title: Genome-Wide Detection and Analysis of Multifunctional Genes.
abstract: Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular functioning, thereby leading to a better understanding of the functional landscape of the cell. However, to date, genome-wide analysis of multifunctional genes (and the proteins they encode) has been limited. Here we introduce a computational approach that uses known functional annotations to extract genes playing a role in at least two distinct biological processes. We leverage functional genomics data sets for three organisms--H. sapiens, D. melanogaster, and S. cerevisiae--and show that, as compared to other annotated genes, genes involved in multiple biological processes possess distinct physicochemical properties, are more broadly expressed, tend to be more central in protein interaction networks, tend to be more evolutionarily conserved, and are more likely to be essential. We also find that multifunctional genes are significantly more likely to be involved in human disorders. These same features also hold when multifunctionality is defined with respect to molecular functions instead of biological processes. Our analysis uncovers key features about multifunctional genes, and is a step towards a better genome-wide understanding of gene multifunctionality.
authors.affiliation: Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Yuri
authors.initials: Y
authors.lastname: Pritykin
authors.affiliation: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America; School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, United States of America.
authors.forename: Dario
authors.initials: D
authors.lastname: Ghersi
authors.affiliation: Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America.
authors.forename: Mona
authors.initials: M
authors.lastname: Singh
mesh_terms: D000818:Animals; D002874:Chromosome Mapping / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D029721:Drosophila Proteins / Q000235:genetics; D004331:Drosophila melanogaster / Q000235:genetics; D016678:Genome / Q000235:genetics*; D055106:Genome-Wide Association Study; D006801:Humans; D005810:Multigene Family / Q000235:genetics*; D009323:Natural Language Processing; D020543:Proteome / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D013045:Species Specificity
chemical_list: D029721:Drosophila Proteins; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins