Evidence map›Paper›PMID 27311443›Full record

ArticleGenome research2016

Heterogeneity of transcription factor binding specificity models within and across cell lines.

Mahfuza Sharmin, Héctor Corrada Bravo, Sridhar Hannenhalli

Abstract read
In one paragraph

Article in Genome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

3 authors.

Mahfuza SharminDepartment of Computer Science, University of Maryland, College Park, Maryland 20742, USA; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA;
Héctor Corrada BravoDepartment of Computer Science, University of Maryland, College Park, Maryland 20742, USA; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA;
Sridhar HannenhalliCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA; Department of Cell and Molecular Biology, University of Maryland, College Park, Maryland 20742, USA.

Funding

Overcoming bias and unwanted variability in next generation sequencingR01HG005220 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2010 to 2018
$3.6M
Integrative Visual and Computational Exploratory Analysis of Genomics DataR01GM114267 · NIGMS · UNIV OF MARYLAND, COLLEGE PARK · PI CORRADA BRAVO, HECTOR · 2015 to 2018
$1.8M
Conundrums in Transcriptional RegulationR01GM100335 · NIGMS · UNIV OF MARYLAND, COLLEGE PARK · PI HANNENHALLI, SRIDHAR · 2012 to 2015
$1.2M
NHGRI NIH HHS R01 HG005220NIGMS NIH HHS R01 GM100335NIGMS NIH HHS R01 GM114267
6 · The paper itself

Abstract

Complex gene expression patterns are mediated by the binding of transcription factors (TFs) to specific genomic loci. The in vivo occupancy of a TF is, in large part, determined by the TF's DNA binding interaction partners, motivating genomic context-based models of TF occupancy. However, approaches thus far have assumed a uniform TF binding model to explain genome-wide cell-type-specific binding sites. Therefore, the cell type heterogeneity of TF occupancy models, as well as the extent to which binding rules underlying a TF's occupancy are shared across cell types, has not been investigated. Here, we develop an ensemble-based approach (TRISECT) to identify the heterogeneous binding rules for cell-type-specific TF occupancy and analyze the inter-cell-type sharing of such rules. Comprehensive analysis of 23 TFs, each with ChIP-seq data in four to 12 different cell types, shows that by explicitly capturing the heterogeneity of binding rules, TRISECT accurately identifies in vivo TF occupancy. Importantly, many of the binding rules derived from individual cell types are shared across cell types and reveal distinct yet functionally coherent putative target genes in different cell types. Closer inspection of the predicted cell-type-specific interaction partners provides insights into the context-specific functional landscape of a TF. Together, our novel ensemble-based approach reveals, for the first time, a widespread heterogeneity of binding rules, comprising the interaction partners within a cell type, many of which nevertheless transcend cell types. Notably, the putative targets of shared binding rules in different cell types, while distinct, exhibit significant functional coherence.

Indexed as

Genetic HeterogeneityBinding SitesCell LineageComputational BiologyDNA-Binding ProteinsGene Expression RegulationGenomicsHumansProtein BindingSensitivity and SpecificityTranscription FactorsDNA-Binding ProteinsTranscription Factors

Identifiers

PMID27311443
PMCPMC4971765

What OpenQuestion holds

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Registered trials

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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.