Evidence map›Paper›PMID 20436461›Full record

ArticleNature biotechnology2010

GREAT improves functional interpretation of cis-regulatory regions.

Cory Y McLean, Dave Bristor, Michael Hiller, Shoa L Clarke, Bruce T Schaar, Craig B Lowe, Aaron M Wenger, Gill Bejerano

Abstract read
In one paragraph

Article in Nature biotechnology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3,090 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3,090citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

3,090 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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3,030 more citing papers are in PubMed but not listed here.

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

8 authors.

Cory Y McLeanDepartment of Computer Science, Stanford University, Stanford, California, USA.
Dave Bristor
Michael Hiller
Shoa L Clarke
Bruce T Schaar
Craig B Lowe
Aaron M Wenger
Gill Bejerano

Funding

Characterization of regulatory elements leading to human limb malformationsR01HD059862 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AHITUV, NADAV, BEJERANO, GILL · 2009 to 2013
$2.2M
Howard Hughes Medical InstituteNICHD NIH HHS 1R01HD059862NICHD NIH HHS R01 HD059862
6 · The paper itself

Abstract

We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.

Indexed as

GenomeRegulatory Elements, TranscriptionalSoftwareAnimalsChromatin ImmunoprecipitationDatabases, GeneticData MiningE1A-Associated p300 ProteinGenomicsHumansJurkat CellsMiceProtein BindingSerum Response FactorE1A-Associated p300 ProteinEp300 protein, mouseSerum Response Factor

Identifiers

PMID20436461
PMCPMC4840234

What OpenQuestion holds

Textmetadata
LicenceTDM
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.