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Kundaje A, et al.  (2008) A predictive model of the oxygen and heme regulatory network in yeast. PLoS Comput Biol 4(11):e1000224

Abstract: Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co(2+). MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.

Status: Published Type: Journal Article PubMed ID: 19008939

Topics addressed in this paper

Number of different genes curated to this paper: 67

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Topics Topics not linked to Genes Genes linked to topics (#1 - 10 )
ABF1 AFR1 ANB1 ARF1 ARG82 ASG1 ASK10 BEM2 CDC42 CTH1
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Topics Genes linked to topics (#11 - 20 )
CYC7 CYT1 DAL80 DAN1 DAN4 FUS3 GLC8 HAP1 HAP4 HMS1
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Topics Genes linked to topics (#21 - 30 )
IBA57 IRE1 MBP1 MDG1 MET18 MET28 MGA2 MTF1 MTH1 NRG1
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Regulation of blue ball
Regulatory Role blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball
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Topics Genes linked to topics (#31 - 40 )
OLE1 PAU2 PAU23 PAU24 PAU3 PAU4 PAU5 PAU6 PAU7 PCL1
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Topics Genes linked to topics (#41 - 50 )
PDE2 PIG1 PLP2 PPH3 PTP3 RGS2 RME1 ROX1 RTG3 SIC1
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Regulatory Role blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball

Topics Genes linked to topics (#51 - 60 )
SIP3 SIW14 SSK22 STP2 SWI5 TBS1 TEC1 UPC2 URE2 WAR1
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Regulatory Role blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball blue ball
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Topics Genes linked to topics (#61 - 67 )
WSC4 WTM1 WTM2 YAP7 YGK3 YPD1 ZAP1
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Regulatory Role blue ball blue ball blue ball blue ball blue ball blue ball blue ball

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