Evidence map›Paper›PMID 23457041›Full record

ArticleBioinformatics (Oxford, England)2013

ChIP-PED enhances the analysis of ChIP-seq and ChIP-chip data.

George Wu, Jason T Yustein, Matthew N McCall, Michael Zilliox, Rafael A Irizarry, Karen Zeller, Chi V Dang, Hongkai Ji

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

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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

8 authors.

George WuDepartment of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Jason T Yustein
Matthew N McCall
Michael Zilliox
Rafael A Irizarry
Karen Zeller
Chi V Dang
Hongkai Ji

Funding

UNIVERSITY OF PENNSYLVANIA CAN CTR SUPPORT GRANTP30CA016520 · NCI · UNIVERSITY OF PENNSYLVANIA · PI Susan M Domchek · 1985 to 2026
$222.3M
Preprocessing and Analysis Tools for High-Throughput TechnologiesR01GM083084 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2007 to 2018
$4.4M
Overcoming bias and unwanted variability in next generation sequencingR01HG005220 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2010 to 2018
$3.6M
Computational Tools for Mining Large Amounts of ChIP and Gene Expression DataR01HG006282 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI JI, HONGKAI · 2012 to 2016
$2.0M
Predoctoral Biostatistics Training in Genetics/GenomicsT32GM074906 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2006 to 2010
$905k
Software for the statistical analysis of microarray probe level dataR01GM103552 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2012 to 2013
$735k
NCI NIH HHS P30 CA016520NHGRI NIH HHS R01 HG005220NHGRI NIH HHS R01HG005220NHGRI NIH HHS R01 HG006282NHGRI NIH HHS R01HG006282NIGMS NIH HHS R01 GM083084NIGMS NIH HHS R01 GM103552NIGMS NIH HHS T32GM074906
6 · The paper itself

Abstract

motivationAlthough chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) or tiling array hybridization (ChIP-chip) is increasingly used to map genome-wide-binding sites of transcription factors (TFs), it still remains difficult to generate a quality ChIPx (i.e. ChIP-seq or ChIP-chip) dataset because of the tremendous amount of effort required to develop effective antibodies and efficient protocols. Moreover, most laboratories are unable to easily obtain ChIPx data for one or more TF(s) in more than a handful of biological contexts. Thus, standard ChIPx analyses primarily focus on analyzing data from one experiment, and the discoveries are restricted to a specific biological context.

resultsWe propose to enrich this existing data analysis paradigm by developing a novel approach, ChIP-PED, which superimposes ChIPx data on large amounts of publicly available human and mouse gene expression data containing a diverse collection of cell types, tissues and disease conditions to discover new biological contexts with potential TF regulatory activities. We demonstrate ChIP-PED using a number of examples, including a novel discovery that MYC, a human TF, plays an important functional role in pediatric Ewing sarcoma cell lines. These examples show that ChIP-PED increases the value of ChIPx data by allowing one to expand the scope of possible discoveries made from a ChIPx experiment. AVAILABILITY: http://www.biostat.jhsph.edu/~gewu/ChIPPED/

Indexed as

TranscriptomeAnimalsBinding SitesCell Line, TumorChromatin ImmunoprecipitationGene Expression RegulationHigh-Throughput Nucleotide SequencingHumansMiceOligonucleotide Array Sequence AnalysisSequence Analysis, DNATranscription FactorsTranscription Factors

Identifiers

PMID23457041
PMCPMC3658457

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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.