Evolutionary Sparse Learning for phylogenomics

Abstract

AO_SCPLOWBSTRACTC_SCPLOWWe 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 does not directly model 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 directly 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. ESLs fast computational times and small memory footprint will also help democratize big data analytics and improve scientific rigor in phylogenomics.