Evidence map›Paper›PMID 22011333›Full record

ArticleBMC genomics2011

Searching ChIP-seq genomic islands for combinatorial regulatory codes in mouse embryonic stem cells.

Gong Chen, Qing Zhou

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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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

2 authors at 1 institution in 1 country.

Gong ChenDepartment of Statistics, University of California, Los Angeles, Los Angeles, USA.
Qing Zhou
University of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo facilitate deciphering underlying transcriptional regulatory circuits in mouse embryonic stem (ES) cells, recent ChIP-seq data provided genome-wide binding locations of several key transcription factors (TFs); meanwhile, existing efforts profiled gene expression in ES cells and in their early differentiated state. It has been shown that the gene expression profiles are correlated with the binding of these TFs. However, it remains unclear whether other TFs, referred to as cofactors, participate the gene regulation by collaborating with the ChIP-seq TFs.

resultsBased on our analyses of the ES gene expression profiles and binding sites of potential cofactors in vicinity of the ChIP-seq TF binding locations, we identified a list of co-binding features that show significantly different characteristics between different gene expression patterns (activated or repressed gene expression in ES cells) at a false discovery rate of 10%. Gene classification with a subset of the identified features achieved up to 20% improvement over classification only based on the ChIP-seq TFs. More than 1/3 of reasoned regulatory roles of cofactor candidates involved in these features are supported by existing literatures. Finally, the predicted target genes of the majority candidates present expected expression change in another independent data set, which serves as a supplementary validation of these candidates.

conclusionsOur results revealed a list of combinatorial genomic features that are significantly associated with gene expression in ES cells, suggesting potential cofactors of the ChIP-seq TFs for gene regulation.

Indexed as

Chromatin ImmunoprecipitationGenomicsAnimalsEmbryonic Stem CellsGene Expression ProfilingMiceTranscription FactorsTranscription Factors

Identifiers

PMID22011333
PMCPMC3265564
OpenAlexW2125560142

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.