title: WHEN SHOULD WE NOT TRANSFER FUNCTIONAL ANNOTATION BETWEEN SEQUENCE PARALOGS?
abstract: Current automated computational methods to assign functional labels to unstudied genes often involve transferring annotation from orthologous or paralogous genes, however such genes can evolve divergent functions, making such transfer inappropriate. We consider the problem of determining when it is correct to make such an assignment between paralogs. We construct a benchmark dataset of two types of similar paralogous pairs of genes in the well-studied model organism S. cerevisiae: one set of pairs where single deletion mutants have very similar phenotypes (implying similar functions), and another set of pairs where single deletion mutants have very divergent phenotypes (implying different functions). State of the art methods for this problem will determine the evolutionary history of the paralogs with references to multiple related species. Here, we ask a first and simpler question: we explore to what extent any computational method with access only to data from a single species can solve this problem.We consider divergence data (at both the amino acid and nucleotide levels), and network data (based on the yeast protein-protein interaction network, as captured in BioGRID), and ask if we can extract features from these data that can distinguish between these sets of paralogous gene pairs. We find that the best features come from measures of sequence divergence, however, simple network measures based on degree or centrality or shortest path or diffusion state distance (DSD), or shared neighborhood in the yeast protein-protein interaction (PPI) network also contain some signal. One should, in general, not transfer function if sequence divergence is too high. Further improvements in classification will need to come from more computationally expensive but much more powerful evolutionary methods that incorporate ancestral states and measure evolutionary divergence over multiple species based on evolutionary trees.
authors.affiliation: Department of Computer Science, Tufts University, Medford, MA 02155, USA, mengfei.cao@tufts.edu.
authors.forename: Mengfei
authors.initials: M
authors.lastname: Cao
authors.forename: Lenore J
authors.initials: LJ
authors.lastname: Cowen
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D020440:Gene Duplication; D005800:Genes, Fungal; D058977:Molecular Sequence Annotation; D060066:Protein Interaction Maps / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D017386:Sequence Homology, Amino Acid; D000069553:Supervised Machine Learning
chemical_list: D029701:Saccharomyces cerevisiae Proteins