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      Discovery of biological networks from diverse functional genomic data

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          Abstract

          BioPIXIE is a probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data.

          Abstract

          We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web.

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          Genomic expression programs in the response of yeast cells to environmental changes.

          G Storz, P T Spellman, John P. Brown … (2000)
          We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.
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            Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

            Srinivasan Iyer, Michael Eisen, A Futcher … (1998)
            We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu
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              A Bayesian networks approach for predicting protein-protein interactions from genomic data.

              R. Jansen (2003)
              We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2005
                Publication date (Electronic): 19 December 2005
                Volume: 6
                Issue: 13
                Page: R114
                Affiliations
                [1 ]Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA
                [2 ]Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08544, USA
                [3 ]Department of Mathematics, Princeton University, Washington Road, Princeton, NJ 08540, USA
                [4 ]Department of Genetics, School of Medicine, Mailstop-S120, Stanford University, Stanford, CA 94305-5120, USA
                Article
                Publisher ID: gb-2005-6-13-r114
                DOI: 10.1186/gb-2005-6-13-r114
                PMC ID: 1414113
                PubMed ID: 16420673
                SO-VID: cfc97a26-3235-4620-9710-c5cac0c19b95
                Copyright © Copyright © 2005 Myers et al; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 1 July 2005
                Date revision received : 31 August 2005
                Date accepted : 21 November 2005
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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