Discovering Biological Networks from Diverse Functional Genomic Data

  • Chad L. Myers
  • Camelia Chiriac
  • Olga G. Troyanskaya
Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 563)

Abstract

Recent advances in biotechnology have produced a wealth of genomic data, which capture a variety of complementary cellular features. While these data promise to yield key insights into molecular biology, much of the available information remains underutilized because of the lack of scalable approaches for integrating signals across large, diverse data sets. A proper framework for capturing these numerous snapshots of complementary phenomena under a variety of conditions can provide the holistic view necessary for developing precise systems-level hypotheses.

Here we describe bioPIXIE, a system for combining information from diverse genomic data sets to predict biological networks. bioPIXIE utilizes a Bayesian framework for probabilistic integration of several high-throughput genomic data types including gene expression, protein–protein interactions, genetic interactions, protein localization, and sequence data to predict biological networks. The main purpose of the system is to support user-driven exploration through the inferred functional network, which is enabled by a public, web-based interface. We describe the features and supporting methods of this integration and discovery framework and present case examples where bioPIXIE has been used to generate specific, testable hypotheses for Saccharomyces cerevisiae, many of which have been confirmed experimentally.

Key words

Data integration network inference function prediction Bayesian networks pathway analysis functional network functional linkage 
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Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Chad L. Myers
    • 1
  • Camelia Chiriac
    • 2
  • Olga G. Troyanskaya
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.PharmacopeiaCranburyUSA
  3. 3.Department of Computer SciencePrinceton UniversityPrincetonUSA

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