FCCC LOGO Faculty Publications
Kumar S , Sharma S
Evolutionary Sparse Learning for Phylogenomics
Mol Biol Evol. 2021 Aug 3
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Abstract
We introduce a supervised machine learning approach with sparsity constraints for phylogenomics, referred to as evolutionary sparse learning (ESL). ESL builds models with genomic loci-such as genes, proteins, genomic segments, and positions-as parameters. Using the Least Absolute Shrinkage and Selection Operator (LASSO), ESL selects only the most important genomic loci to explain a given phylogenetic hypothesis or presence/absence of a trait. ESL models do not directly involve conventional parameters such as rates of substitutions between nucleotides, rate variation among positions, and phylogeny branch lengths. Instead, ESL directly employs the concordance of variation across sequences in an alignment with the evolutionary hypothesis of interest. ESL provides a natural way to combine different molecular and non-molecular data types and incorporate biological and functional annotations of genomic loci in model building. We propose positional, gene, function, and hypothesis sparsity scores, illustrate their use through an example, and suggest several applications of ESL. The ESL framework has the potential to drive the development of a new class of computational methods that will complement traditional approaches in evolutionary genomics, particularly for identifying influential loci and sequences given a phylogeny and building models to test hypotheses. ESL's fast computational times and small memory footprint will also help democratize big data analytics and improve scientific rigor in phylogenomics.
Notes
1537-1719 Kumar, Sudhir Sharma, Sudip R35 GM139540/GM/NIGMS NIH HHS/United States Journal Article United States Mol Biol Evol. 2021 Aug 3:msab227. doi: 10.1093/molbev/msab227.