TY - JOUR
T1 - MEGA-CC
T2 - Computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis
AU - Kumar, Sudhir
AU - Stecher, Glen
AU - Peterson, Daniel
AU - Tamura, Koichiro
N1 - Funding Information:
Funding: Research grants from the US National Institutes of Health (HG002096-11 and GM081066-04 to SK) and Japan Society for the Promotion of Science (KT).
PY - 2012/10
Y1 - 2012/10
N2 - 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.
AB - 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.
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U2 - 10.1093/bioinformatics/bts507
DO - 10.1093/bioinformatics/bts507
M3 - Article
C2 - 22923298
AN - SCOPUS:84870450672
SN - 1367-4803
VL - 28
SP - 2685
EP - 2686
JO - Bioinformatics
JF - Bioinformatics
IS - 20
ER -