Methods for Evolutionary Genomics Analysis

PROJECT SUMMARY This administrative supplement request aims to develop a cloud-enabled, highly scalable version of the computational core of the Molecular Evolutionary Genetics Analysis software (MEGA-CC: www.megasoftware.net).

The development of MEGA-CC is a significant component of the NIH-funded research project to develop machine learning methods and tools for comparative analysis of molecular sequences.

With big advances in genome sequencing, researchers are assembling datasets containing large numbers of species, strains, genes, and genomic segments.

Phylogenomic analyses of these data are essential to understanding the dynamics of evolutionary change of pathogens, humans, and species across the tree of life.

Machine learning methods and software tools for phylogenomics are now necessary because the expanding size of phylogenomic datasets limits the practical utility of currently available methods and tools due to excessive computational time and memory requirements.

One component of the funded grant is implementing our new machine learning methods in the MEGA software suite (www.megasoftware.net), an extremely popular bioinformatics software (>20,000 peer-reviewed citations and 350,000 software downloads in the year 2020 alone).

The MEGA software includes a large repertoire of tools for assembling sequence alignments, inferring evolutionary trees, estimating genetic distances and diversities, inferring ancestral sequences, computing timetrees, and testing selection.

These analyses are now required in all research investigations and fields in which multiple DNA or RNA sequences are used.

However, MEGA and its computational core (MEGA-CC) are not optimized for distribution and execution on cloud infrastructure and high-performance computing clusters.

This supplement to the funded grant will enable us to advance MEGA for cloud readiness to harness the scalability, elastic computing power, and easy software upgrade and maintenance enabled by cloud infrastructure (MEGA-CR). It will also make MEGA interoperable with existing and future cloud infrastructure.

Additionally, this supplement will facilitate using the new machine learning methods in MEGA with big genomic data in practice, thus addressing an imminent and fast-growing need for an increasingly larger community of researchers using MEGA.

MEGA-CR will increase the usability of MEGA for the scientific community analyzing very large datasets for which greater accessibility, cost-efficiency, and scalability of cloud-readiness is becoming crucial.