SpringerProtocols: Abstract: Discovering Biological Networks from Diverse Functional Genomic Data

9. Discovering Biological Networks from Diverse Functional Genomic Data

By: Chad L. Myers¹, Camelia Chiriac², Olga G. Troyanskaya³

Abstract

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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.

Affiliation(s): (1) Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
(2) Pharmacopeia, Cranbury, NJ, USA
(3) Department of Computer Science, Princeton University, Princeton, NJ, USA

Book Title: Protein Networks and Pathway Analysis

Series: Methods in Molecular Biology | Volume: 563 | Pub. Date: Jul-01-2009 | Page Range: 157-175 | DOI: 10.1007/978-1-60761-175-2_9

Subject: Protein Science

Key Words: Data integration - network inference - function prediction - Bayesian networks - pathway analysis - functional network - functional linkage

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