Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.

We announce the release of the fourth version of MEGA software, which expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written in figure legend format, in order to provide natural language descriptions of the models and methods used in the analyses. This facility aims to promote a better understanding of the underlying assumptions used in analyses, and of the results generated. Another new feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary distances between all pairs of sequences simultaneously, with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages. This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree. This new version is a native 32-bit Windows application with multi-threading and multi-user supports, and it is also available to run in a Linux desktop environment (via the Wine compatibility layer) and on Intel-based Macintosh computers under the Parallels program. The current version of MEGA is available free of charge at (http://www.megasoftware.net).

A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.

With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of species and multigene families, estimating rates of molecular evolution, and inferring the nature and extent of selective forces shaping the evolution of genes and genomes. The scope of these investigations has now expanded greatly owing to the development of high-throughput sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. One such effort has been to produce Molecular Evolutionary Genetics Analysis (MEGA) software, with its focus on facilitating the exploration and analysis of the DNA and protein sequence variation from an evolutionary perspective. Currently in its third major release, MEGA3 contains facilities for automatic and manual sequence alignment, web-based mining of databases, inference of the phylogenetic trees, estimation of evolutionary distances and testing evolutionary hypotheses. This paper provides an overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA.

A mathematical model for the evolutionary change of restriction sites in mitochondrial DNA is developed.Formulas based on this model are presented for estimating the number of nucleotide substitutions between two populations or species. To express the degree of polymorphism in a population at the nucleotide level, a measure called "nucleotide diversity" is proposed. In recent years a number of authors have studied the genetic variation in mitochondril DNA (mtDNA) within and between species by using restriction endonucleases (1-6). An important finding from these studies is that mtDNA has a high rate of nucleotide substitution compared with nuclear DNA, and thus it is suited for studying the genetic divergence of closely related species (5-7). However, the mathematical theory for analyzing data from restriction enzyme studies is not well developed. To our knowledge, the only study is that of Upholt (8).A restriction endonuclease recognizes a specific sequence of nucleotide pairs, generally four or six pairs in length, and cleaves it. Therefore, if a circular DNA such as mtDNA has m such recognition (restriction) sites, it is fragmented into m segments after digestion by this enzyme. The number and locations of restriction sites vary with nucleotide sequence. The higher the similarity of the two DNA sequences compared, the closer the cleavage patterns. Therefore, it is possible to estimate the number of nucleotide substitutions between two homologous DNAs by comparing the locations of restriction sites. Similarly, the number of nucleotide substitutions may be estimated from the proportion of DNA fragments that are common to two organisms. Upholt (8) studied these two problems, but his formulation is not general and seems to involve some errors. Furthermore, Upholt paid no attention to the apparently high degree of heterogeneity of DNA sequences within populations (5). When the genetic divergence between closely related species is to be studied, it is necessary to eliminate the effect of this heterogeneity.The purpose of this paper is to develop a more rigorous mathematical model of genetic divergence of DNA and present a statistical method for analyzing data from restriction enzyme studies. In the first four sections we shall either assume that there is no polymorphism within populations or consider the genetic divergence between a pair of organisms (individuals) only. The assumption of no polymorphism will be removed in the fifth section.Evolutionary change of restriction sites Under certain circumstances it is possible to map restriction sites in DNA. Once these restriction sites are determined for two different organisms, the proportion of sites shared by them canThe publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked "advertisement" in accordance with 18 U. S. C. §1734 solely to indicate this fact. 5269 be computed. This proportion is expected to decline as the organisms' DNA sequences diverge. Before studying this problem, howeve...

No abstract

A method is presented by which the gene diversity (heterozygosity) of a subdivided population can be analyzed into its components, i.e., the gene diversities within and between subpopulations. This method is applicable to any population without regard to the number of alleles per locus, the pattern of evolutionary forces such as mutation, selection, and migration, and the reproductive method of the organism used. Measures of the absolute and relative magnitudes of gene differentiation among subpopulations are also proposed.

s.kumar@asu.edu