Evidence map›Paper›PMID 19008939›Full record

ArticlePLoS computational biology2008

A predictive model of the oxygen and heme regulatory network in yeast.

Anshul Kundaje, Xiantong Xin, Changgui Lan, Steve Lianoglou, Mei Zhou, Li Zhang, Christina Leslie

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
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  5. Prediction of protein-ligand interactions from paired protein sequence motifs and ligand substructures.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018
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  11. Review
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  14. Integrating heterogeneous gene expression data for gene regulatory network modelling.Theory in biosciences = Theorie in den Biowissenschaften · 2012
    Article
  15. Article
  16. Article
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  18. Article
  19. Hypoxia elicits broad and systematic changes in protein subcellular localization.American journal of physiology. Cell physiology · 2011
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Anshul KundajeDepartment of Computer Science, Columbia University, New York, New York, United States of America.
Xiantong Xin
Changgui Lan
Steve Lianoglou
Mei Zhou
Li Zhang
Christina Leslie
The University of Texas at Dallas · USMemorial Sloan Kettering Cancer Center · USColumbia University · US

Funding

Nation Center: Multi-Scale Study- Cellular Networks(RMI)U54CA121852 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI CALIFANO, ANDREA · 2005 to 2015
$37.4M
Molecular Chaperones in Heme SignalingR01GM062246 · NIGMS · UNIVERSITY OF TEXAS DALLAS · PI ZHANG, LI · 2002 to 2009
$2.5M
NCI NIH HHS CA121852.NCI NIH HHS U54 CA121852NIGMS NIH HHS GM62246NIGMS NIH HHS R01 GM062246
6 · The paper itself

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 Co2+. 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.

Indexed as

Models, BiologicalAlgorithmsComputational BiologyDatabases, Nucleic AcidDown-RegulationGene Expression ProfilingGene Regulatory NetworksHemeHot TemperatureMultigene FamilyOxygenSaccharomyces cerevisiaeUp-RegulationHemeOxygen

Identifiers

PMID19008939
PMCPMC2573020
OpenAlexW2054578424

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.