MEGA11: Molecular Evolutionary Genetics Analysis Version 11

• Published in: Molecular biology and evolution Vol. 38; no. 7; pp. 3022 - 3027
• Main Authors: Tamura, Koichiro, Stecher, Glen, Kumar, Sudhir
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.07.2021
• Subjects: Animal behavior; Bayesian analysis; Big data; Biological evolution; Computer software industry; Confidence intervals; Distribution (Probability theory); Divergence; Evolutionary genetics; Gene families; Genetics; Graphical user interface; International economic relations; Machine learning; Memory (Computers); Molecular evolution; Phylogeny; Resources; Sampling; Software; Statistical analysis; User experience; User interface; Window systems; Windows (computer programs); United States; phylogenetics; neutrality; software; tip dating; timetrees
• ISSN: 0737-4038; 1537-1719; 1537-1719
• DOI: 10.1093/molbev/msab120

Abstract The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor, and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net.