Literature DB >> 22923298

MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis.

Sudhir Kumar1, Glen Stecher, Daniel Peterson, Koichiro Tamura.   

Abstract

UNLABELLED: There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis. AVAILABILITY: http://www.megasoftware.net/.

Mesh:

Year:  2012        PMID: 22923298      PMCID: PMC3467750          DOI: 10.1093/bioinformatics/bts507

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

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Authors:  Alexandros Stamatakis
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2.  MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences.

Authors:  Sudhir Kumar; Masatoshi Nei; Joel Dudley; Koichiro Tamura
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3.  MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

Authors:  Koichiro Tamura; Daniel Peterson; Nicholas Peterson; Glen Stecher; Masatoshi Nei; Sudhir Kumar
Journal:  Mol Biol Evol       Date:  2011-05-04       Impact factor: 16.240

  3 in total
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