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

Bioinformatics. 2015 Apr 1;31(7):1093-101. doi: 10.1093/bioinformatics/btu786. Epub 2014 Nov 26.


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).

Availability and implementation: An interactive website hosting all of our interaction predictions is publically available at http://pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https://bitbucket.org/libsleipnir/libsleipnir.bitbucket.org.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Chromatin Immunoprecipitation
  • Computational Biology / methods*
  • Gene Regulatory Networks*
  • Genomics / methods*
  • Humans
  • Organ Specificity
  • Phosphorylation
  • Protein Interaction Mapping
  • Protein Serine-Threonine Kinases / antagonists & inhibitors
  • Protein Serine-Threonine Kinases / genetics
  • Protein Serine-Threonine Kinases / metabolism*
  • RNA, Small Interfering / genetics
  • Signal Transduction
  • Software
  • Transcription Factors / genetics
  • Transcription Factors / metabolism


  • RNA, Small Interfering
  • Transcription Factors
  • Protein Serine-Threonine Kinases
  • TBK1 protein, human