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Mega 12.1: Cross-Platform Release for macOS and Linux Operating Systems

  • Glen Stecher
  • , Michael Suleski
  • , Qiqing Tao
  • , Koichiro Tamura
  • , Sudhir Kumar
  • Temple University
  • Tokyo Metropolitan University
  • Institute for Genomics and Evolutionary Medicine
  • Department of Cancer Biology

Research output: Contribution to journalLetter

67 Scopus citations

Abstract

The Molecular Evolutionary Genetics Analysis (MEGA) software is widely used for molecular evolutionary and phylogenetic analyses. We present MEGA version 12.1, a cross-platform release that operates natively on macOS (Intel and Apple M-series processors) and modern Linux distributions. This version incorporates all the methodological and computational improvements of MEGA 12 for Microsoft Windows, including techniques that markedly reduce computational time during maximum likelihood (ML) analyses. These features include a filtered best-fit ML model test that bypasses evaluating derivative models unlikely to be optimal, an adaptive bootstrap test of phylogeny that automatically determines the necessary number of replicates, and fine-grained parallelization of ML algorithms for better multi-core performance. MEGA 12.1 has an enhanced graphical user interface, supporting high-resolution displays and improving analysis progress reporting and result visualization. A significant addition in MEGA 12.1 is an improved Calibration Editor that integrates seamlessly with the TimeTree database of molecular divergence times for easy retrieval of calibration points for molecular dating. This version also supports full cross-platform session file compatibility, allowing seamless sharing of analysis sessions across macOS, Linux, and Windows. These updates enhance accessibility, computational efficiency, and usability of MEGA across diverse computing environments. MEGA 12.1 is available for free at https://www.megasoftware.net.

Original languageEnglish
Pages (from-to)14-18
Number of pages5
JournalJournal of Molecular Evolution
Volume94
Issue number1
Early online dateNov 17 2025
DOIs
StatePublished - Feb 2026

Keywords

  • Algorithms
  • Computational Biology/methods
  • Evolution, Molecular
  • Likelihood Functions
  • Phylogeny
  • Software
  • User-Computer Interface

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