Evidence map›Paper›PMID 28235418›Full record

ArticleGenome biology2017

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes.

Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra Sekhar Reddy Chilamakuri, Bruce A J Ponder, Kerstin B Meyer, Florian Markowetz

Abstract read
In one paragraph

Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
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  4. Epistatic contributions to human traits via transcription factor mechanisms.medRxiv : the preprint server for health sciences · 2025
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Ines de SantiagoCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
Wei LiuCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
Ke YuanCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
Martin O'ReillyCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
Chandra Sekhar Reddy ChilamakuriCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
Bruce A J PonderCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK.
Kerstin B MeyerCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK. Kerstin.Meyer@cruk.cam.ac.uk.
Florian MarkowetzCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, UK. florian.markowetz@cruk.cam.ac.uk.ORCID 0000-0002-2784-5308

Funding

Cancer Research UK 19274Cancer Research UK A19274Cancer Research UK C14303/A17197
6 · The paper itself

Abstract

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods of detecting an allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy-number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and six targeted FAIRE-seq samples, we show that BaalChIP effectively corrects allele-specific analysis for copy-number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

Indexed as

AllelesBayes TheoremBinding SitesAllelic ImbalanceCell Line, TumorChromatin ImmunoprecipitationComputational BiologyDNA Copy Number VariationsGene AmplificationGenotypeHigh-Throughput Nucleotide SequencingHumansNeoplasmsQuality ControlReproducibility of ResultsTranscription FactorsTranscription FactorsAllele frequencyAllele-specific bindingBayesian statisticsCancerChIP-sequencingCopy-number changeFAIRE-sequencing

Identifiers

PMID28235418
PMCPMC5326502

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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.