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

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

Author information

1
Department of Computer Science, Princeton University, Princeton, NJ 08544, USA, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA and Simons Center for Data Analysis, Simons Foundation, New York, NY, 10010, USA Department of Computer Science, Princeton University, Princeton, NJ 08544, USA, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA and Simons Center for Data Analysis, Simons Foundation, New York, NY, 10010, USA.
2
Department of Computer Science, Princeton University, Princeton, NJ 08544, USA, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA and Simons Center for Data Analysis, Simons Foundation, New York, NY, 10010, USA.
3
Department of Computer Science, Princeton University, Princeton, NJ 08544, USA, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA and Simons Center for Data Analysis, Simons Foundation, New York, NY, 10010, USA Department of Computer Science, Princeton University, Princeton, NJ 08544, USA, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA and Simons Center for Data Analysis, Simons Foundation, New York, NY, 10010, USA Department of Computer Science, Princeton University, Princeton, NJ 08544, USA, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA and Simons Center for Data Analysis, Simons Foundation, New York, NY, 10010, USA.

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).

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.

PMID:
25431329
PMCID:
PMC4804827
DOI:
10.1093/bioinformatics/btu786
[Indexed for MEDLINE]
Free PMC Article

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