Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms

Christopher Y. Park, Arjun Krishnan, Qian Zhu, Aaron K. Wong, Young Suk Lee, Olga G. Troyanskaya

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Motivation: Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses. Results: We developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry).

Original languageEnglish (US)
Pages (from-to)1093-1101
Number of pages9
JournalBioinformatics
Volume31
Issue number7
DOIs
StatePublished - Apr 1 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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