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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

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

Sudhir Kumar¹, Glen Stecher, Daniel Peterson

¹Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University (ASU), Tempe, AZ 85287-5301, USA.

Bioinformatics (Oxford, England)

|August 28, 2012

Summary

This summary is machine-generated.

Researchers can now automate large-scale molecular evolutionary genetics analysis (MEGA) with MEGA-CC, a command-line tool. This enables integration into complex bioinformatics workflows for faster, more efficient data processing and evolutionary studies.

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Area of Science:

Bioinformatics
Computational Biology
Molecular Evolution

Background:

The Molecular Evolutionary Genetics Analysis (MEGA) software is widely used.
There is a growing demand for batch processing and workflow integration of MEGA.
Existing MEGA versions lack command-line functionality for automated analysis.

Purpose of the Study:

To introduce MEGA-CC, a command-line executable of the MEGA software.
To present MEGA-Proto, an analysis prototyper for workflow development.
To enable automated and iterative data analysis using MEGA's computational tools.

Main Methods:

Developed MEGA-CC as a stand-alone executable providing access to all MEGA GUI analyses.
Integrated parallel processing capabilities for maximum likelihood phylogenetic analysis.
Created MEGA-Proto to facilitate the design of analysis workflows.

Main Results:

MEGA-CC offers comprehensive computational analyses including alignment, model selection, phylogeny inference, and selection tests.
Enhanced phylogenetic analysis with parallel execution on multi-core processors.
Demonstrated the utility of MEGA-CC and MEGA-Proto for automated data analysis.

Conclusions:

MEGA-CC and MEGA-Proto significantly enhance the utility of MEGA for large-scale and automated analyses.
These tools facilitate the integration of MEGA into complex bioinformatics pipelines.
The release supports advanced research in molecular evolution and related fields.