Genome-Wide Detection and Analysis of Multifunctional Genes

Yuri Pritykin, Dario Ghersi, Mona Singh

Research output: Contribution to journalArticle

20 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere1004467
JournalPLoS Computational Biology
Volume11
Issue number10
DOIs
StatePublished - 2015

Fingerprint

Genome
genome
Genes
Gene
Biological Phenomena
gene
biological processes
genes
Likely
Protein Interaction Maps
Tend
Distinct
Functional Genomics
detection
analysis
Melanogaster
protein
Protein Interaction Networks
Genomics
physicochemical property

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Molecular Biology
  • Ecology
  • Cellular and Molecular Neuroscience

Cite this

Genome-Wide Detection and Analysis of Multifunctional Genes. / Pritykin, Yuri; Ghersi, Dario; Singh, Mona.

In: PLoS Computational Biology, Vol. 11, No. 10, e1004467, 2015.

Research output: Contribution to journalArticle

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