Evidence map›Paper›PMID 22863112›Full record

ArticleBMC genomics2012

Cell-type specificity of ChIP-predicted transcription factor binding sites.

Tony Håndstad, Morten Rye, Rok Močnik, Finn Drabløs, Pål Sætrom

Abstract read
In one paragraph

Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

13 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

5 authors.

Tony HåndstadDepartment of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway.
Morten Rye
Rok Močnik
Finn Drabløs
Pål Sætrom

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundContext-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context-the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding?

resultsWe compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity.

conclusionsTogether, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data-ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure-we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

Indexed as

Base SequenceBinding SitesCell Line, TumorChromatinChromatin ImmunoprecipitationDNAGene ExpressionGene Regulatory NetworksGenotypeHeLa CellsHigh-Throughput Nucleotide SequencingHistonesHumansPolymorphism, Single NucleotideRegulatory Sequences, Nucleic AcidSequence Analysis, DNAChromatinDNAHistonesTranscription Factors

Identifiers

PMID22863112
PMCPMC3574057

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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.