetts.


Fitch 1971

Fitch, W. M. 1971. Towards defining the course of evolution: Minimum change for a specific tree topology. Systematic Zoology 20:406-416.


Eck and Dayhoff 1966

Eck, R. V. and M. O. Dayhoff. 1966. Atlas of Protein Sequence and Structure. National Biomedical Research Foundation, Silver Springs, Maryland.


Sankoff and Cedergren 1983

Sankoff, D. and R. J. Cedergren. 1983. Simultaneous comparison of three or more sequences related by a tree. In D. Sankoff and J. B. Kruskal, eds., Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, pp. 253-263. Addison-Wesley, Reading, Massachusetts.


Felsenstein 1978

Felsenstein, J. 1978. Cases in which parsimony or compatibility methods will be positively misleading. Systematic Zoology 27:401-410.


Hendy and Penny 1989

Hendy, M. D. and D. Penny. 1989. A framework for the quantitative study of evolutionary trees. Systematic Zoology 38:297-309.


DeBry 1992

DeBry, R. W. 1992. The consistency of several phylogeny-inference methods under varying evolutionary rates. Molecular Biology and Evolution 9:537-551.


Tateno et al. 1994

Tateno, Y., N. Takezaki, and M. Nei. 1994. Relative efficiencies of the maximum likelihood, neighbor-joining, and maximum parsimony methods when substitution rate varies with site. Molecular Biology and Evolution 11:261-277.


Sourdis and Nei 1988

Sourdis, J. and M. Nei. 1988. Relative efficiencies of the maximum parsimony and distance-matrix methods in obtaining the correct phylogenetic tree. Molecular Biology and Evolution 5:298-311.


Hendy and Penny 1982

Hendy, M. D. and D. Penny. 1982. Branch and bound algorithms to determine minimal evolutionary trees. Mathematical Biosciences 59:277-290.


Kishino and Hasegawa 1989

Kishino, H. and M. Hasegawa. 1989. Evaiuation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in Hominoidea. Journal of Molecular Evolution 29:170- 179.


Efron 1982

Efron, B. 1982. The Jackknife, the Bootstrap and Other Resampling Plans. CBMS-NSF Regional Conference Series in Applied Mathematics, Monograph 38, SIAM, Philadelphia.


Felsenstein 1985

Felsenstein, J. 1985. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39:783-791.


Penny and Hendy 1985

Penny, D. and M. D. Hendy. 1985. The use of tree comparison metrics. Systematic Zoology 34:75-82.


Felsenstein and Kishino 1993

Felsenstein, J. and H. Kishino. 1993. Is there something wrong with the bootstrap on phylogenies? A reply to Hillis and Bull. Systematic Biology 42:193-200.


Hillis and Bull 1993

Hillis, D. M. and J. J. Bull. 1993. An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. Systematic Biology 42:182-192.


Nei et al. 1985

Nei, M., J. C. Stephens, and N. Saitou. 1985. Methods for computing the standard errors of branching points in an evolutionary tree and their application to molecular data from humans and apes. Molecular Biology and Evolution 2:66-85.


Hedges et al. 1992

Hedges, S. B., S. Kumar, K. Tamura, and M. Stoneking. 1992. Human origins and analysis of mitochondrial DNA sequences. Science 255:737-739.


Nei and Kumar 2000

Nei, M. and S. Kumar. 2000. Molecular Evolution and Phylogenetics. Oxford University Press, New York.


Press et al. 1993

To complete


Comeron 1995

Comeron, J. M.  1995.  A method for estimating the numbers of synonymous and nonsynonymous substitutions per site. Journal of Molecular Evolution 41:1152-1159.


Dopazo 1994

Dopazo, J.  1994.  Estimating errors and confidence intervals for branch lengths in phylogenetic trees by a bootstrap approach. Journal of Molecular Evolution 38:300-304.


Kumar et al. 1993

Kumar, S., K. Tamura, and M. Nei.  1993.  MEGA: Molecular Evolutionary Genetics Analysis. Pennsylvania State University, University Park, PA.


Zhang et al. 1998

Zhang, J., H. F. Rosenberg, and M. Nei.  1998.  Positive Darwinian selection after gene duplication in primate ribonuclease genes. Proceedings of the National Academy of Sciences USA 95:3708-3713.


Zhang et al. 1997

Zhang, J., S. Kumar, and M. Nei.  1997.  Small-sample tests of episodic adaptive evolution: a case study of primate lysozymes. Molecular Biology and Evolution 14:1335-1338.


Zhang and Gu 1998

Zhang, J. and X. Gu.  1998.  Correlation between the substitution rate and rate variation among sites in protein evolution. Genetics 149:1615-1625.


Swofford 1998

Swofford, D. L. 1998. PAUP*: Phylogenetic Analysis Using Parsimony (and Other Methods) Sunderland, MA: Sinauer Associates.


Yang 1999

Yang, Z. 1999. PAML: Phylogenetic analysis by maximum likelihood, ver 2.0. University College London, London.


Gu and Zhang 1997

Gu, X. and J. Zhang. 1997. A simple method for estimating the parameter of substitution rate variation among sites. Molecular Biology and Evolution 15:1106-1113.


Hillis et al. 1996

Hillis, D. M., C. Moritz and B .K. Mable. 1996. Molecular Systematics. 2 ed. Sunderland, MA: Sinauer Associates, Inc.


Li 1997

Li, W.-H. 1997. Molecular Evolution. Sunderland, MA: Sinauer Associates.


Page and Holmes 1998

Page R. D. M. and E. C. Holmes 1998. Molecular Evolution: A Phylogenetic Approach.  Blackwell Science, Oxford, U.K.


Nei et al. 1998

Nei, M., S. Kumar, K. Takahashi. 1998. The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small. Proceedings of the National Academy of Sciences USA 95:12390-12397


Takahashi and Nei 2000

Takahashi, K., and M. Nei. 2000. Efficiencies of fast algorithms of phylogenetic inference under the criteria of maximum parsimony, minimum evolution, and maximum likelihood when a large number of sequences are used. Molecular Biology and Evolution 17:1251-1258.


Li 1993

Li, W.-H. 1993. Unbiased estimation of the rates of synonymous and nonsynonymous substitution. Journal of Molecular Evolution 36:96-99.


Swofford et al. 1996

Sankoff, D. L., G. J. Olsen, P. J. Waddell, and D. M. Hillis. 1996. Phylogenetic Inference. In D. M. Hiillis, C. Mortiz, and B. K. Mable, eds., Molecular Systematics, pp. 407-514.Sinauer Associates, Sunderland, Massachusetts.


Tajima 1989

Tajima, F. 1989. Statistical methods to test for nucleotide mutation hypothesis by DNA polymorphism. Genetics 123:585-595.


Kumar and Gadagkar 2001

Kumar, S. and S. R. Gadagkar 2001. Disparity Index: A simple statistic to measure and test the homogeneity of substitution patterns between molecular sequences.  Genetics 158:1321-1327.


Open Data

File | Open Data

Choose this command to load a data file for analysis. A dialog box will appear to allow you to give the data file name. Once a data file is given, MEGA will first read the data file to check if it contains the Format command (see MEGA format), which specifies certain attributes of the input data (e.g., type of data).  If MEGA does not find sufficient information in the Format command, it will request the necessary information through an Input Data Format dialog.

If you attempt to open a data set from a file and MEGA detects inconsistencies or errors in the data format, the data file will then be opened in the text editor, allowing you to make any required changes to the text file to conform it to the MEGA format.

Once a data file is opened successfully, some basic attributes of that data set can be seen in the bottom of the main window.  The Open Data command is now disabled. To enable this command, close the currently active data using the File | Close Data command.

Open dialog box

Use the Open dialog box to load new data into MEGA for analysis.

Property	Description
Look In	Lists the current directory. Use the drop-down list to select a different drive or directory.
Files	Displays all files in the current directory matching the wildcards given in File Name or the file type in Files Of Type. You can display a list of files (default) or you can show details for each file.
File Name	Enter the data file name you want to load or type in the wildcards to use as filters.
Files of Type	Choose the type of data file you want to open.  At present MEGA allows you to load in MEGA format files only, which should usually have the .MEG extension.
Up One Level	Clicking this button will move you one directory level up from the current directory.
Create New Folder	Click this button to create a new subdirectory in the current directory.
List	Click this button to view a list of files and directories in the current directory.
Details	Click this button to view a list of files and directories along with time stamp, size, and attribute information.


Export Data

File | Export Data

Choosing this command brings up a message asking you to use the Write Data command in the Data Explorer accessible through the Data menu (Data | Data Explorer).  In the Data Explorer, you may save the currently active data subset to a file in the chosen format (MEGA, PHYLIP; PAUP, and Publication formats).


Close Data

File | Close Data

Deactivates the currently open data file.   Before issuing this command, make sure that you have saved any modifications to the data you wish to retain by exporting the data through the data explorer (Data | Data Explorer) in the MEGA format.

This command is enabled only if a dataset is loaded in MEGA for analysis.


Reopen Data

File | Reopen Data

Choose this command to reopen a recently closed data file from the submenu, which consists of names of the five most recently used data files.


Printer Setup

File | Printer Setup

Choose this command to change the properties of your printer.


Exit

File | Exit

Choose this command to close MEGA.  The currently active data file and all other windows will be automatically closed. Therefore, before issuing this command, you may want to issue the File | Export Data command to save any change to the data set, and issue Print or Save commands for the results displayed on the screen, if needed.   In particular, note that MEGA does not automatically save changes made to the active data to the original data file.


Main MEGA Window

The main window in MEGA contains a menu bar, a toolbar, and a Data Description Window (DDW).  The menu bar may contain two or more menu items depending on whether a data file is active and the type of data being analyzed.

Menu Bar

Menus	Description
File menu	Use the File menu commands to open, save, close data for analysis and for editing text files
Data menu	Use the View menu commands to display the active data, edit different data attributes, and compute basic statistical properties of the data.
Distance menu	Use the Distance menu commands to calculate evolutionary distances and diversity.
Phylogeny menu	Use the Phylogeny menu commands to calculate evolutionary trees, test their reliability, and view saved trees.
Tests menu	Use the Tests menu commands to conduct various tests, including that for selection, inferred phylogeny, and molecular clocks.
Help menu	Use the Help menu to access the online Help system, which is displayed in a special Help window.

Toolbar

Contains shortcuts to some frequently used menu commands, such as those in the View menu.

Data Description window

Displays a summary of the currently active data set for your convenience.


Data Explorer

Data | Data Explorer

This command is used to view the currently active data set, calculate basic statistical attributes of the data, export data in formats compatible with other programs, and define subsets of the data for analysis.  Depending on the currently active data type, one of the following explorers will be available.

Data Type	Explorer
DNA, RNA, Protein sequences	Sequence Data Explorer
Evolutionary divergence	Distance Data Explorer


Setup/Select Taxa & Groups

Data | Setup/Select Taxa & Groups

This command invokes the Setup/Select Taxa & Groups dialog box for including/excluding taxa, defining groups of taxa, and editing names of taxa and groups.


Setup/Select Taxa & Groups Dialog

This dialog box has two sub-windows (Taxa/Groups windows and the Ungrouped Taxa window), a panel bar between them containing a few buttons, and a command panel (lower part containing Add, Delete, Close, and Help buttons).

Taxa/Groups sub-window on the left:  It shows all the currently defined taxa and group names in a hierarchical fashion.  If a taxon has been assigned to a group, it will appear connected to that group.  Groups may be displayed in collapsed format (indicated by a + mark before their name). You can click '+' to expand the group to a listing of the taxa contained in it, and click ‘–‘ to collapse the group to only view the group name. Groups that do not contain any members will not have this box. The next item is a checkbox indicating whether a given group or taxon will be included in analysis. Then there is an icon to indicate a taxon (single box) or a group (layer of boxes).  Grayed out check boxes are used to indicate that some of the taxa of a group are selected and others are unselected.  You can rearrange the order of taxa and groups using drag-and-drop. However, note that this order is not automatically used in the Data Explorer. To enforce this order, use the Sort command in the Data Explorer.

Ungrouped Taxa sub-window on the right:  This shows the names of all the taxa that do not belong to any of the groups.  It is provided to make it easier to move taxa to groups.  If it does not appear on your screen, then hold and drag the lower right corner of the dialog box to expand its width to unhide it.

Middle Command Panel:  It resides between the above two sub-windows and contains a splitter on its right edge.  You can grab the splitter and move it to change the proportion of the space taken by the two sub-windows.  Left and right arrow buttons in this panel are used to add or remove taxa to groups.  Clicking the hand-with-a-pencil icon with a taxon or group name highlighted will allow you to edit their name.

Lower Command Panel:  In the lower part of the Select/Edit Taxa/Groups window are buttons that are used to add and/or delete groups.  The ‘+’ and ‘–‘ buttons are also present on the middle command panel.

Buttons	Description
Add	Creates a new group.
Delete	Deletes the currently selected group. The group is removed from the list. Any taxa that were assigned to the group will become freestanding.
Close	Closes the dialog box.
Help	Brings up help regarding the dialog box.
How to perform functions:

Function	Description
Creating a new group	Click on the Add button. Click on the highlighted name of the group and type a new name.
Deleting a group	Select the group and click the Delete button.  Any taxa that were assigned to this group will become freestanding.
Adding taxa to a group	Drag-and-drop the taxon on the desired group. Or, select one or more taxa in Ungrouped Taxa window and click on the left arrow button on the middle command panel.
Removing a taxon from a group	Click on the taxon and drag-and-drop it into a group (or outside all groups).  Or, select the taxon and click on the right arrow button on the middle command panel.
Include/Exclude taxa or groups	Click the checkbox next to the group or taxa name.


Setup/Select Genes & Domains

Data | Setup/Select Genes & Domains

Use this command to view, specify, and edit genes and domains; and to label sites. These operations are facilitated by the Setup/Select Genes & Domains dialog box.


Setup/Select Genes & Domains Dialog

Use the Gene & Domain Editor to inspect, define, and select domains and genes.  You can also inspect, define and edit labels for individual sites.

The Genes & Domains dialog consists of two tabs: Define/Edit/Select and Site Labels.

Define/Edit/Select tab

This tab contains a hierarchical listing of gene and domain names with the corresponding information organized in four columns for amino acid sequence data and six columns for nucleotide sequences.

Gene and domain name listing

Each line in this display contains a small 'expand/contract' box, a checkbox, a gene/domain icon, and the name of the gene/domain. The 'expand/contract' box allows you to display or hide the information below a given gene. The checkbox is checked if the gene or domain is currently selected for analysis.  All defined genes and domains appear below the Genes\Domain node in the hierarchy.  All domain names are shown with a yellow background.  The Independent node shows the number of Independent sites, which are not assigned to any domains/genes.

If your input data file did not contain any domains, then MEGA will automatically create a domain called ‘Data’.  If you wish to create new domains, then we recommend that you delete the ‘Data’ domain to make all sites independent.  Remember that only independent sites can be assigned to domains, and that sites cannot be assigned to multiple domains.  Genes are simply collections of domains, and thus the gene boundaries are decided based on the domains contained in it.  The MEGA gene/domain organizer is flexible and it is designed to enable you to specify genes and domains as they appear in a genome.  For instance, a sequence may contain one or more genes, each of which may contain one or more domains.  In between genes, there may be intergenic domains.  Within or between genes or domains, you can have sites that are not a member of any domain.

At the bottom of this tab, you will find a toolbar with many drop-down menu buttons.  These buttons can be used to Add/Insert new genes or domains.  The Add and Insert operations differ in the following way.  If you Add a gene (or domain), then the new gene (or domain) will be added at the end of the list to which the currently focused gene (or domain) belongs to.  In the case of Insert, the new gene (or domain) will be inserted by shifting all the following genes (or domain) down.  Add and Insert commands work in a context sensitive fashion.

You can rearrange the relative position of genes and domains by Drag-and-drop operations.  Also, you can drag-and-drop domains from one gene to another, etc.

Inspecting/modifying attributes of genes and domains

When you start, all genes and domains are shown.  Click on the ‘+’ in the expand/contract box to expand the listing for each gene to its domains. Click again on the ‘-‘ to collapse to the gene. To select and deselect genes or domains from analysis, click in the corresponding checkbox. When a gene is selected but some domains within the gene are not, the checkbox for the gene is grayed to warn you. If you deselect a gene, all domains within that gene are also automatically deselected.

On the right side of the Gene and domain hierarchy, you will find at least four columns of information for each domain (and gene).  All information shown for genes is computed based on the domains contained.

The first two columns show the site number in the sequence where the domain begins (From column) and where it ends (To column).  The number of total sites shown next to the To column shows the total number of sites automatically computed based on the information given on the range given in the previous two columns.  A question mark (?) shows that the domain exists but that the range of sites is yet to be specified.

To specify (or change) sites that belong to a given domain, just click on the domain name.  You will see that the corresponding rows in the From and the To columns contain a button with three dots (ellipses).  To change the start site, just click on the ellipses in the From column, it will bring up a small site picker dialog box where you can highlight the desired site and click OK.  In this viewer, you will see that sites have different background colors.  White background marks independent sites, a red background indicates that the site is used by another domain, and the yellow background shows that the current site in fact belongs to the domain being edited.  To cancel any changes, just click on the Cancel button in the site picker dialog box.

For nucleotide sequences, two additional columns are found in the Define/Edit/Select tab:  Coding? column and the Codon Start column.  A check mark in the Coding? column shows that a given domain is protein coding.  If it is checked, then the next column allows you to specify whether the first site in the domain is the first codon position, second codon position, or the third codon position.

Site Labels Tab

This tab displays the sequences and allows the users to label individual sites.  This is done by changing the default underscore (_) to the label of choice in the topmost line with a light green background.  The site number is displayed below in a window, next to which the name of the domain (along with gene name) is shown.   Labeled sites can be selected or deselected for analysis.

To change or give a label to a site, click on that site and type the character you wish to mark that site with. You can use the left and right arrow buttons on the keyboard to move to adjacent sites to label them. To change a label, simply overtype. To remove a label, type a space (use the spacebar).

Example

Imagine a sequence alignment consisting of genomic sequence including a gene and its upstream and downstream regions. You can define each intron and exon as a domain, and then define the overall gene, assigning the exons and introns to that gene. The upstream and downstream regions can also be defined as domains, or possibly multiple domains, depending on the analysis you wish to perform. These domains do not have to be assigned to any gene. Furthermore, some sites may be left unassigned, as Independent sites. These can be scattered throughout the sequence. They can be included or excluded from analysis as a group.  If you have complicated patterns of sites you wish to analyze as groups, and the domain/gene approach is unsuitable, you should assign a category to these sites (they can be specified in addition to the groups and domains).


Select Genetic Code Table

Data | Select Genetic Code Table

Use the Select Genetic Code Table dialog from the Data menu to select the genetic code used by protein-coding nucleotide sequence data.  It also allows you to add genetic codes to the list, edit existing codes, and compute a few simple statistical properties of chosen genetic code. This option becomes visible when you open a data set containing nucleotide sequences.


Select Genetic Code Table Dialog

Use this dialog for selecting the genetic code to use, and for editing and displaying properties of genetic codes.  At present only one genetic code can be selected in MEGA at any given time, which will be used for all coding regions in all sequences in the data set.

Click in the square box left of the genetic code you wish to select.

You can also highlight any genetic code by clicking on the text.

You can then use the following buttons found along the top of the dialog box:

Button	Description
Add	Creates a new genetic code table.  A code table editor will be shown with the genetic code of the currently highlighted code table loaded.
Delete	Removes the highlighted genetic code from the list.  Note that the standard genetic code cannot be deleted.
Edit	Used to modify the highlighted genetic code or its name.  The code table editor will be invoked for you to edit the genetic code.
View	Displays the highlighted genetic code in a printable format.
Statistics	Displays the number of synonymous and non-synonymous sites for the codons of the highlighted genetic code following the Nei-Gojobori (1986) method.  The degeneracy values for the first, second, and third codon positions are also displayed following Li et al. (1985).


Select Preferences

Data | Select Preferences

This submenu can be used to specify (1) how the alignment gaps and missing will be handled, (2) which codon positions are to be used, and (3) whether or not to restrict the analysis to the sites with selected labels.  One or more of these options may be disabled depending on the attributes of the data set.  For instance, the selection of codon positions is not valid when the amino acid sequence data is being analyzed.

It is worth noting that all these options are also available in the Options dialog box that appears in response to a requested analysis (e.g., distance computation or phylogenetic reconstruction).  Thus, you have the flexibility to select and change appropriate options at the time of analysis.


Handling Gaps and Missing Data

Data | Select Preferences | Handling Gaps and Missing Data

Use this menu item to specify whether to use the Pairwise-deletion or the Complete- Deletion options for handling alignment gaps and missing data.  You can also specify these options in the dialog box that appears in response to a requested analysis (e.g., distance computation or phylogenetic reconstruction).  Therefore, you have the flexibility to select/change appropriate options at the time of analysis.


Include Codon Positions

Data | Select Preferences | Include Codon Positions

Use this menu item to specify the codon positions to include in the nucleotide sequence analysis.  You can choose to include any combination of 1st, 2nd, 3rd positions and non-coding sites.  The specified options are used only if you conduct nucleotide-by-nucleotide site analysis.  If relevant, you will also be given this choice in the dialog box that appears in response to a requested analysis (e.g., distance computation or phylogenetic reconstruction).  Thus, you have the flexibility to select/change appropriate options at the time of analysis.


Include Labeled Sites

Data | Select Preferences | Include Labeled Sites

Use this menu item to specify whether to include only the labeled sites in the analysis, and if so, which labels to include.  This option is available only if you have some sites labeled.  If relevant, you will also be given this choice in the dialog box that appears in response to a requested analysis (e.g., distance computation or phylogenetic reconstruction).  Thus, you have the flexibility to select/change appropriate options at the time of analysis.


Choose Model

Distances | Choose Model…

Choose this command to select a specific model of change to be used to compute distances.  The model can also be chosen/changed in the dialog box that appears in response to a requested analysis (e.g., distance computation or phylogenetic reconstruction).  Thus, you have the flexibility to select/change appropriate options at the time of a given evolutionary analysis.


Compute Pairwise

Distances | Compute Pairwise…

Choose this command to compute distances (and their standard errors) between pairs of taxa.  A Select Distance Options dialog will appear where you can choose the desired distance estimation method and other relevant options.


Select Distance Options Dialog

When you choose to compute distances, this dialog box allows you to select the type of distance and options associated with it.  There are three major parts to this dialog box.

At the very top are two tabs, described below:

Tab Name	Description
Distance Options	Use to specify the types of distance information you would like to see and how it should be computed.
Include Sites	Use to further select a subset of the currently active data and how the missing data and alignment gaps should be handled.

Below these tabs is the Model button, which reveals a menu of distance methods and types.  The selected distance method is displayed next to the Model button.

Below the Model button, you may need to specify the Transition/Transversion (R) ratio and/or the Gamma parameter, which are enabled if the distance method chosen requires that information from the user.


Compute Overall Mean

Distances | Compute Overall Mean…

Use this command to calculate the mean pairwise distance (and its standard error) for the set of sequences under study. The overall mean is the arithmetic mean of all individual pairwise distances between taxa.  A Select Distance Options dialog will appear where you can choose the desired distance estimation method and other relevant options.  Before using the bootstrap method to compute standard error, please read how MEGA implements the bootstrap method for this purpose.


Compute Within Groups Mean

Distances | Compute Within Groups Means…

Choose this command to compute the mean pairwise distances within groups of taxa.  Within group means are arithmetic means of all individual pairwise distances between taxa within groups.  A Select Distance Options dialog will appear where you can choose the desired distance estimation method and other relevant options. You must have at least one group of taxa with a minimum of two taxa defined for this option to work.

How to define groups of taxa.


Compute Between Groups Means

Distances | Compute Between Groups Means…

Choose this command to compute the average distances between groups of taxa.  Average distance between two groups is the arithmetic mean of all pairwise distances between taxa in the inter-group comparisons.  A Select Distance Options dialog will appear where you can choose the desired distance estimation method and other relevant options.

You must have at least two groups of taxa for this option to work.

How to define groups of taxa.


Compute Net Between Groups Means

Distances | Compute Net Between Groups Means…

Choose this command to compute the net average distances between groups of taxa.  The net average distance between two groups is given by

dA = dXY – (dX - dY)/2

where, dXY is the average distance between groups X and Y, and dX and dY are the mean within-group distances.  A Select Distance Options dialog will appear where you can choose the desired distance estimation method and other relevant options.

You must have at least two groups of taxa with a minimum of two taxa each for this option to work.

How to define groups of taxa.


Compute Sequence Diversity

Distances | Compute Sequence Diversity

The Sequence Diversity submenu provides four commands that are useful for computing population and subpopulation diversities in molecular population genetics studies.  In MEGA you define a population of sequences by defining a group.  Unlike the generic average within group, between group, and net between group distances calculated using other commands in the Distances menu, formulas used in the following commands are those specifically used in the population genetics analysis.

The commands are:

Mean Diversity within subpopulations

In a subpopulation, the mean diversity is defined as

 where [image] is the frequency of i-th sequence in the sample from subpopulation i, and q is the number of different sequences in this subpopulation.

Mean Diversity for Entire Population

For the entire population, the mean diversity is defined as

, where [image] is the estimate of average frequency of the i-th allele in the entire population, and q is the number of different sequences in the entire sample.

Mean Interpopulational Diversity

The estimate of interpopulational diversity is given by

.

Coefficient of Differentiation

It is the estimate of the proportion of interpopulational diversity and is given by

.


Code Table Editor

The Code Table Editor allows you to create new genetic codes to add to the existing selection in MEGA and to edit the existing genetic codes.  It always contains the code of the highlighted genetic code table from the previous window.  To name the new genetic code or to change the name of the existing code, click in the 'Name' box at the top. Type the new name.

The genetic code is shown in this editor in an intuitive fashion.  To save space, only the amino acid encoded by codons are shown. To first position of the codon is shown on the left, the second position on the top, and the third position on the right.  To find the codon for any given entry on the screen, position your mouse over the desired amino acid and wait for a moment, a yellow hint will be displayed.

To change the amino acid encoded by any codon, click and scroll down to choose the desired amino acid. Alternatively, once the codon has been selected, you can type in the first letter of the name of the amino acid and you will jump to that part of the list. To indicate a stop codon, select '***' or type *.

Once you have made all the required changes to the name and codons, click OK. Otherwise, click Cancel.  We recommend that you check the genetic code using the View option to make sure that your changes have been properly interpreted by MEGA.


Input Data Format Dialog

The Input data Format dialog is displayed if MEGA does not find enough information about the type of data included in the input file.

Data Type

It is a list of data types that the current version of MEGA is able to analyze.  Highlight the current data type by clicking on it.  Depending on the type of data selected, you may need to provide information about the following additional items.

For Sequence Data

•	Missing Data

Character used to show missing data in the data file.  It should usually be set to a question mark (?).

•	Alignment Gap

Character used to represent gaps inserted in the multiple sequence alignment.  It is set to a dash (-) by default.

•	Identical Symbol

Character used to represent identity with the first sequence in the data files.  It is set to a dot (.) by default.

For Pairwise Distance Data

•	Missing Data

Character used to show missing data in the data file.  It should usually be set to a question mark (?).

•	Matrix Format

Choose lower-left or upper-right distance matrix for the pairwise distance data type.

Note.- To avoid having to answer these questions every time you read in your data file, consider saving the data in MEGA format by exporting the data in MEGA format.


UPGMA

Phylogeny | UPGMA…

This command is used to construct a UPGMA tree.  This tree-making method assumes that the rate of evolution has remained constant throughout the evolutionary history of the taxa included.  Therefore, it produces a rooted tree.

If your input data is a distance matrix, then using this command makes MEGA directly proceed to construct and display the UPGMA tree.  In all other instances, you will be requested to specify the distance estimation method, subset of sites to include, and whether or not to conduct a test of the inferred tree through an Analysis Preferences dialog box.


Neighbor Joining (NJ)

Phylogeny | Neighbor-Joining (NJ)…

This command is used to construct a neighbor-joining (NJ) tree (Saitou & Nei 1987).  The NJ method is a simplified version of the minimum evolution (ME) method. In the ME method, distance measures that correct for multiple hits at the same sites are used, and a topology showing the smallest value of the sum of all branches is chosen as an estimate of the correct tree.  However, construction of a minimum evolution tree is time-consuming, because in principle the S values for all topologies have to be evaluated. The number of possible topologies (unrooted trees) rapidly increases with the number of taxa. Therefore, it becomes very difficult to examine all topologies.

In the case of the NJ method, the S value is not computed for all or many topologies, but the examination of different topologies is imbedded in the algorithm, so that only one final tree is produced. The algorithm of the NJ method is somewhat complicated and is explained in detail in Nei and Kumar (2000).  The NJ method produces an unrooted tree, because it does not require the assumption of a constant rate of evolution.  However, MEGA displays NJ trees in a manner similar to rooted trees for the ease of inspection.

For constructing the NJ tree, MEGA may request you to specify the distance estimation method, subset of sites to include, and whether or not to conduct a test of the inferred tree through an Analysis Preferences dialog box.


Minimum Evolution (ME)

Phylogeny | Minimum Evolution (ME)…

This command is used to construct a phylogenetic tree under the minimum evolution criterion.  In this method the sum, S, of all branch length estimates, i.e.,

S = ∑bi,

is computed for all plausible topologies, and the topology that has the smallest S value is chosen as the best tree – the ME tree.  This criterion does not require the evolutionary rate constancy assumption needed in the UPGMA analysis.  Therefore the phylogenetic tree inferred is an unrooted tree, even though it is often displayed in a manner similar to rooted trees for ease of inspection.

MEGA employs the Close-Neighbor-Interchange (CNI) algorithm to find the ME tree.  It is a branch swapping method, which begins with a given initial tree.  You can ask MEGA to automatically construct a NJ tree and use that as the starting tree.  (Alternatively, you can provide your own topology; this option will be available in version 2.1).  Note that the final tree produced after this search is not guaranteed to be the ME tree.  These options are available in the ME Tree Tab of the Analysis Preferences dialog box displayed before the phylogenetic analysis begins.  This dialog box also allows you to specify the distance estimation method, subset of sites to include, and whether or not to conduct a test of the inferred tree.


Maximum Parsimony (MP)

Phylogeny | Maximum Parsimony (MP)…

This command is used to construct phylogenetic trees under the maximum parsimony criterion.  For a given topology, the sum of the minimum possible substitutions over all sites is known as the Tree Length of that topology.  The topology with the minimum tree length is known as the Maximum Parsimony tree.

The phylogenetic tree(s) inferred using this criterion are unrooted trees, even though they are often displayed in a manner similar to rooted trees for ease of inspection.

MEGA includes the Max-mini branch-and-bound search,  which guarantees to find all the MP trees.  However, it is often too time consuming for more than 15 sequences.  In those cases, you should use the Close-Neighbor-Interchange (CNI) algorithm to find the MP tree.  It is a branch swapping method, which begins with a given initial tree.  You can ask MEGA to automatically obtain a set of initial trees by using the Min-mini algorithm with a given search factor.  Alternatively, you can provide your own topology for this purpose or use the random addition option to produce the initial trees.  These options are available in the MP Tree Tab of the Options dialog box displayed before the phylogenetic analysis begins. Note that these CNI branch-swapping procedures may not produce the MP trees or all the MP trees.

By default, all nucleotide (or amino acid) changes are weighted equally in MEGA (standard parsimony).  However for nucleotide sequences, you have the option of conducting transversion parsimony analysis.  In this case, only transversional changes are considered in calculating the tree length.  In addition, you are given options on the subset of sites to include, and whether or not to conduct a test of the inferred tree through the Analysis Preferences/Options dialog box.


Display Saved Tree Session

   Phylogeny | Display Saved Tree Session…

Use this command to display a previously saved Tree Explorer session (saved in a filename with .MTS extension).


Relative Rate Tests

Tests | Relative Rate Tests

This submenu provides access to test the constancy of evolutionary rates between two sequences or clusters of sequences, using an outgroup sequence.


Tajima's Test (Relative Rate)

Tests | Relative Rate Tests | Tajima’s Test

Use this menu item to conduct Tajima’s relative rate test (Tajima 1993).  It works in the following way.  Consider three sequences, 1, 2 and 3, and let 3 be the outgroup.  Let nijk be the observed number of sites where sequences 1, 2 and 3 have nucleotides i, j and k.  Under the molecular clock hypothesis, E(nijk)  =  E(njik) irrespective of the substitution model and whether or not the substitution rate varies with site.  If this hypothesis is rejected, then the molecular clock hypothesis can be rejected for this set of sequences.

You can select the three sequences to conduct Tajima’s test in the dialog box produced in response to this menu command.  For nucleotide sequences, it offers flexibility to use only transitions, only transversions, or both.  If the data is protein coding, then you can choose to conduct analysis on translated sequences or any combination of codon positions by clicking on the ‘Data for Analysis’ button.

See Nei and Kumar (2000) (page 193-196) for further description and an example.


Tajima's Test of Neutrality

Tests | Tajima’s Test of Neutrality

Use this menu item to conduct Tajima’s test of neutrality (Tajima 1989). Tajima’s test compares the number of segregating sites per site with the nucleotide diversity.   (A site is considered a segregating site if there are two or more nucleotides at that site in a comparison of m sequences, and nucleotide diversity is defined as the average number of nucleotide differences per site between two sequences.)  If all the alleles are selectively neutral, then the product 4Nv (where N is the effective population size and v is the mutation rate per site) can be estimated in two ways, and the difference in the estimate obtained in these two ways provides indication of non-neutral evolution.  Please see Nei and Kumar (2000) (page 260-261) for further description.


Codon-based Tests of Selection

Tests | Codon-based Tests of Selection

This submenu provides access to small sample and large sample tests of selection that are based on the comparison of the numbers of synonymous and nonsynonymous substitutions between sequences.


Fisher's Exact Test of Selection

Tests | Codon-based Tests of Selection | Fisher’s Exact Test

Use this command to conduct a small sample test of positive selection (Zhang et al. 1997).  A one-tailed Fisher’s Exact test of positive selection is conducted.  If the resulting P -value is less than 0.05, then the null hypothesis of neutral evolution (strictly neutral and purifying selection) is rejected.  If the observed number of synonymous differences per synonymous site (pS) exceeds the number of nonsynonymous differences per nonsynonymous site (pN) then MEGA sets P  = 1 to indicate that the existence of purifying selection, rather than the positive selection.

See Nei and Kumar (2000) (page 56) for further description and an example.


Z-Test of Selection

Tests | Codon-based Tests of Selection | Z-test (large sample)

One way to test whether positive selection is operating on a gene is to compare the relative abundance of synonymous and nonsynonymous substitutions that have occurred in the gene sequences.  For a pair of sequences, this is done by first estimating the number of synonymous substitutions per synonymous site (dS) and the number of nonsynonymous substitutions per nonsynonymous site (dN), and their variances: Var(dS) and Var(dN), respectively.  With this information, we can test the null hypothesis that H0: dN = dS using a Z-test:

Z = (dN - dS) / SQRT(Var(dS) + Var(dN))

The level of significance at which the null hypothesis is rejected depends on the alternative hypothesis (H1) .

H0:	 dN = dS

H1: 	(a)	dN ≠ dS	(test of neutrality).

(b)	dN > dS	(positive selection).

(c)	dN < dS	(purifying selection).

For alternative hypotheses (b) and (c), we use a one-tailed test and for (a) we use a two-tailed test.  These three tests can be conducted directly for pairs of sequences, overall sequences, or for within groups of sequences.  For testing for selection in pairwise manner, you can compute the variance of (dN - dS) by using either the analytical formulas or the bootstrap resampling method.

For data sets containing more than two sequences, you can compute the average number of synonymous substitutions and the average number of nonsynonymous substitutions to conduct a Z-test in a manner similar to the one mentioned above.  The variance of the difference between these two quantities is estimated by the bootstrap method (See Nei and Kumar (2000) page 55).


Bootstrap Test (Phylogeny)

Phylogeny | Bootstrap Test

One of the most commonly used tests of reliability of an inferred tree is Felsenstein's (1985) bootstrap test, which is evaluated using Efron's (1982) bootstrap resampling technique.  Let there be m sequences, each with n nucleotides.  A phylogenetic tree is reconstructed from these sequences using some tree building method.  From each sequence, n nucleotides (or codons or amino acids) are then randomly chosen with replacement, giving rise to m rows of n columns each.  These now constitute a new set of sequences.  A tree is then reconstructed with these sequences using the same tree building method as before.  The topology of this tree is compared to that of the original tree.  Each interior branch of the original tree that is different from the bootstrap tree in terms of the sequences it partitions is given a score of 0; all other interior branches are given the value 1.  This procedure of resampling the sites and subsequent tree reconstruction is repeated several hundred times, and the percentage of times each interior branch is given a value 1 is noted.  This is known as the bootstrap value.  As a general rule, if the bootstrap value for a given interior branch is 95% or higher, then the topology at that branch is considered "correct".

See Nei and Kumar (2000) (chapter 9) for further details.


Interior Branch Test

Tests | Interior Branch Test

This test is available only for the NJ and Minimum Evolution trees (or a user-specified tree in version 2.1).  In this case, a t-test is constructed based on the interior branch length and its standard error, which is computed using the bootstrap procedure in MEGA2.   MEGA will show the confidence probability in the TreeExplorer, if this value is greater than 95 percent for a given branch then the inferred length for that branch is considered significantly positive.

See Nei and Kumar (2000) (chapter 9) for further details.


About

Help | About…

This command will display as the About dialog box showing the copyright, authors, and the version information for MEGA.


File Menu

File Menu

This menu allows you to perform various important tasks, including activating a data file, closing the data file, editing text files, and exiting MEGA.


Data Menu

Data Menu

The Data Menu allows you to explore the active data set, set various data attributes, and data subset options.


Phylogeny Menu

Phylogeny Menu

Use the Phylogeny Menu to construct phylogenetic trees and to view previously constructed trees.


Distances Menu

Distances Menu

Use this menu to compute: pairwise and average distance between sequences; within, between, and net average distance among groups; and sequence diversity statistics for data from multiple populations.


Tests Menu

Tests Menu

This menu provides access to bootstrap and interior branch tests of phylogeny, molecular clock tests, and tests of selection.


Help Menu

Help Menu

This menu provides access to the Help Index as well as the About dialog box which provides version information for MEGA.


Index

Help | Index

This command provides access to the help file Index and keyword searching facilities.


Citing MEGA in Publications

If you wish to cite the use of MEGA in your publications, we suggest the following citation.

(1) When referring to MEGA in the main text of your publication, you may choose a format such as:

Phylogenetic and molecular evolutionary analyses were conducted using MEGA version 2.1 (Kumar et al. 2001).

(For later versions of MEGA, replace 2.1 with the appropriate version number [e.g., 2.2] that can be obtained from the Help menu in MEGA [see Help | About])

(2) When including a MEGA citation in the Literature Cited/Bibliography section, you may use the following:

Sudhir Kumar, Koichiro Tamura, Ingrid B. Jakobsen, and Masatoshi Nei (2001) MEGA2: Molecular Evolutionary Genetics Analysis software, Bioinformatics (submitted).

We will e-mail you as soon as an article describing MEGA2 is accepted for publication.  In the mean time, you may cite the following:

Sudhir Kumar, Koichiro Tamura, Ingrid B. Jakobsen, and Masatoshi Nei (2001) MEGA2: Molecular Evolutionary Genetics Analysis software, Arizona State University, Tempe, Arizona, USA.


Bootstrap Test for UPGMA Tree

Phylogeny | Bootstrap Test | UPGMA

See the bootstrap test of phylogeny description.


Booststrap Test for Neighbor-Joining Trees

Phylogeny | Bootstrap Test | Neighbor-Joining

See the bootstrap test of phylogeny description.


Bootstrap Tests for Maximum Parsimony Trees

Phylogeny | Bootstrap Test | Maximum Parsimony

See the bootstrap test of phylogeny description.


Bootstrap Tests for Minimum Evolution Trees

Phylogeny | Bootstrap Test | Minimum Evolution Trees

See the bootstrap test of phylogeny description.


Interior Branch Test for Neighbor-Joining Trees

Phylogeny | Interior Branch Test | Neighbor-Joining

See the Interior-branch test description.


Sequence Data Explorer

The Sequence Data Explorer shows the aligned sequence data.  You can scroll along the alignment using the scrollbar at the bottom-right hand side of the explorer window.  The Sequence Data Explorer provides a number of useful functionalities for exploring statistical attributes of the data and also for selecting data subsets.

This explorer consists of a number of regions as follows:

Menu Bar

Data menu

Display menu

Highlight menu

Statistics menu

Help: This item brings up the help file for the Sequence Data Explorer.

Tool Bar

The tool bar provides quick access to a number of menu items.

•	General Utilities

•	 : Clicking this icon brings up the Exporting Sequence Data dialog box, which contains options to control how MEGA writes the output data.

•	Color: Clicking this button will bring up a color palette selection box where you can choose the color to be used to display the highlighted sites.

•	 : Clicking this button will bring up the dialog box to setup and select domains and genes.

•	 : Clicking this button will bring up the dialog box for setting up, editing, and selecting taxa and groups of taxa.

•	 : This button is a toggle for replacing the nucleotide (amino acid) at a site with the identical symbol (e.g., a dot) at that site if it contains the same nucleotide (amino acid) as the first sequence displayed.

•	Highlighting Sites

•	C: If this button is pressed then all constant sites will be highlighted.  The count of highlighted sites will be displayed on the status bar.

•	V: If this button is pressed then all variable sites will be highlighted.  The count of highlighted sites will be displayed on the status bar.

•	Pi: If this button is pressed then all parsimony-informative sites will be highlighted.  The count of highlighted sites will be displayed on the status bar.

•	S: If this button is pressed then all singleton sites will be highlighted.  The count of highlighted sites will be displayed on the status bar.

•	0: If this button is pressed then sites will be highlighted only if they are zero-fold degenerate sites in all sequences displayed.  The count of highlighted sites will be displayed on the status bar.  (This button is available only if the dataset contains protein coding DNA sequences).

•	2: If this button is pressed then sites will be highlighted only if they are two-fold degenerate sites in all sequences displayed.  The count of highlighted sites will be displayed on the status bar.  (This button is available only if the dataset contains protein coding DNA sequences).

•	4: If this button is pressed then sites will be highlighted only if they are four-fold degenerate sites in all sequences displayed.  The count of highlighted sites will be displayed on the status bar.  (This button is available only if the dataset contains protein coding DNA sequences).

•	 : This button provides facility to translate codons in the sequence data into amino acid sequences and back.  All protein-coding regions will be automatically identified and translated properly for display.  When the translated sequence is already displayed, then issuing this command will display the original nucleotide sequences (including all coding and non-coding regions).  Depending on the data displayed (translated or nucleotide), relevant menu options in the sequence data explorer will become enabled.  Note that the translated/un-translated status in this data explorer does not have any impact on the analysis options available in MEGA (e.g., Distances or Phylogeny menus), as MEGA will provide all possible options for your dataset at all times.

The 2-Dimensional Data Grid

Fixed Row:  This is the first row in the data grid.  It is used to display the nucleotides (or amino acids) in the first sequence when you have chosen to show the identity with the first sequence using a special character.  For protein coding regions, it also clearly marks the first, second, and the third codon positions.

Fixed Column:  This is the first and the leftmost column in the data grid.  This column is always visible even when you are scrolling through sites.  It contains the sequence names, which have a check box associated with them.  When a sequence is included in the data set for analysis, this box is checked.  You can check/uncheck this check box to include/exclude a sequence from analysis.  In this column, you can drag-and-drop sequences to sort them in a desired manner.

Rest of the Grid:  Cells to the right of the first column and below the first row contain the nucleotides or amino acids of the input data.  Note that all cells are drawn in light color if they contain data corresponding to unselected sequences or genes/domains.

Status Bar

This section displays the location of the focused site and the total sequence length.  It also shows the site label, if any.  When certain sites are highlighted, the count of the highlighted sites is also shown in this area.


Export Data (in Sequence Data Explorer)

Data | Write Data to File

This dialog box, called Exporting Sequence Data, first displays an edit box for entering a title for the sequence data being exported.  The default name is the original name of the data set, if there was one.  Below the title area is an area for the user to be able to enter a brief description of the data set being exported.

Next is the option for determining the format of the data set being exported; MEGA currently allows the user to export the data in MEGA, PAUP 3.0 and PAUP 4.0 (Nexus, Interleaved in both cases), and PHYLIP 3.0 (Interleaved).  Next to this is "Writing site numbers." The three options available here are not to write any number, write one for each site, and write the site number of the last site in each line, at the end of the line.

Other options in the dialog box include the number of sites per line, which codon position(s) are to be used and whether or not noncoding regions should be included, and whether the output is to be interleaved.  As far as missing/ambiguous data and alignment gaps are concerned, there are four options: include all such data, exclude all such data, exclude/include sites with missing/ambiguous data only, and exclude sites with alignment gaps only.


Translate/Untranslate (in Sequence Data Explorer)

Data | Translate/Untranslate

This command is available only if the data contains protein-coding nucleotide sequences.  MEGA will automatically extract all protein-coding domains (regions) for translation and display the corresponding protein sequence.  If the translated sequence is already displayed, then issuing this command will display the original nucleotide sequences (including all coding and non-coding regions).  Depending on the data displayed (translated or nucleotide), relevant menu options in the sequence data explorer will be enabled.  However, translated/un-translated status does not have any impact on the analysis options available in MEGA (e.g., Distances or Phylogeny menus), as MEGA will provide all possible options for your dataset at all times.


Setup/Select Genes & Domains (in Sequence Data Explorer)

Data | Setup/Select Genes & Domains

Setup/Select Genes & Domains,  invoked from within the Data menu in Sequence Data Explorer is also available in the main interface directly in the Data Menu.


Select Genetic Code Table (in Sequence Data Explorer)

Data | Select Genetic Code Table

Select Genetic Code Table,  invoked from within the Data menu in Sequence Data Explorer is also available in the main interface directly in the Data Menu.


Setup/Select Taxa & Groups (in Sequence Data Explorer)

Data | Setup/Select Taxa & Groups

Setup/Select Taxa & Groups,  invoked from within the Data menu in Sequence Data Explorer is also available in the main interface directly in the Data Menu.


Quit Data Viewer

Data | Quit Data Viewer

This command closes the Sequence Data Explorer, and takes the user back to main interface.


Show Only Selected Sequences

Display | Show only Selected Sequences

The check boxes in the left column of the display grid can be used to select (check) or deselect (uncheck) sequences for analysis.  Subsequent use of the "Show Only Selected Sequences" option in the Display menu of Sequence Data Explorer hides all the deselected sequences and displays only the selected ones.


Use Identical Symbol

Display | Use Identical Symbol

When there are multiple aligned sequences in the data it may be easier to view the sequences if the nucleotide (amino acid) at each site is replaced by a dot in all the sequences if it is the same as that nucleotide (amino acid) in the corresponding site in the first sequence.  This makes it easier to visualize the alignment.  Choosing this option again brings back the nucleotide (amino acid) single-letter codes.


Color Cells

Display | Color cells

This command colors individual cells in the two-dimensional display grid according to the nucleotide or amino acid it contains.  A list of default colors, based on biochemical properties of the residues, is given below.  In a future version, these colors will be customizable by the user.

For DNA sequences:

Symbol	Color
A	Yellow
G	Fuchsia
C	Olive
T	Green
U	Green

For amino acid sequences:

Symbol	Color		Symbol	Color
A	Yellow		M	Yellow
C	Olive		N	Green
D	Aqua		P	Blue
E	Aqua		Q	Green
F	Yellow		R	Red
G	Fuchsia		S	Green
H	Teal		T	Green
I	Yellow		V	Yellow
K	Red		W	Green
L	Yellow		Y	Lime


Sort Sequences

Display | Sort Sequences

The sequences in the data set can be sorted based on several options: sequence name, group name, group and sequence names, or as per the order in the Select/Edit Taxa Groups dialog box.


Sort Sequences By Sequence Name

Display | Sort Sequences | By Sequence Name

The sequences are sorted by the alphabetical order of sequence names or numerical order of sequence ID numbers.  If the sequence names contain both a name and a number, then the sorting is done in a nested fashion, with the numerical order nested within the alphabetical order.


Sort Sequences by Group Name

Display | Sort Sequences | By Group Name

If the sequences have been grouped in Select/Edit Taxa Groups they can now be sorted by the alphabetical order of group names or numerical order of group ID numbers.  If the group names contain both a name and a number, then the sorting is done in a nested fashion, with the numerical order nested within the alphabetical order.


Sort Sequences by Group and Sequence Names

Display | Sort Sequences | By Group and Sequence Names

If the sequences have been grouped in Select/Edit Taxa Groups they can now be sorted by the alphabetical order of group names or numerical order of group ID numbers.  If the group names contain both a name and a number, then the sorting is done in a nested fashion, with the numerical order nested within the alphabetical order.  The sequences can be further arranged by sorting the sequence names in a nested fashion within the group names.


Sort Sequences As per Taxa/Group Organizer

Display | Sort Sequences | As per Taxa/Group Organizer

The sequence/group order seen in Select/Edit Taxa Groups is initially the same as the order in the input text file.  However, this order can be changed there by a process of drag-and-drop.  If the user wishes to see the same order in Sequence Data Explorer as in Select/Edit Taxa Groups, then this option must be chosen.


Restore Input Order

Display | Restore Input Order

Choosing this option restores the order in Sequence Data Explorer by that in the input text file.


Show Sequence Names

Display | Show Sequence Names

This option displays the full sequence names in Sequence Data Explorer


Show Group Names

Display | Show Group Names

This option displays the full group names in Sequence Data Explorer if the sequences have been grouped in Select/Edit Taxa Groups


Change Font...

Display | Change Font…

This command brings up the Change Font dialog box that allows various changes to be made to the display font, including font type, style and size.  Options to strikeout or underline selected parts of the sequences are also available.  There is also an option for using different scripts.  However, the only option available at present is "Western".  Finally, there is a window in the dialog box that displays the effect of the choices made ("Sample").


Highlight Conserved Sites

Highlight | Conserved Sites

Use this command to highlight conserved sites


Highlight Variable Sites

Highlight | Variable Sites

Use this command to highlight variable sites sites.


Highlight Parsimony Informative Sites

Highlight | Parsim-Info Sites

Use this command to highlight parsimony-informative sites.


Highlight Singleton Sites

Highlight | Singleton Sites

Use this command to highlight singleton sites.


Highlight 0-fold Degenerate Sites

Highlight | 0-fold Degenerate Sites

Use this command to highlight 0-fold degenerate  sites.


Highlight 4-fold Degenerate Sites

Highlight | 4-fold Degenerate Sites

Use this command to highlight 4-fold degenerate sites. This command is visible only if the data consists of nucleotide sequences.


Highlight 2-fold Degenerate Sites

Highlight | 2-fold Degenerate Sites

Use this command to highlight 2-fold degenerate sites. This command is visible only if the data consists of nucleotide sequences.


Nucleotide Composition

Statistics | Nucleotide Composition

This command is visible only if the data consists of nucleotide sequences.  MEGA will compute the base frequencies for each sequence as well as an overall average.  They will be displayed in a Text Editor domain by domain (if domains have been defined in Setup/Select Genes & Domains).


Nucleotide Pair Frequencies

Statistics | Nucleotide Pair Frequencies

This command is visible only if the data consists of nucleotide sequences.  There are two options available here: one, where nucleotide acid pairs are counted site-by-site bidirectionally for the two sequences (giving rise to 16 different nucleotide pairs); the other, where the pairs are counted unidirectionally (10 nucleotide pairs).  MEGA will compute the frequencies for these quantities for each sequence as well as an overall average.  They will be displayed in a Text Editor domain by domain (if domains have been defined in Setup/Select Genes & Domains).


Codon Usage

Statistics | Codon Usage

This command is visible only if the data contains protein-coding nucleotide sequences.  MEGA2 will compute the percent codon usage and RCSU values for each codon for all sequences included in the dataset.  Results will be displayed in a Text Editor domain by domain (if domains have been defined in Setup/Select Genes & Domains).


Amino Acid Composition

Statistics | Amino acid Composition

This command is visible only if the data consists of amino acid sequences or if the translated protein coding nucleotide sequences are displayed.  MEGA will compute the amino acid frequencies for each sequence as well as an overall average.  They will be displayed in a Text Editor domain by domain (if domains have been defined in Setup/Select Genes & Domains).


Use All Selected Sites

Statistics | Use All Selected Sites

Analysis is conducted on all sites in the sequences, irrespective of whether any sites have been labeled or highlighted.


Use only Highlighted Sites

Statistics | Use only Highlighted Sites

Sites can be selected according to various criteria (see Highlight Sites), and analysis will be performed only on the chosen subset of sites.  All statistical attributes will be based only on these sites.


Data Menu (in sequence Data Explorer)

This menu provides access to commands for exporting the selected data subset to various formats, translating/un-translating the protein coding sequences, defining and including/excluding genes and domains, defining and including/excluding taxa and groups, etc.

The commands in this menu are:

Write Data to File	This brings up the Exporting Sequence Data dialog  box.
Translate/Untranslate	Translates protein-coding nucleotide sequences into protein sequences, and back to nucleotide sequences.
Select Genetic Code Table	Brings up the Select Genetic Code dialog box, where you can select, edit or add a genetic code table.
Setup/Select Genes and Domains	Brings up the Sequence Data Organizer, where you can define and edit genes and domains.
Setup/Select Taxa and Groups	Brings up the Select/Edit Taxa and Groups dialog, where you can edit taxa and define groups of taxa.
Quit Data Viewer	Takes the user back to the main interface.


Display Menu (in Sequence Data Explorer)

This menu provides access to commands for adjusting the display of DNA and protein sequences displayed in the grid.

The commands in this menu are:

•	Show only selected sequences:  If the user is interested only in a subset of all the sequences in the data set, then the specific sequences must be chosen by checking the check box .  Subsequently choosing this option will display only those sequences that are checked.

•	Use Identical Symbol:  Replaces the nucleotide (amino acid) at a site with a dot (.) if it contains the same nucleotide (amino acid) as the corresponding site in the sequence that is first in the list.  The Sequence Data Explorer will return to displaying the single letter code for the nucleotide (amino acid) if you uncheck this option.

•	Color Cells:  Choosing this option displays the sequences in color such that consecutive sites with the same nucleotide (amino acid) are painted with the same color.

•	Sort Sequences:  The sequences in the data set can be sorted based on several options: sequence names, group names, group and sequence names, or as per the order in the Select/Edit Taxa Groups dialog box.

•	Restore input order:  Resets any changes in the order of the displayed sequences (due to sorting, etc.) back to that in the input data file.

•	Show Sequence Name:  The name of the sequences can be displayed or hidden by checking or unchecking this option.  If the sequences have been grouped, then unchecking this option would retain only the group name.  If no groups have been made then no name is displayed.

•	Show Group Name.  This option can be used to display or hide group names if the taxa have been categorized into groups.

•	Change Font.  Brings up the Font dialog box, and allows the user to choose the type, style, size, etc. of the font used to display the sequences.


Highlight Menu (in Sequence Data Explorer)

This menu can be used to highlight certain types of sites.  The options are conserved sites, variable sites, parsimony-informative sites, singleton sites, 0-fold, 2-fold and 4-fold degenerate sites.


Statistics Menu (in Sequence Data Explorer)

Various summary statistics of the sequences can be computed and displayed using this menu.  They are:

Nucleotide Composition.

Nucleotide Pair Frequencies.

Codon Usage.

Amino Acid Composition.

Use All Selected Sites.

Use only Highlighted Sites.  Sites can be selected according to various criteria (see Highlight Sites), and analysis can be performed only on the chosen subset of sites.


Sequence Data Subset Selection

In MEGA any subset of sequence data can be selected for analysis using the options available in the Data menu.  You may

1.	Select Taxa (sequences) or Groups of taxa through the Setup/Select Taxa & Groups dialog box,

2.	Choose Domains and Genes through the Setup/Select Genes & Domains dialog box,

Items 1 and 2 lead to the construction of a primary data subset, which is maintained until it is modified in the two dialog boxes mentioned in the above items or in the Sequence Data Explorer.

3.	Select any combination of Codon Positions to use through the Analysis Preferences/Options dialog box from the Data | Select Preferences menu item in the main interface.

4.	Choose to include only the Labeled Sites through the Data | Select Preferences menu item.

5.	Decide to enforce Complete-Deletion or Pairwise-Deletion of the missing data and alignment gaps.

Items 3, 4, and 5 provide the second level of data subset options.  You are given relevant choices immediately prior to the start of the analysis.  Therefore, these choices are secondary in nature and are specific to the currently requested analysis.  Of course, the Analysis Preferences dialog box remembers them for your convenience and provides them as default the next time you conduct an analysis that utilizes those options.


Options dialog box

At the top is an option for the output format (Publication and MEGA).  The type of information that is output (Distances) is mentioned below the output format.  Below this is the option for outputting the distance data as a lower left triangular matrix or an upper right triangular matrix.

On the right are options to specify the number of decimal places for the pairwise distances in the output, and the maximum number of distances per line in the matrix.

In addition there are three buttons, one to print/save the output, one to quit the Options dialog box without exporting the data (Cancel), and the third to bring up the help file (this file).  The print/save button brings up the Distances Display Box, where the distances are displayed as specified, with various options to edit, print and save the output.


Distance Data Explorer

The Distance Data Explorer shows the pair-wise distance data.  This explorer is flexible and it provides useful functionalities for computing within group, among group, and overall averages; as well as facilities for selecting data subsets.

This explorer consists of a number of regions as follows:

Menu Bar

File menu

Display menu

Average menu.

Help: This item brings up the help file.

Tool Bar

The tool bar provides quick access to a number of menu items.

•	General Utilities

•	 :  Clicking this icon brings up the Options dialog box to export the distance matrix as a text file with options to control how MEGA writes the output data.

•	 : Clicking this button will bring up the dialog box for setting up, editing, and selecting taxa and groups of taxa.

•	Distance Display Precision

•	 : Decreases the precision of the distance display by one decimal place with each click of the button.

•	 : Increases the precision of the distance display by one decimal place with each click of the button.

•	Column Sizer: It is a slider that can be used to increase or decrease the width of the columns that show the pairwise distances.

The 2-Dimensional Data Grid

This grid displays the pair-wise distances between all the sequences in the data in the form of a lower or upper triangular matrix.  The names of the sequences/groups are the row-headers and the column headers are numbered from 1 to m, m being the number of sequences.  There is a column sizer button for the row-headers, so as to be able to increase/decrease the column size to accommodate the full name of the sequences/groups.

Fixed Row:  This is the first row in the data grid that displays the column number.

Fixed Column:  This is the first and the leftmost column in the data grid.  This column is always visible even if you scroll past the initial screen.  It contains taxa names, which have a check box associated with them.  When a taxon is included in the data set for analysis, this box is checked.  You can check/uncheck this check box to include/exclude taxa from analysis.  In this column, you can drag-and-drop taxa names to sort them in a desired manner.

Rest of the Grid:  Cells to the right of the first column and below the first row contain the nucleotides or amino acids of the input data.  Note that all cells are drawn in light color if they contain data corresponding to unselected sequences or genes/domains.

Status bar

The Status bar shows the sequence pair corresponding to the position of the cursor, whenever the cursor is on any distance value in the display.


File Menu (in Distance Data Explorer)

The File menu consists of three commands:

•	Select & Edit Taxa/Groups:  It brings up a dialog box to categorize the taxa into groups.

•	Export/Print Distances:  It brings up a dialog box to write pairwise distances as a text file, with a choice of several formats.

•	Quit Viewer:  Closes the Distance Data Explorer.


Display Menu (in Distance Data Explorer)

The Display menu consists of four main commands:

•	Show Only Selected Taxa:  It is toggled to show a matrix of all or only selected taxa.

•	Sort Taxa:  It provides a submenu to sort the order of taxa in one of three ways: by input order, by taxon name or by group name.

•	Show Group Names:  It is a toggle for displaying or hiding the group name next to the name of each taxon, when available.

•	Change Font:  Choosing this command brings up the dialog box, which allows you to make choices regarding the type and size of the font used to display the distance values.


Average Menu (in Distance Data Explorer)

This menu is for computation of average values using the selected taxa.  The following averaging options are available.

Overall:  Simply computes and displays the overall average.

Within groups:  This menu item is enabled only if at least one group is defined.  For each group, an arithmetic average is computed for all valid pairwise comparison and results are displayed in the Distance Matrix Explorer.  All incalculable within group averages are shown with a "n/c" in red color.

Between groups:  This menu item is enabled only if at least two groups of taxa are defined.  For each between group average, an arithmetic average is computed for all valid inter-group pairwise comparisons and results are displayed in the Distance Matrix Explorer.  All incalculable within group averages are shown with a "n/c" in red color.

Net Between Groups:  This menu item is enabled only if at least two groups of taxa are defined.  To compute net average distances between groups of taxa.  The net average distance between two groups is given by

dA = dXY – (dX - dY)/2

where, dXY is the average distance between groups X and Y, and dX and dY are the mean within-group distances.   You must have at least two groups of taxa with a minimum of two taxa each for this option to work.  All incalculable within group averages are shown with a "n/c" in red color.


Distance Matrix Explorer

The Distance Matrix Explorer is used to display results from the pair-wise distance calculations.  It is an intelligent viewer with flexibility to alter display modes and functionalities for computing within group, among group, and overall averages.

This explorer consists of a number of regions as follows:

Menu Bar

File Menu

Display Menu

Average Menu

Help: This button brings up the help file.

Tool Bar

The tool bar provides quick access to a number of menu items.

•	General Utilities

•	Lower-left Triangle button:  Click this icon to display pairwise distances in the lower-left matrix.  If standard errors (or other statistics) are also displayed, then it will display them in the upper-right portion of the display.

•	Upper-right Triangle button:  Click this icon to display pairwise distances in the upper-right matrix.  If standard errors (or other statistics) are also displayed, then it will display them in the lower-left portion of the display.

•	(A,B):  This button is an on-off switch to write or hide the name of the taxa pair highlighted.  The taxa pair is displayed in the status bar below.

•	Distance Display Precision

•	 :  Decreases the precision of the distance display by one decimal place with each click of the button.

•	 :  Increases the precision of the distance display by one decimal place with each click of the button.

•	Column Sizer: It is a slider that can be used to increase or decrease the width of the columns that show the pairwise distances.

The 2-Dimensional Data Grid

This grid displays the pair-wise distances between taxa (or within groups etc.) in the form of a lower or upper triangular matrix.  The taxa names are the row-headers and the column headers are numbered from 1 to m, m being the number of taxa.  There is a column sizer button for the row-headers, so as to be able to increase/decrease the column size to accommodate the full name of the sequences/groups.

Fixed Row:  This is the first row in the data grid that displays the column number.

Fixed Column:  This is the first and the leftmost column in the data grid.  This column is always visible even if you scroll past the initial screen.  It contains taxa names, which have a check box associated with them.  When a taxon is included in the data set for analysis, this box is checked.  You can check/uncheck this check box to include/exclude taxa from analysis.  In this column, you can drag-and-drop taxa names to sort them in a desired manner.

Rest of the Grid:  Cells to the right of the first column and below the first row contain the nucleotides or amino acids of the input data.  Note that all cells are drawn in light color if they contain data corresponding to unselected sequences or genes/domains.

Status bar

On the left sub-panel, it shows the name of the statistic for the currently selected value.  In the next panel, the status bar shows the taxa-pair name for the selected value.


Average Menu (in Distance Matrix Explorer)

This menu is for computation of average values.  The following averaging options are available.

Overall:  Simply computes and displays the overall average.

Within groups:  This menu item is enabled only if at least one group is defined.  For each group, an arithmetic average is computed for all valid pairwise comparison and results are displayed in the Distance Matrix Explorer.  All incalculable within group averages are shown with a "n/c" in red color.

Between Groups:  This menu item is enabled only if at least two groups of taxa are defined.  For each between group average, an arithmetic average is computed for all valid inter-group pairwise comparisons and results are displayed in the Distance Matrix Explorer.  All incalculable within group averages are shown with a "n/c" in red color.

Net Between Groups:  This menu item is enabled only if at least two groups of taxa are defined.  To compute net average distances between groups of taxa.  The net average distance between two groups is given by

dA = dXY – (dX - dY)/2

where dXY is the average distance between groups X and Y, and dX and dY are the mean within-group distances.   You must have at least two groups of taxa with a minimum of two taxa each for this option to work.  All incalculable within group averages are shown with a "n/c" in red color.


Display Menu (in Distance Matrix Explorer)

The Display menu consists of four main commands:

•	Show Pair Name:  It is toggled to write or hide the name of the taxa pair highlighted.  The name of the taxa pair is displayed in the status bar below.

•	Sort Taxa:  It provides a submenu to sort the order of taxa in one of three ways: by input order, by taxon name or by group name.

•	Show Names:  It is a toggle for displaying or hiding the taxa name.

•	Show Group Names:  It is a toggle for displaying or hiding the group name next to the name of each taxon, when available.

•	Change Font:  Choosing this command brings up the dialog box, which allows you to make choices regarding the type and size of the font used to display the distance values.


File Menu (in Distance Matrix Explorer)

The File menu consists of three commands:

•	Show Input Data Title:  It displays the title of the input data.

•	Show Analysis Description:  It displays various options used for calculating the quantities displayed in the Matrix Explorer.

•	Export/Print Distances:  It brings up a dialog box to write pairwise distances as a text file, with a choice of several formats.

•	Quit Viewer:  Exits the Distance Data Explorer.


Distance Options Tab (Only Distance estimation)

This dialog box is occupied by a set of overlapping pages, with each pages marked by Tabs that run across the top of the dialog box.  You can go to any page by simply clicking on the Tab.  Each tab page organizes a set of logically related options.  Information from all the pages will be used in the requested analysis, so it is important that you examine the currently selected options in each tab before pressing OK to proceed with analysis.

Models of Substitution

In the top part, a model (method) for estimating evolutionary distance is selected by pressing the Models button.  It will reveal a menu containing many different distance methods.  The selected distance method is displayed next to the Models button in bold letters.

Below the Models button, you may need to specify the Transition/Transversion ratio (R) and/or the Gamma parameter depending on the distance method chosen.

Substitutions to Include

Depending on the distance method selected, the evolutionary distance can be teased apart into two or more components.  The Substitutions to Include list box allows you to select one of these components.  The list of components relevant to the chosen model for estimating distances is made available.

Compute

Use this to specify whether to compute Distances only or the Distances and Standard Errors.  If you select the latter option, then you are given a choice on how to compute it in the Standard Error Computation box.

Standard Error Computation Box

This box is visible only if you have chosen Distances and standard errors in the Compute box.  Depending on the type of distance computed, you may choose to use analytical formulas or bootstrap method to compute standard errors.  Whenever the standard errors are estimated by the bootstrap method, you will be prompted for the number of bootstrap replicates to use and a random number seed.


Include Sites Tab (Distance methods)

This tab contains options for specifying options for handling gaps/missing data in the analysis, inclusion and exclusion of codon positions, and whether to restrict the analysis to labeled sites only (if applicable).

Handling Gaps/Missing Data

You may choose to remove all sites containing alignment gaps and missing-information sites before the calculation begins (Complete-deletion option).  Alternatively, you may choose to retain all such sites initially, excluding them as necessary in the pairwise distance estimation (Pairwise-deletion option).

Codon Positions/Sites Included

In this box you may select any combination of 1st, 2nd, 3rd, and non-coding positions for analysis.  It is available only if the nucleotide sequences contain protein-coding regions and you have selected nucleotide-by-nucleotide analysis.

Include only Labeled Sites

This section is enabled only if you have associated labels with some or all sites in the data.  If you select to Include only labeled sites, then the labeled sites will be first extracted from the data.  Then all other options mentioned above will be enforced.  In MEGA, you can choose to exclude sites with certain labels.  To do so, uncheck the corresponding labels in the list displayed.  Remember, labels associated with all three positions in the codon must be included to include a full codon in the analysis.


Include Sites Tab (Parsimony Analysis)

It provides options for handling gaps/missing data in the analysis, specifying inclusion and exclusion of codon positions, and options to whether to restrict the analysis to some types of labeled sites only (if applicable).

Handling Gaps/Missing Data

You may choose to remove all sites containing alignment gaps and missing-information sites before the parsimony analysis begins (Complete-deletion option).  Alternatively, you may choose to retain all such sites.  In this case, all missing-information and alignment gap sites are treated as missing data in the calculation of tree length.

Codon Positions/Sites Included

In this box you may select any combination of 1st, 2nd, 3rd, and non-coding positions for analysis.  It is available if the nucleotide sequences contain protein-coding regions and you have selected nucleotide-by-nucleotide analysis (e.g., Nucleotide pairwise distances or parsimony analysis for nucleotide sequences).

Data To Analyze

If the dataset contains protein-coding regions, then this group of options is enabled.  It allows you to choose between analysis of nucleotide sequences or translated protein sequences.  If the latter is chosen, MEGA will translate all protein-coding regions into amino acid sequences and conduct the protein sequence parsimony analysis.

Include only Labeled Sites

This section is enabled only if you have associated labels with some or all sites in the data.  If you select to Include only labeled sites, then the labeled sites will be first extracted from the data.  Then all other options mentioned above will be enforced.  In MEGA, you can choose to exclude sites with certain labels.  To do so, uncheck the corresponding labels in the list displayed.  Remember, labels associated with all three positions in the codon must be included to include a full codon in the analysis.


Test of Phylogeny Tab

Using this page you can select method to use for assessing the reliability of a phylogenetic tree.

Number of Replications and Random Seed

All the tests of phylogeny included in MEGA use the bootstrap re-sampling strategy.  Therefore, you need to provide the number of replicates and a starting random seed for the chosen test.


ME Tree Options

Use this page to set the extensiveness of the heuristic search for the Minimum Evolution (ME) tree.  MEGA employs Close-Neighbor-Interchange (CNI) algorithm to find the ME tree.  It is a branch swapping method, which begins with a given initial tree.  You can choose to automatically construct an NJ tree to use for this purpose.  Alternatively, you can provide a specific topology.  If you choose the latter option, then the Select User Tree page becomes available, where you can select a desired topology.


MP Tree Search Options

Use this page to select between the branch-and-bound and the heuristic (close-neighbor interchange) searches.  For the branch and bound search, an optimized Max-mini branch-and-bound algorithm is used.  While this algorithm guarantees to find all the MP trees, a branch-and-bound search is often too time consuming for more than 15 sequences (although this number varies from data set to data set).  Alternatively, you may use the heuristic search (Close-Neighbor-Interchange).   It is a branch swapping method, which begins with a given initial tree.  You may automatically obtain a set of initial trees by using the Min-mini algorithm with a given search factor.  Alternatively, you can provide your own topology for the purpose or use the random addition option to produce the initial trees.


Large Sample Tests of Selection

One way to test whether positive selection is operating on a gene is to compare the relative abundance of synonymous and nonsynonymous substitutions that have occurred in the gene sequences.  For a pair of sequences, this is done by first estimating the number of syonymous substitutions per synonymous site (dS) and the number of nonsynonymous substitutions per nonsynonymous site (dN), and their variances: Var(dS) and Var(dN), respectively.  With this information, we can test the null hypothesis that H0: dN = dS using a Z-test:

Z = (dN - dS) / SQRT(Var(dS) + Var(dN))

The level of significance at which the null hypothesis is rejected depends on the alternative hypothesis (H1) .

H0:	 dN = dS

H1: 	(a)	dN ≠ dS	(test of neutrality).

(b)	dN > dS	(positive selection).

(c)	dN < dS	(purifying selection).

For alternative hypotheses (b) and (c), we use a one-tailed test and for (a) we use a two-tailed test.  These three tests can be conducted directly for pairs of sequences, overall sequences, or for within groups of sequences.  For testing for selection in pairwise manner, you can compute the variance of (dN - dS) by using either the analytical formulas or the bootstrap resampling method.

For data sets containing more than two sequences, you can compute the average number of synonymous substitutions and the average number of nonsynonymous substitutions to conduct a Z-test in manner similar to the one mentioned above.  The variance of the difference between these two quantities is estimated by the bootstrap method (Nei and Kumar [2000], page 56).


Analysis Preferences (Maximum Parsimony)

This dialog box contains four overlapping pages, with each pages marked by Tabs that run across the top of the dialog box.  You can go to any page by simply clicking on the Tab.  Each tab page organizes a set of logically related options.  Information from all the pages will be used in the requested analysis, so it is important that you examine the currently selected options in each tab before pressing OK to proceed with analysis.

Page	Description
Include Sites	Use to specify handling of missing data and alignment gaps, inclusion/exclusion of codon positions (if appropriate) and labeled sites (if available).  If the currently active data contains protein-coding regions, you can choose to conduct parsimony analysis on protein sequences (MEGA will automatically translate appropriate regions) or nucleotide sequences.
MP Tree Search Options	Use to select a branch-and-bound or heuristic search method for finding the maximum parsimony tree(s).
Scoring Changes	Used to specify relative weights for different types of changes to use in calculation of tree length.
Test of Phylogeny	Use to specify whether or not to test the reliability of the inferred phylogenetic tree.  Only bootstrap method is available for testing the reliability of the parsimony trees.


Analysis Preferences (Distance Computation)

This dialog box contains two overlapping pages, with each page marked by Tabs that run across the top of the dialog box.  You can go to any page by simply clicking on the Tab.  Each tab page organizes a set of logically related options.  Information from all the pages will be used in the requested analysis, so it is important that you examine the currently selected options in each tab before pressing OK to proceed with analysis.

Tab page	Description
Distance Options	Use to specify the model for distance estimation and the types of substitutions to include in the pairwise distance calculation.
Include Sites	Use to specify handling of missing data and alignment gaps, inclusion/exclusion of codon positions (if appropriate) and labeled sites (if available).


Analysis Preferences (NJ/UPGMA)

This dialog box contains three overlapping pages, with each pages marked by Tabs that run across the top of the dialog box.  You can go to any page by simply clicking on the Tab.  Each tab page organizes a set of logically related options.  Information from all the pages will be used in the requested analysis, so it is important that you examine the currently selected options in each tab before pressing OK to proceed with analysis.

Tab page	Description
Distance Options	Use to specify the model for distance estimation and the types of substitutions to include in the pairwise distance calculation.
Include Sites	Use to specify handling of missing data and alignment gaps, inclusion/exclusion of codon positions (if appropriate) and labeled sites (if available).
Test of Phylogeny	Use to specify whether or not to test the reliability of the inferred phylogenetic tree.  Tests not available are grayed out.


Analysis Preferences (Minimum Evolution)

This dialog box contains four overlapping pages, with each pages marked by Tabs that run across the top of the dialog box.  You can go to any page by simply clicking on the Tab.  Each tab page organizes a set of logically related options.  Information from all the pages will be used in the requested analysis, so it is important that you examine the currently selected options in each tab before pressing OK to proceed with analysis.

Tab page	Description
Distance Options	Use to specify the model for distance estimation and the types of substitutions to include in the pairwise distance calculation.
Include Sites	Use to specify handling of missing data and alignment gaps, inclusion/exclusion of codon positions (if appropriate) and labeled sites (if available).
ME Tree Options	Use to select attributes of the heuristic search for finding the minimum evolution tree (s).
Test of Phylogeny	Use to specify whether or not to test the reliability of the inferred phylogenetic tree.  Tests not available are shown in gray.


Analysis Preferences (Fisher's Exact Test)

When the numbers of codons in the data or the total numbers of synonymous and/or nonsynoymous substitutions are small, the Z-test is known to be too liberal in rejecting the null hypothesis.  In these cases, tests of selection can be conducted to examine the null hypothesis of the neutral evolution.  Only Nei-Gojobori and Modified Nei-Gojobori methods can be used for this test because it requires the direct computation of the numbers of synonymous and nonsynonymous differences, and the number of synonymous and nonsynonymous sites.  It should only be used when sequences show only a small number of differences.

To conduct Fisher’s Exact Test for positive selection, you need to specify two sets of options, which are presented in two overlapping Tabs in this dialog box.

Tab page	Description
Distance Options	Use it to specify whether you want to use the original or modified Nei & Gojobori methods.  The latter allows you to take into account the transition/transversions bias.
Include Sites	Use to specify handling of missing data and alignment gaps, inclusion/exclusion of codon positions (if appropriate) and labeled sites (if available).


Analysis Preferences (Z-test of Selection)

Options for this test are organized in three overlapping pages, with each pages marked by Tabs that run across the top of the dialog box.  You can go to any page by simply clicking on the Tab.  Information from all the pages will be used in the requested analysis, so it is important that you examine the currently selected options in each tab before pressing OK to proceed with analysis.

Tab page	Description
Distance Options	Use to specify the model for estimating synonymous and nonsynonymous distances between pairs of sequences.
Test Selection	Use to specify the hypothesis to test, method to use for variance computation, and whether to conduct analysis for sequence pairs, overall average, or within groups.
Include Sites	Use to specify handling of missing data and alignment gaps, inclusion/exclusion of codon positions (if appropriate) and labeled sites (if available).


Distance Options Tab (Phylogenetic reconstruction)

This page provides options for selecting a model of substitution (nucleotide, amino acid, or codon) and associated options.

Models of Substitution

In the top part, a model (method) for estimating evolutionary distance is selected by pressing the Models button.  It will reveal a menu containing many different distance methods.  The selected distance method is displayed next to the Models button in bold letters.

Below the Model button, you may need to specify the Transition/Transversion (R) ratio and/or the Gamma parameter depending on the distance method chosen.  MEGA will automatically enable the required option.

Substitutions to Include

Depending on the distance method selected, the evolutionary distance can be teased apart into two or more components.  The Substitutions to Include list box allows you to select one of these components.  The list of various components associated with different models used for estimating distances is given along with the description for the model.


Score Changes Tab (Maximum Parsimony)

By default, all nucleotide (or amino acid) changes are weighted equally in MEGA (standard parsimony).  However, for nucleotide sequences, you have the option of conducting the transversion parsimony analysis.  In this case, only transversional changes are considered in calculating the tree length.


Disparity_Index_Analysis_Options

This dialog box contains two overlapping pages, with each pages marked by Tabs that run across the top of the dialog box.  You can go to any page by simply clicking on the Tab.  Each tab page organizes a set of logically related options.  Information from all the pages will be used in the requested analysis, so it is important that you examine the currently selected options in each tab before pressing OK to proceed with analysis.

Tab page	Description
Include Sites	Use to specify handling of missing data and alignment gaps, inclusion/exclusion of codon positions (if appropriate) and labeled sites (if available).
Disparity Index Test	Use to specify whether to compute Composition Distance, Disparity Index, or Test homogeneity of evolutionary patterns.


Include Sites Tab (Disparity Index Analysis)

It provides options for handling gaps/missing data in the analysis, specifying inclusion and exclusion of codon positions, Protein sequence translation, and options to whether to restrict the analysis to some types of labeled sites only (if applicable).

Handling Gaps/Missing Data

You may choose to remove all sites containing alignment gaps and missing-information sites before the parsimony analysis begins (Complete-deletion option).  Alternatively, you may choose to retain all such sites.  In this case, all missing-information and alignment gap sites are treated as missing data in the calculation of tree length.

Codon Positions/Sites Included

In this box you may select any combination of 1st, 2nd, 3rd, and non-coding positions for analysis.  It is available if the nucleotide sequences contain protein-coding regions and you have selected nucleotide-by-nucleotide analysis (e.g., Nucleotide pairwise distances or parsimony analysis for nucleotide sequences).

Data To Analyze

If the dataset contains protein-coding regions, then this group of options is enabled.  It allows you to choose between analysis of nucleotide sequences or translated protein sequences.  If the latter is chosen, MEGA will translate all protein-coding regions into amino acid sequences and conduct the protein sequence parsimony analysis.

Include only Labeled Sites

This section is enabled only if you have associated labels with some or all sites in the data.  If you select to Include only labeled sites, then the labeled sites will be first extracted from the data.  Then all other options mentioned above will be enforced.  In MEGA, you can choose to exclude sites with certain labels.  To do so, uncheck the corresponding labels in the list displayed.  Remember, labels associated with all three positions in the codon must be included to include a full codon in the analysis.


Disparity Index Test Tab

Using this page you can select whether to compute Composition Distance, Disparity Index, or test the Homogeneity of substitution Pattern (Kumar and Gadagkar 2001).  If the test is selected, MEGA will conduct the Monte-Carlo analysis, for which you need to provide the number of replicates and a starting random seed for the chosen test.


Distance Methods Menu

Under this tab you will see the Models button with a drop down menu next to it.  The name of the currently selected model is shown next to the button.  You can select a desired model using the drop-down menu associated with the Models button by click on the button.  This reveals four options: Nucleotide, Nucleotide Gamma, Syn/Nonsynonymous and Amino acid.  Each of these options provides a list of distance methods that can be selected.


Models for estimating distances

Evolutionary distance between a pair of sequences is usually measured by the number of nucleotide (or amino acid) substitutions between them. Evolutionary distances are fundamental for the study of molecular evolution and are useful for phylogenetic reconstruction and estimation of divergence times. In MEGA, most of the widely used methods for distance estimation for nucleotide and amino acid sequences are included. In the following, a brief discussion of these methods is presented in three sections: nucleotide substitutions, synonymous-nonsynonymous substitutions, and amino acid substitutions. Further details of these methods and general guidelines for the use of these methods are given in Nei and Kumar (2000).  Note that in addition to the distance estimates, MEGA2 also computes the standard errors of the estimates using the analytical formulas and the bootstrap method.

Category	Description
Nucleotide	Sequences are compared nucleotide-by-nucleotide.  These distances can be computed for protein coding and non-coding nucleotide sequences.
Synonymous & nonsynonymous	Sequences are compared codon-by-codon.  These distances can only be computed for protein-coding sequences or domains.
Amino acid	Amino acid sequences are compared residue-by-residue.  These distances can be computed for protein sequences and protein-coding nucleotide sequences.  In the latter case, protein-coding nucleotide sequences are automatically translated using the selected genetic code table.

Distance methods included in MEGA are as follows:

Nucleotide

	No. of differences
	p-distance
	Jukes-Cantor distance ( with Gamma model)
	Tajima-Nei distance
	Kimura 2-parameter distance (with Gamma model)
	Tamura 3-parameter distance
	Tamura-Nei distance (with Gamma model)
Syn-Nonsynonymous

	Nei-Gojobori Method
	Modified Nei-Gojobori Method
	Li-Wu-Luo Method
	Pamilo-Bianchi-Li Method
	Kumar Method
Amino acid

	No. of differences
	p-distance
	Poisson correction
	Gamma distance


No. of differences (Nucleotide)

This distance is simply the number of sites at which the two sequences compared are different.  If you are using pairwise deletion option for handling gaps and missing data, it is important to realize that this count does not normalize the number of differences based on the number of valid sites compared (if the sequences contain alignment gaps).  Therefore, we recommend that you use the complete-deletion option option if you use this distance.

For this distance, MEGA2 provides facilities to compute the following quantities:

Quantity	Description
D: Transitions + Transversions	Number of nucleotide sites different.
S: Transitions only	Number of nucleotide sites with transitional differences.
v: Transversions only	Number of nucleotide sites with transversional differences.
R = s/v	Transition/transversions ratio.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows.

Quantity	Formula	Variance
	-
s	-
v	-
R
where [image] and

P and Q are the proportion of sites showing transitional and transversional differences, respectively.

See also Nei and Kumar (2000), page 33.


p-distance (Nucleotide)

This distance is the proportion (p) of nucleotide sites at which the two sequences compared are different. It is obtained by dividing the number of nucleotide differences by the total number of nucleotides compared.  It does not make any correction for multiple substitutions at the same site, substitutional rate biases (for example, differences in the transitional and transversional rates), or differences in evolutionary rates among sites.

MEGA2 provides facilities to compute the following p-distances and related quantities:

Quantity	Description
D: Transitions + Transversions	Proportion of nucleotide sites different.
S: Transitions only	Proportion of nucleotide sites with transitional differences.
v: Transversions only	Proportion of nucleotide sites with transversional differences.
R = s/v	Transition/transversions ratio.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
s
v
R
where [image] and

P and Q are the proportion of sites showing transitional and transversional differences, respectively.

See also Nei and Kumar (2000), page 33.


Jukes-Cantor distance

This method (Jukes and Cantor 1969) was developed under the assumption that the rate of nucleotide substitution is the same for all pairs of the four nucleotides A, T, C, and G.  It gives a maximum likelihood estimate of the number of nucleotide substitutions between two sequences.  It assumes equality of substitution rates among sites (see the related gamma distance), equal nucleotide frequencies, and it does not correct for higher rate of transitional substitutions as compared to transversional substitutions.

MEGA2 provides facilities to compute the following quantities:

Quantity	Description
d: Transitions + Transversions	Number of nucleotide substitutions per site.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
p is the proportion of sites with different nucleotides.

See also Nei and Kumar (2000), page 36.


Jukes-Cantor Gamma distance

The Jukes-Cantor Gamma distance is the same as the Jukes-Cantor distance, except that it does not assume that all sites are evolving at the same rate.  This aspect is modeled using the Gamma distribution, and you will need to provide a gamma parameter (a) for computing this distance.

MEGA2 provides facilities to compute the following p-distances and related quantities:

Quantity	Description
d: Transitions + Transversions	Number of nucleotide substitutions per site.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
p is the proportion of sites with different nucleotides.

See also Nei and Kumar (2000), page 36 and estimating gamma parameter.


Tajima-Nei distance

In the real data, nucleotide frequencies often deviate substantially from 0.25. In this case the Tajima-Nei distance (Tajima and Nei 1984) gives a better estimate of the number of nucleotide substitutions than the Jukes-Cantor distance. Note that it assumes equality of substitution rates among sites and between transitionalal and transversional substitutions.

MEGA2 provides facilities to compute the following quantities for this method:

Quantity	Description
d: distance	Number of nucleotide substitutions per site.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
Here, [image] and

where [image] is the relative frequency of the nucleotide pair i and j,  gi’s are the nucleotide frequencies.

See also Nei and Kumar (2000), page 38.


Kimura 2-parameter distance

Kimura’s two parameter model (1980) corrects for multiple hits taking into account transitional and transversional substitution rates, while assuming that the four nucleotide frequencies are the same and that rates of substitution does not vary among sites (see related Gamma distance).

MEGA2 provides facilities to compute the following quantities:

Quantity	Description
d: Transitions + Transversions	Number of nucleotide substitutions per site.
s: Transitions only	Number of transitional substitutions per site.
v: Transversions only	Number of transversional substitutions per site.
R = s/v	Transition/transversions ratio.
L: No of valid common sites	Number of sites compared.
Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
d
s
v
R

where P and Q are the frequencies of sites with transitional and transversional differences, respectively.

See also Nei and Kumar (2000), page 37.


Kimura gamma distance

Kimura’s two-parameter Gamma model corrects for multiple hits taking into account transitional and transversional substitution rates and differences in substitution rates among sites.  Evolutionary rates among sites are modeled using the Gamma distribution, and you will need to provide a gamma parameter for computing this distance.

MEGA2 provides facilities to compute the following quantities:

Quantity	Description
d: Transitions + Transversions	Number of nucleotide substitutions per site.
s: Transitions only	Number of transitional substitutions per site.
v: Transversions only	Number of transversional substitutions per site.
R = s/v	Transition/transversions ratio.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
d
s
v
R

where P and Q are the respective total frequencies of transition type pairs and transversion type pairs.

See also Nei and Kumar (2000), page 44 and estimating gamma parameter.


Tamura 3-parameter distance

Tamura’s 3-parameter model corrects for multiple hits taking into account the differences in transitional and transversional rates and the G+C-content bias (1992).  It assumes equality of substitution rates among sites.

MEGA2 provides facilities to compute the following quantities:

Quantity	Description
d: Transitions & Transversions	Number of nucleotide substitutions per site.
s: Transitions only	Number of transitional substitutions per site.
v: Transversions only	Number of transversional substitutions per site.
R = s/v	Transition/transversions ratio.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
d
s
v
R

where P and Q are the proportion of sites with transitional and transversional differences, respectively, and

 is the GC content.

See also Nei and Kumar (2000), page 39.


Tamura-Nei distance

The Tamura-Nei model (1993) corrects for multiple hits taking into account substitutional rate differences between nucleotides and inequality of nucleotide frequencies.  It distinguishes between transitionalal substitution rates between purines and transversional substitution rates between pyrimidines.  It assumes equality of substitution rates among sites (see related gamma model).

MEGA2 provides facilities to compute the following quantities for this method:

Quantity	Description
d: Transitions & Transversions	Number of nucleotide substitutions per site.
s: Transitions only	Number of transitional substitutions per site.
v: Transversions only	Number of transversional substitutions per site.
R = s/v	Transition/transversions ratio.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
d
s
v
R
where P1 and P2 are the proportions of transitional differences between nucleotides A and G, and between T and C, respectively, Q is the proportion of transversional differences, and [image] are the respective frequencies of A, C, G and T.

See also Nei and Kumar (2000), page 40.


Tamura-Nei gamma distance

Tamura-Nei’s (1993) distance with Gamma model corrects for multiple hits taking into account the substitutional rate differences between nucleotides and inequality of nucleotide frequencies.  In this distance, evolutionary rates among sites are modeled using the Gamma distribution.  You will need to provide a gamma parameter for computing this distance.

MEGA2 provides facilities to compute the following quantities for this method:

Quantity	Description
d: Transitions & Transversions	Number of nucleotide substitutions per site.
s: Transitions only	Number of transitional substitutions per site.
v: Transversions only	Number of transversional substitutions per site.
R = s/v	Transition/transversions ratio.
L: No of valid common sites	Number of sites compared.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
d
s
v
R

where P1 and P2 are the proportions of transitional differences between nucleotides A and G, and between T and C, respectively, Q is the proportion of transversional differences, and [image] are the respective frequencies of A, C, G and T.

See also Nei and Kumar (2000), page 45 and and estimating gamma parameter.


Nei-Gojobori Method

This is a method for computing the numbers of synonymous and nonsynonymous substitutions and the numbers of potentially synonymous and potentially nonsynonymous sites (Nei and Gojobori 1986).  Based on these estimates, MEGA can be asked to produce the following quantities.

Number of differences (Sd or Nd)

These are simple counts of the number of synonymous (Sd) and nonsynonymous (Nd) differences.  It is important to note that to compare these two numbers, you must use p-distance because the number of potential synonymous sites is much smaller than the number of nonsynonymous sites.

p-distance (pS or pN)

The count of the number of synonymous differences (Sd) is normalized using the number of potential synonymous sites (S).  A similar computation can be made for the nonsynonymous differences also.

 	Jukes-Cantor correction (dS or dN)

The p-distances computed above can be corrected to account for the multiple substitutions at the same site.

Difference between synonymous and nonsynonymous distances

MEGA2 can also compute differences between the synonymous and nonsynonymous distances.  These statistics are useful in conducting tests for selection.

Number of Sites (S or N)

The number of potentially synonymous and the number of potentially nonsynonymous sites can be computed using this option.  For each pair of sequence, average number of synonymous or nonsynonymous sites is reported.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
Dp
Dd
See also Nei and Kumar (2000), page ????.


Modified Nei-Gojobori Method

The modified Nei-Gojobori distance differs from the original Nei-Gojobori formulation in one way: the transitional and transversional substitutions are no longer assumed to be occurring with the same frequency.  The user is requested to provide the Transition/Transversion (R) ratio.  When R = 0.5, this method becomes identical to the Nei-Gojobori method.  When R > 0.5, the number of synonymous sites will be less than that estimated using Nei-Gojobori method.  Consequently, the number of nonsynonymous sites will be larger than that estimated in the original Nei-Gojobori (Nei and Gojobori 1986) approach.

Number of differences (Sd or Nd)

These are simple counts of the number of synonymous (Sd) and nonsynonymous (Nd) differences.  It is important to note that to compare these two numbers, you must use p-distance because the number of potential synonymous sites is much smaller than the number of nonsynonymous sites.

p-distance (pS or pN)

The count of the number of synonymous differences (Sd) is normalized using the number of potential synonymous sites (S).  A similar computation can be made for the nonsynonymous differences also.

 	Jukes-Cantor correction (dS or dN)

The p-distances computed above can be corrected to account for the multiple substitutions at the same site.

Difference between synonymous and nonsynonymous distances

MEGA2 can also compute differences between the synonymous and nonsynonymous distances.  These statistics are useful in conducting tests for selection.

Number of Sites (S or N)

The number of potentially synonymous and the number of potentially nonsynonymous sites can be computed using this option.  For each pair of sequence, average number of synonymous or nonsynonymous sites is reported.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
D

See also Nei and Kumar (2000), page 52.


Li-Wu-Luo Method

In this method (Li et al 1985), each site in a codon is allocated to 0-fold, 2-fold or 4-fold degenerate categories.  For computing distances, all 0-fold and two-thirds of the 2-fold sites are considered nonsynonymous sites, whereas one-third of the 2-fold and all of the 4-fold sites are considered synonymous sites.  The observed transitional and transversional differences between codons are then partitioned into those occurring at 0-fold, 2-fold and 4-fold degenerate sites.  Based on this information, the following quantities can be estimated.

Synonymous distance

It is the number of synonymous substitutions per synonymous site.

Nonsynonymous distance

It is the number of nonsynonymous substitutions per nonsynonymous site.

 	Substitutions at the 4-fold degenerate sites

It is the number of substitutions per 4-fold degenerate site.  It is useful for measuring the rate of neutral evolution.

Substitutions at the 0-fold degenerate sites

It is the number of substitutions per 0-fold degenerate site.  It is useful for measuring the rate of amino acid sequence evolution.

Number of 4-fold degenerate sites

The estimate of the number of 4-fold degenerate sites is computed by averaging the number of 4-fold degenerate sites in the two sequences compared.

Number of 0-fold degenerate sites

The estimate of the number of 0-fold degenerate sites is computed by averaging the number of 0-fold degenerate sites in the two sequences compared.

Difference between synonymous and nonsynonymous distances

It computes the differences between the synonymous and nonsynonymous distances.  These statistics are useful for conducting tests of selection.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
D

Here,

 are the number of 0-fold, 2-fold and 4-fold degenerate sites, respectively.

,   and

, where

,  [image], [image],

Pi and Qi are the proportions of i-fold degenerate sites that show transitional and transversional differences, respectively.

,

See also Nei and Kumar (2000), page 62.


Pamilo-Bianchi-Li Method

This method (Pamilo and Bianchi 1993; Li 1993 ) is a modification of Li, Wu and Luo's method. The only difference concerns the allocation of 2-fold sites to synonymous and nonsynonymous categories. Rather than assuming an equal transition and transversion rate, the rate is inferred from the observed number of transitions and transversions at the 4-fold degenerate sites. Based on this information, the following quantities can be estimated.

Synonymous distance

It is the number of synonymous substitutions per synonymous site.

Nonsynonymous distance

It is the number of nonsynonymous substitutions per nonsynonymous site.

 	Substitutions at the 4-fold degenerate sites (d4)

It is the number of substitutions per 4-fold degenerate site.  It is useful for measuring the rate of neutral evolution.

Substitutions at the 0-fold degenerate sites (d0)

It is the number of substitutions per 0-fold degenerate site.  It is useful for measuring the rate of amino acid sequence evolution.

Number of 4-fold degenerate sites(L4)

The estimate of the number of 4-fold degenerate sites is computed by averaging the number of 4-fold degenerate sites in the two sequences compared.

Number of 0-fold degenerate sites (L0)

The estimate of the number of 0-fold degenerate sites is computed by averaging the number of 0-fold degenerate sites in the two sequences compared.

Difference between synonymous and nonsynonymous distances (D)

It computes the differences between the synonymous and nonsynonymous distances.  These statistics are useful for conducting tests of selection.

Formulas for computing these quantities are as follows:

Quantity	Formula	Variance
d4
d0
D
Ai
Bi

Here,

 are the number of 0-fold, 2-fold and 4-fold degenerate sites, respectively.

,   and

, where

,  [image], [image],

Pi and Qi are the proportions of i-fold degenerate sites that show transitional and transversional differences, respectively.

,

See also Nei and Kumar (2000), page 64.


Kumar Method

This method is a modification of the Pamilo-Bianchi-Li method and Comeron’s (1995) methods to be able to handle some problematic degeneracy class assignments (see a detailed description below).  MEGA computes the following quantities.

Synonymous distance

It is the number of synonymous substitutions per synonymous site.

Nonsynonymous distance

It is the number of nonsynonymous substitutions per nonsynonymous site.

 	Substitutions at the 4-fold degenerate sites

It is the number of substitutions per 4-fold degenerate site.  It is useful for measuring the rate of neutral evolution.

Substitutions at the 0-fold degenerate sites

It is the number of substitutions per 0-fold degenerate site.  It is useful for measuring the rate of amino acid sequence evolution.

Number of 4-fold degenerate sites

The estimate of the number of 4-fold degenerate sites is computed by averaging the number of 4-fold degenerate sites in the two sequences compared.

Number of 0-fold degenerate sites

The estimate of the number of 0-fold degenerate sites is computed by averaging the number of 0-fold degenerate sites in the two sequences compared.

Difference between synonymous and nonsynonymous distances

It computes the differences between the synonymous and nonsynonymous distances.  These statistics are useful for conducting tests of selection.

Kumar’s modification of the PBL method:

The treatment of arginine and isoleucine codons in the Li-Wu-Luo method as well as the Pamilo-Bianchi-Li method is arbitrary.  This sometimes creates a problem because the arginine codons are quite frequent.  Comeron (1995) addressed this problem by dividing 2-fold degenerate sites into two groups: 2S-fold and 2V-fold degenerate sites.  The former refer to sites where the transitional change is synonymous and the two transversional changes are nonsynonymous, whereas the latter represent sites where the transitional change is nonsynonymous and the transversional changes are synonymous.  Although these definitions do help in correcting some of the inaccurate classifications of synonymous and nonsynonymous sites (e.g., methionine codons), it did not solve the problem completely.  For example consider mutations in the first nucleotide position of arginine codon CGG produces TGG (Trp), AGG (Arg), or GGG (Gly).  The transitional change (C to T) results in a nonsynonymous change.  Of the two transversional substitutions, one (C to A) results in a synonymous change, while the other (C to G) results in a nonsynonymous change.  Therefore, this nucleotide site is neither a 2S-fold nor a 2V-fold site.  Thus, the first position of three arginine codons (CGU, CGC, and CGA) and the third position of two isoleucine codons (ATT and ATC) cannot be assigned to any of the Comeron (1995) categories.  For this reason, Comeron (personal communication) used a more complicated classification of codons when he wrote his computer program. For example, the first position of arginine codon CGG was assigned to a 2V-fold site with a probability of 1/3 and to a 0-fold site with a probability of 2/3.  Similar assignments are used by W.-H. Li (personal computer) in his computer program.

Since details of the above nucleotide site assignment are quite arbitrary and may not apply for all known genetic code tables, Kumar developed another method that allows us to use PBL method for any genetic code table.  In this version nucleotide sites are first classified into the 0-fold, 2-fold, and 4-fold degenerate sites.  The 2-fold degenerate sites are further subdivided into simple 2-fold and complex 2-fold degenerate sites.  Simple 2-fold sites are those at which the transitional change results in a synonymous substitution and the two transversional changes in nonsynonymous substitutions.  All other 2-fold sites, including those for the three isoleucine codons, belong to the complex 2-fold site category.  If we use this definition, all nucleotide sites can be classified into the five groups as given in the following Table.

Table.

Degeneracy ->	0-fold	Simple 2-fold	Complex 2-fold	4-fold
No. of sites ->	L0	L2S	L2C	L4
			Syn	Nonsyn
Transition (s)	s0	s2	s2S	s2N	s4
Transversion (v)	v0	V2	v2S	v2N	v4
Here, L0, L2S, L2C, and L4 are the numbers of 0-fold, simple 2-fold, complex 2-fold, and 4-fold degenerate sites, respectively.

Once this table is filled using the observed counts for a given pair of sequences, we compute the proportions of transitional (Pi) and transversional (Qi) differences for the i-fold degenerate site in the following way.

From these quantities, we compute the Ai and Bi as in the PBL method.   Then using L2 = L2C + L2S, we apply the formulas for the PBL method

See also Nei and Kumar (2000), page 64.


No. of differences (Amino acids)

This distance is simply the number of sites at which the two sequences compared are different.  If you are using pairwise deletion option for handling gaps and missing data, it is important to realize that this count does not normalize the number of differences based on the number of valid sites compared (if the sequences contain alignment gaps).  Therefore, we recommend that you use the complete-deletion option option if you use this distance.

MEGA computes the following quantities:

Quantity	Description
d: distance	Number of sites different.
L: No of valid common sites	Number of sites compared.

Formulas used are:

Quantity	Formula	Variance
	None

See also Nei and Kumar (2000), page 18.


p-distance (Amino acids)

This distance is the proportion (p) of amino acid sites at which the two sequences compared are different.  It is obtained by dividing the number of amino acid differences by the total number of sites compared.  It does not make any correction for multiple substitutions at the same site or differences in evolutionary rates among sites.

MEGA provides facilities to compute the following quantities:

Quantity	Description
d: distance	Proportion of amino acid sites different.
L: No of valid common sites	Number of sites compared.

Formulas used are:

Quantity	Formula	Variance
where [image] is the number of amino acids different between two aligned sequences.

See also Nei and Kumar (2000), page 18.


Poisson Correction (PC) distance

The Poisson correction distance corrects for multiple substitutions at the same site, but it assumes equality of substitution rates among sites and assumes equal amino acid frequencies.

MEGA provides facilities to compute the following quantities:

Quantity	Description
d: distance	Number of amino acid substitutions per site.
L: No of valid common sites	Number of sites compared.

Formulas used are:

Quantity	Formula	Variance

See also Nei and Kumar (2000), page 20.


Gamma distance (Amino acids)

The Gamma distance improves upon the Poisson correction distance by taking care of the inequality of the substitution rates among sites.  For this purpose, you will need to provide the gamma shape parameter (a).

For estimating the Dayhoff distance, use a = 2.25 (see Nei and Kumar [2000], page 21 for details).

For computing Grishin’s distance, use a = 0.65. 23 (see Nei and Kumar [2000], page 23 for details)

MEGA provides facilities to compute the following quantities:

Quantity	Description
d: distance	Number of amino acid substitutions per site.
L: No of valid common sites	Number of sites compared.

Formulas used are:

Quantity	Formula	Variance

See also Nei and Kumar (2000), page 23 and estimating gamma parameter.


Bootstrap method to compute standard error of distance estimates

When you choose the bootstrap method for estimating the standard error, you need to specify the number of replicates and the seed for the psuedorandom number generator.  In each bootstrap replicate, the desired quantity will be estimated and then the standard deviation of the original values will be computed (see Nei and Kumar [2000], page 25 for details).

It is possible that in some bootstrap replicates the quantity you desired is not calculable due to statistical or technical reasons.  In this case, MEGA will discard the results of those bootstrap replicates and the final estimates will be based on the results of all the valid replicates.  This means that the actual number of bootstrap replicates used can be smaller than the number specified by the user.  However, if the number of valid bootstrap replicates is less than 25, then MEGA will report that the standard error cannot be computed (a n/c sign will appear in the result window).


Psuedorandom number generator

MEGA uses a pseudorandom number generator with an essentially infinite series length (see page 280, Press et al. 1993).


Computing the Gamma Parameter (a)

In the computation of gamma distances, it is necessary to know the gamma parameter (a).  This parameter may be estimated from the dataset under consideration or one may use the value obtained from previous studies.  For estimating a, a substantial number of sequences is necessary; if the number of sequences used is small, the estimate has a downward bias (Zhang and Gu 1998).  The current release of MEGA2 does not contain any programs for estimating a, however we plan to make them available starting with release 2.1.  Therefore you need to use other programs for estimating the a value.  Some of the frequently used programs that include this facility are PAUP* (Swofford 1998) for DNA sequences, PAML and PAMP programs for DNA and protein sequences (Yang 1999), and GAMMA programs from Gu and Zhang (1997).


Text File Editor and Format Converter

A text file editor is included in MEGA.  It is useful for creating and editing ASCII text files. Text File editor is also invoked automatically by MEGA if the input data file processing modules detect errors in the data file format.  In this case you should make appropriate changes and save the data file.

The text editor is straightforward to use if you are familiar with programs like Notepad. Click the mouse on the section you wish to change, type in the new text, or select text to cut, copy or paste. Only one font can be used in a document - this is the display font. You can have as many different text editor windows open at the same time as you like, and close them independently of each other. However, if you have a file open in the text editor, you should save the file and close the text editor window before trying to use that data file for analysis in MEGA. Otherwise, MEGA may not have the most up-to-date version of the data.

The Text File Editor and Format converter is a sophisticated tool with numerous special abilities that include:

•	Large files – it is able to operate on files of virtually unlimited size and line lengths.

•	General purpose – can be used to view/edit any ASCII text file.

•	Undo/ReDo – there is an unlimited depth of undo/redo options available

•	Search/Replace – you have the ability to search for arbitrary strings, and do block replacements on them.

•	Clipboard – supports familiar clipboard cut, copy, and paste operations.

•	Normal and Column blocks – regular contiguous line blocks and columnar blocks are supported.  This is quite useful while manually aligning sequences in the text editor.

•	Drag/Drop – you can move text around either with the familiar cut/paste operations or by simply selecting the text you want to move and then moving with the mouse.

•	Screenshots – you can create screen snapshots directly from the edit window for teaching and documentation purposes.

•	Printing – you can print the contents of the edit file.

The text editor contains a menu bar, a toolbar, and a status bar.

The Menu bar

Menu	Description
File menu	The File Menu contains the functions that are most commonly used to open, save, rename, print, and close files.  (Although there is no separate "rename" function available, you can rename a file by choosing the Save As… menu item and giving the file a different name before you save it.)
Edit menu	The Edit Menu contains functions that are commonly used to manipulate blocks of text.  Many of the edit menu items interact with the Windows Clipboard, which is a kind of hidden window that allows various selections to be copied and pasted across documents and even different applications.
Search menu	The Search Menu has several functions that allow you to perform searches and replacements of text strings.  You can also jump directly to a specific line number in the file.
Display menu	The Display Menu contains functions that affect the visual display of files in the edit windows.
Utilities menu	The Utilities Menu contains several functions that make this editor especially useful for working with files containing molecular sequence data (note that MEGA2 editor does not try to understand the data contained, it will simply operate on the text assuming that the user knows what (s)he is doing.
Toolbar

It contains shortcuts to some frequently used menu commands.

Status Bar

The Status bar is positioned at the bottom of the editor window. It shows the position of the cursor (line number and position in the line), whether the file has been edited, and the status of some keyboard keys (CAPS, NUM, and SCROLL lock).

Hotkeys and Shortcut keys

Many menu items have a hotkey and/or a shortcut key.  These are special key combinations that are helpful for people who are more comfortable using a keyboard than the mouse.  Hotkeys are identified by an underscore character in the menu item’s name, eg, "File", "New".  These allow you to hold down the Alt-key (usually found next to the space bar on the keyboard) then hit the underlined letter to produce the same action as if you clicked that name with the mouse.  We show this here using the notation <Alt>+key – eg., hitting the hotkey for the File menu item is shown as <Alt>+F.  This is just like holding the <Shift> key down to type a capital letter, only you’re using the <Alt> key instead of the <Shift> key – you want to be sure that you depress both keys together, holding the <Alt> key down a little bit longer than the letter key.  (Some people try hitting both keys simultaneously, as if they’re hitting two keys on a piano keyboard.  Quite often, this approach does not produce the desired results.  Simply hold the <Alt> key down, then hit the letter key, the release the <Alt> key.)

For instance, you could create a new file by clicking the mouse on the "File" menu item, then clicking on the "New" item beneath it.  Using hotkeys, you could type <Alt>+F followed by <Alt>+N.  Or, more simply, while you’re holding down the <Alt> key, hit the ‘F’ key followed by the ‘N’ key, then release the <Alt> key.

You might notice that several menu items, eg., the New Item on the File menu, show something to the right that looks like ‘Ctrl+N’.  This is called a Shortcut key sequence.  Where hotkeys can often require several keystrokes to get what you want, shortcut keys can do the same thing in just one keystroke.   Shortcut keys work the same as hotkeys, except you hold down the <Ctrl> key instead of the <Alt> key.   So, to create a new file for example, you can hold down the <Ctrl> key and hit the ‘N’ key, which is shown as <Ctrl>+N here.  (In the menus, this appears simply as ‘Ctrl+N’.)

Not all menu items have shortcut keys associated with them.  That’s because there can be a maximum of 26 shortcut keys, one for each letter of the alphabet.  Hotkeys, in contrast, are said to be localized to each menu and submenu.  For hotkeys to work, the menu item must be visible.  Shortcut keys work at any time.  For instance, you can be typing data into a text file and want to create a note for yourself in a new window.  You could simply hit the shortcut key sequence, <Ctrl>+N and a new window would open up.  You could type the note, hit <Ctrl>+S to save it, give it a file name, hit the enter key, then the <Alt>+F+C hotkey sequence to close the file (there is no shortcut key for closing a file).


New (in Text Editor)

File | New

Use this command to create a new file.


Save (in Text Editor)

File | Save

It allows you to save the file currently being edited.


Open (in Text Editor)

File | Open

Use this command to open an existing file in the text editor.


Save As (in Text Editor)

File | Save As

This command will save the current open file. The Save As dialog box will appear, allowing you to choose the directory to save to, the filename and extension, and type of file. To make the file suitable for loading as data in MEGA, you should save the file in MEGA format (it is a plain ASCII text file).  If another file with the same name already exists, it will be overwritten


Print (in Text Editor)

File | Print

This command will print the currently displayed file to the selected printer.


Exit Editor (in Text Editor)

File | Exit Editor

Closes the currently open file.  If the file was modified, but the modifications not yet saved, MEGA will ask whether or not to discard the changes made.  Note that you will exit the text editor only, not MEGA.


Undo (in Text Editor)

Edit | Undo

Choose this command to undo the most recent action you have committed.  Repeated use of this command will undo editing starting from the most recent to the oldest (Unlimited depth).


Cut (in Text Editor)

Edit | Cut

Choose this command to place a copy of the selected text on the Windows Clipboard.  It removes the original string. To paste the contents on the Clipboard you should use the Paste command.


Copy (in Text Editor)

Edit | Copy

Choose this command to place a copy of the selected text on the Windows Clipboard.  It leaves the original string untouched. To paste the contents on the Clipboard you should use the Paste command.


Paste (in Text Editor)

Edit | Paste

Choose this command to insert the most recently copied text present on the Windows Clipboard.


Font (in Text Editor)

Display | Set Font

Choose this command to change the display font used by the text editor. Note that an ASCII text file does not have a font attribute, it simply contains the text in the file.  Therefore the change in the font only affects the display, and is remembered by MEGA as your preferred display font for the Text Editor as long as you do not change it again.  A simple self-explanatory dialog box will be displayed to choose the desired font.


Find (in Text Editor)

Search | Find

Choose this command to display the Find Text dialog box.


Find Again (in Text Editor)

Search | Find Again

Choose this command to repeat the last Find command.


Replace (in Text Editor)

Search | Replace

It brings up a search and replace dialog box, which allows you to replace instances of a text string in the file currently being edited.


Reopen (in Text Editor)

File | Reopen

Choose this command to reopen a recently closed text file from a most-recently-used-files list. When you close a text file in the Text Editor, it is added to the Reopen list.


Close File (in Text Editor)

File | Close File

Close the current file.


Delete (in Text Editor)

Edit | Delete

Delete the selected (highlighted) text.  It is NOT copied to the clipboard.


Select All (in Text Editor)

Edit | Select All

This is used to select (highlight) EVERYTHING in the displayed file.


Go to Line (in Text Editor)

Edit | Go to Line #

This opens a small dialog box that allows you to enter a line# where you want to move to.


Show Line Numbers (in Text Editor)

Display | Show Line Numbers

This item can be checked (on) or un-checked (off) to show whether line numbers are displayed next to the lines or not.


Word Wrap (in Text Editor)

Display | Word Wrap

This item can be checked (on) or un-checked (off) to show whether lines in the edit window are automatically wrapped around based on the current window’s width or not.


Convert to Mega Format (in Text Editor)

Utilities | Convert to Mega Format

This item is used to convert the sequence data in the current edit window (or a selected file) into a MEGA file format.  It brings up a dialog box which allows you to choose the file and/or the format for this purpose.  MEGA2 will convert the data file and display the converted data in the editor.

Files written in a number of popular data formats can be converted into MEGA format.  MEGA2 supports conversion of CLUSTAL, NEXUS (PAUP, MacClade), PHYLIP, GCG, FASTA, PIR, NBRF, MSF, IG, and XML formats.  Details about how MEGA reads and converts these file formats are given in the section on: Importing Data from Other Fromats section.


Convert to MEGA Format Dialog Box

In this dialog box, you can select the file and the file format to convert to MEGA format.  Details about how MEGA reads and converts these file formats are given in the section on: Importing Data from Other Formats section.


Format Selected Sequence

Utilities | Convert to Mega Format

This submenu presents four other menu items that offer some common ways of reformatting text.

Merge Multiple Lines: This is used to merge several separate lines into one long (very wide) line

Remove Spaces/Digits: This is used to remove spaces and digits from a genetic sequence.

Insert Spaces Every 3: This is used to break the selected text into three-character chunks (e.g., codons).  Note that it does not remove any already existing spaces.

  Insert Spaces Every 10: This is used to break the selected text into ten-character chunks.


Reverse Complement

Utilities | Reverse Complement

This item reverses the order of characters in the selected block and then replaces each character by its complement.  Only A, T, U, C, and G are complemented, and the rest of the characters are left as is.  Note that MEGA2 does not make any attempts to validate whether or not the characters in the selected block are nucleotides or not.  So, please use it carefully.


Copy Screenshot to Clipboard

Utilities | Copy Screenshot to Clipboard

This item presents three other options for selecting the format of the image that is copied to the clipboard.  Once copied, they can just be pasted in any other Graphic or Word Processing program.

Bitmap Format: This is the common Windows Bitmap (BMP) Format.

Windows Metafile Format: This selects the Windows Metafile Format (WMF)

Enhanced Metafile Format: This selects the Windows Enhanced Metafile Format.


Tree Explorer

Phylogeny | Any tree-building option

The Tree Explorer displays the evolutionary tree based on the options used to compute or display the phylogeny.  The Tree Explorer has the following items in the main menu:

File Menu

Image Menu

Subtree Menu

View Menu

Compute Menu


Divergence Time Dialog Box

This dialog box allows the uesrs to specify the evolutionary rate for constructing linearized trees.  This can be done by providing the evolutionary rate directly or by providing the divergence time for the given node.


File Menu (in Tree Explorer)

This menu has the following options:

Save	Brings up the Save As dialog box and saves all the information currently held by the Tree Explorer to a file in a binary format.  This feature is provided to allow you to retrieve the current Tree Explorer session for tree manipulation and printing later.
Export Current Tree	Writes the topology of the current tree in the MEGA tree format to a specified file.  Note that only the branching pattern is stored.
Export All Trees	Writes the topologies of all trees in the MEGA tree format to a specified file. Note that only the branching pattern is stored.
Show Information	Brings up the Information dialog box.
Print	Brings up the Print dialog box and prints the current tree in the displayed size.  This means that if the displayed tree is larger than the page size, it will be printed in multiple pages.
Print in a sheet	Brings up the Print dialog box and prints the current tree, after restricting the size of the printed tree to one sheet. The current tree can be printed using the button on the toolbar also.
Printer Setup	Allows the user to setup the printer.
Exit Tree Explorer	Exits.


Image Menu (in Tree Explorer)

This contains two options, one to copy the tree image to the clipboard (this can also be done by simultaneously pressing Ctrl and ‘C’ keys), and the other to save the image as an enhanced windows metafile (.EMF).  The second option brings up the Save As dialog box.


Subtree Menu (in Tree Explorer)

This menu contains various tree manipulation options, namely, Swap, Flip and Compress/Uncompress.  In addition, the user can specify the root of the tree, and display a subtree (a portion of the tree defined by a given internal branch) in a separate window.  This done by clicking on the corresponding items in the menu (for which there are tool buttons on the left).

Choosing the option of ‘Divergence Time’ transforms the cursor to an arrow below which is the icon associated with the divergence time option.  If the user is interested in obtaining the evolutionary rate of a specific lineage then he/she should point the cursor to that branch and click.  On the other hand, if the average evolutionary rate of a cluster of two or more taxa is of interest, then the user should click at the node at the common ancestor of the cluster.  Either way, MEGA brings up the Divergence Time dialog box, which displays the evolutionary rate information for a given divergence time.

All the above menu options can also chosen by two other means – the single letter codes given next to the options in the menu, or the icons given on the left hand side of the Tree Explorer dialog box.  Holding the cursor over an icon brings up a little popup that tells the user what the icon does.


View Menu (in Tree Explorer)

This menu brings up several viewing options:

Topology only	Displays the tree only in the form of relationships among the taxa, ignoring the branch lengths.
Root on Midpoint	This roots the tree on the midpoint of the longest path between two taxa.
Arrange Taxa	It allows you to arrange the taxa in the tree either based on the order of taxa in the input data file or to produce a tree that looks "balanced."
Tree/Branch Style	It allows you to select the display of the tree in one of three styles: Traditional, Radiation, or Circle.  For traditional style, there are three additional options for branch display: Rectangular, Straight or Curved.
Show/Hide	It allows the user to display or hide the following information: taxon label, taxon marker, statistics (e.g., bootstrap values), branch lengths, or scale bar.
Fonts	Use it to choose the font type, size etc. for writing various information, including taxon label, statistics, scale bar etc.
Options	It brings up the Option dialog box, which provides control over various aspects of the tree drawing including individual branches, the taxon names, and the scale bar.


Compute Menu (in Tree Explorer)

Allows various computations from the tree, namely, Condensed tree, Linearized tree, and Consensus tree.  Furthermore, it also allows the user to estimate the divergence time for each node using the molecular clock.


Options dialog box (in Tree Explorer)

Through this dialog box, you can specify various drawing attributes for the tree.  All options are organized in five tabs.

Tree

Branch

Labels

Scale Bar

Cutoff Values


Tree tab (in Options dialog box)

This tab allows the user to manipulate aspects of the tree depending upon the style used to draw the tree.  For instance, if the traditional rectangular style is used, then the user can manipulate the taxon separation distance, branch length, or tree width, in the number of pixels.  This tab also contains a schematic of a tree illustrating these aspects.


Branch tab (in Options dialog box)

This tab has options for the following aspects of the tree:

Line Width.  Allows the user to choose the width of the lines.

Display Statistics/Frequency.  Option to Hide or Show the statistics/frequency, to choose a desired font, to alter the placement of the numbers by manipulating the horizontal and vertical positions.

Display Branch Length.  Option to Show the branch length or Hide it if it is shorter than a specified length, alter the placement of the branch lengths written, and choose the number of decimal places for writing the branch lengths.


Labels tab (in Options dialog box)

This tab has options for the following:

Display Taxon Names.  Option to show (checked) or hide (unchecked) the label and a choice of font to use.

Display Markers.  This allows the user to choose to draw small symbols along with or instead of taxa names in the tree.  Two combo boxes and a list provide facilities to select the marker graphics and its color.


Scale Bar tab (in Options dialog box)

This tab has options for the following:

Line Width.  Allows the user to choose the width of the line used in the scale bar from a drop-down menu of different sizes.  The user may also choose the font size of the scale bar.

Show Distance Scale.  Option to show/hide the distance in the scale bar, and to enter the unit used.  The user also has the option to decide the length of the scale bar and the interval between tick marks on the scale bar, if used.

Show Time Scale. Option to show/hide the divergence time in the scale bar, and to enter the units used.  The user also has the option to determine the interval between two major ticks and two minor ticks.  Divergence time for a node or the evolutionary rate must be given to activate this option.


Information Box

The information box in the Tree Explorer provides various statistical attributes of the tree displayed and the branch or node highlighted.  It usually contains multiple tabs.

General.  This reminds the user about the number of taxa used (and groups, if any) and the strategy used to deal with gaps and missing data.

Tree.  This contains information about the type of tree made – rooted/unrooted, and the sum of branch lengths, SBL, or the treelength.  In addition, information about the total number of trees and the tree number of the current tree is displayed.

Branch.   In the Tree Explorer window you may click on a branch or on a node of the tree.  If you click on a branch this tab displays the location of the branch in terms of the two nodes this branch connects.  (Leaf taxa are numbered in the order they appear in the input data file.)  Furthermore, it also displays the length of the selected branch.  In addition, if you click on a node, the internal identification number of that node is displayed (discussion of the actual use of this number will be given in a future release of MEGA).


Subtree Drawing Options (in Tree Explorer)

This dialog box provides choices for compressing a subtree or changing its drawing attributes locally.

Name.  It is the name for the current subtree.

Font: You can choose the font to be used for writing the name of the subtree.

Style.  Specifies how the branch connecting the group to the rest of the tree is drawn.

Color: Color for drawing the subtree branching pattern.

Draw Subtree Name w/Bracket. Allows you to add a enclosing bracket or line (selected from Style) on the right side of the subtree.  You can select, Color and Width for this line, as well as fonts for taxa labels.

Compress Subtree.  Options to compress or decompress the subtree and the options to fill the triangle cluster area when the subtree is compressed.


Cutoff Values Tab

In this tab, you specify a cut-off level for the condensed or consensus trees.  Appropriate options become available depending on the trees displayed.


Blank Names Are Not Permitted

As the error suggests, you cannot provide a blank name for a sequence, taxa, domain, or gene.


Domains Cannot Overlap

In MEGA, any given site can belong to at most one domain.  You are attempting to assign a site (or range of sites) that already belong to another domain.  If you want to carry out this action, you should first change the other domain's definition (or delete the other domain) to free the desired sites.  Then you can include them into your domain of choice.


Fisher's Exact Test Has Failed

Fisher's exact test for non-neutral evolution fails for a number of reasons.  In this test, we use the estimates of the number of synonymous sites (S), the number of nonsynonymous sites (N), the number of synonymous differences (Sd), and the number of nonsynonymous differences (Nd).  If these numbers are very large, some mathematical functions in MEGA may not be able to handle them (we have tried to avoid this problem by using logarithms of factorials).  Please compute S, N, Sd, and Nd using the Distances|Pairwise option four times to diagnose the problem.  If you are still uncertain about why this happened, please contact us.


Gamma Distance Failed Because p > 0.99

For amino acid distance estimation, the proportion of amino acids that are different between two sequences has exceeded 99%.  In this case, the Gamma distance cannot be calculated.  If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Gene Names Must Be Unique

MEGA requires that all gene names be unique, as not two genes in a genome should be named the same.  On the other hand, many domains can have the same name.  This is because, for convenience, you may want to name the first exon for all genes to be Exon-I.


Inapplicable Computation Requested

You have requested a computation that is not allowed (or available) for the currently active dataset.  If you think that this is not true, then please report this potential software bug to us.


Incorrect Command Used

The selected command/option is not valid here.  Please look at the brief description provided in the error message window to get an idea about the nature of the problem.


Invalid special symbol in molecular sequences

Unique ASCII characters, except letters and '*', can be used as special symbols for alignment gaps, missing data, and identical sites.  Frequently used symbols for identical sites, alignment gaps, and missing data are '.', '-', and '?', respectively.  It appears that you have attempted to use the same symbols for two or more of these types of sites, or a chosen symbol is not appropriate, e.g., do not use N (the ambiguous site symbol for DNA/RNA sequences), or X (the ambiguous site symbol for protein sequences) because they are already available as the IUPAC symbols for molecular sequences.


Jukes-Cantor Distance Failed

The Jukes-Cantor correction is used in nucleotide distances and the synonymous and nonsynonymous substitution distances.  If the proportion of sites that are different (nucleotides, synonymous, or nonsynonymous) is greater than or equal to 75%, the Jukes-Cantor correction cannot be applied.  This seems to have happened for some pairs in your data.  If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Kimura Distance Failed

The Kimura (1980) distance correction is used in a number of places, including nucleotide distances and the synonymous and nonsynonymous substitution distances.  These formulas cannot be applied if the argument in the logarithm becomes close to zero or negative.  This seems to have happened for some pairs in your data.  If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Missing data or invalid distances in the matrix

The selected set of taxa contains one or more pair of taxa for which the evolutionary distance is either invalid or not available.  Please inspect the distance data in the Data Explorer to identify those pairs. You may need to  remove one or more taxa.


No Common Sites

For the given selection of sequences and the data subset options, MEGA found zero common sites in the data.  Please examine your data in the Data Explorer to see why this has happened.


Not Enough Groups Selected

Your currently active dataset (or subset) does not contain enough groups to conduct the desired analysis.  Please define or select more groups using the Setup Taxa and Groups Dialog.


Not Enough Taxa Selected

Your currently active dataset (or subset) does not contain enough numbers of sequences or taxa to conduct the desired analysis.  Please add (or select) more sequences.


Not Yet Implemented

The task you requested appears to have not been activated in MEGA.  This function either should not be available in your release of MEGA or it needs to be activated by us.  Please contact the authors and report this software bug at the earliest convenience.


p distance is found to be > 1

This peculiar situation can occur in the computation of the proportion of synonymous (or nonsynonymous) substitutions per site, especially when the number of codons included is small.  If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Poisson Correction Failed because p > 0.99

For amino acid distance estimation, the proportion of amino acids different between two sequences has exceeded 99%.  In this case, the Poisson correction distance formula cannot be applied. If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Tajima-Nei Distance Could Not Be Computed

For one or more pairs of sequences, Tajima-Nei correction could not applied. This usually occurs if the argument in the log term of the formula becomes too close to zero.  If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Tamura (1992) Distance Could Not Be Computed

For one or more pairs of sequences, Tajima-Nei correction could not applied. This usually occurs if the argument in the log term of the formula becomes too close to zero or negative, or if the G+C-content is 0% or 100%.  If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Tamura-Nei Distance Could Not Be Computed

For one or more pairs of sequences, Tamura-Nei distance could not applied. Tamura-Nei's distance formula contains many log terms, and some of their arguments have becomes too close to zero or negative.  If you wish to know which pair(s) of sequences has this problem, please use the Distances|Pairwise option.  All such pairs will be shown in the Distance Matrix Dialog with a red entry (n/c).


Unexpected Error

While carrying out the requested task, an unexpected error has occurred in MEGA.  Please contact the authors and report this software bug as soon as possible.  Thank you for taking time to write the bug report.  We will try and solve the problem at the earliest possible time.


User Stopped Computation

You appear to have aborted the current process by pressing the Stop process button on the progress indicator.