Optimization of co-evolution analysis through phylogenetic profiling reveals pathway-specific signals

Abstract

Summary The exponential growth in available genomic data is expected to reach full sequencing of a million genomes in the coming decade. Improving and developing methods to analyze these genomes and to reveal their utility is of major interest in a wide variety of fields, such as comparative and functional genomics, evolution and bioinformatics. Phylogenetic profiling is an established method for predicting functional interactions between proteins based on similarities in their evolutionary patterns across species. Proteins that function together (i.e. generate complexes, interact in the same pathways or improve adaptation to environmental niches) tend to show coordinated evolution across the tree of life. The normalized phylogenetic profiling (NPP) method takes into account minute changes in proteins across species to identify protein co-evolution. Despite the success of this method, it is still not clear what set of parameters is required for optimal use of co-evolution in predicting functional interactions. Moreover, it is not clear if pathway evolution or function should direct parameter choice. Here, we create a reliable and usable NPP construction pipeline. We explore the effect of parameter selection on functional interaction prediction using NPP from 1028 genomes, both separately and in various value combinations. We identify several parameter sets that optimize performance for pathways with certain biological annotation. This work reveals the importance of choosing the right parameters for optimized function prediction based on a biological context. Availability and implementation Source code and documentation are available on GitHub: https://github.com/iditam/CompareNPPs. Supplementary information Supplementary data are available at Bioinformatics online.

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“…In our recent work, we developed an improved version of PP that normalizes signals across hundreds of eukaryotic genomes to identify important new members of cellular and disease pathways (27–31) . As protein function and evolution are very complex (32) , co-evolution within a local lineage (i.e. mammals, vertebrates, fungi, etc.)…”

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Expanding the MECP2 network using comparative genomics reveals potential therapeutic targets for Rett syndrome

“…In our recent work, we developed an improved version of PP that normalizes signals across hundreds of eukaryotic genomes to identify important new members of cellular and disease pathways ( Arkadir et al, 2019 ; Sherill-Rofe et al, 2019 ; Tabach et al, 2013a ; Tabach et al, 2013b ; Schwartz et al, 2013 ). As protein function and evolution are very complex ( Bloch et al, 2020 ), co-evolution within a local lineage (i.e. mammals, vertebrates, fungi, etc.)…”