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

Santiago, Inês de; Liu, Wei; Yuan, Ke; O’Reilly, Martin; Ponder, Bruce A.J.; Meyer, Kerstin B.; Markowetz, Florian

doi:10.1186/s13059-017-1165-7

Cited by 29 publications

(19 citation statements)

References 48 publications

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“…This is conveniently provided by ASB data. We collected ASB variants from five studies [14][15][16][17][18] , with each study providing heterozygous variants, the sample or cell line from which it was obtained, and reference and alternate allele read counts and the TF affected ( Figure 1a). Since read counts were collected from different sources and processed with their respective analysis pipelines, we also ensured results from different studies were consistent with one another where possible.…”

Section: A Compendium Of Allele-specific Binding Eventsmentioning

confidence: 99%

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Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Wagih

Merico

Delong

et al. 2018

Preprint

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Genetic variation has long been known to alter transcription factor binding sites, resulting in sometimes major phenotypic consequences. While the performance for current binding site predictors is well characterised, little is known on how these models perform at predicting the impact of variants. We collected and curated over 132,000 potential allele-specific binding (ASB) ChIP-seq variants across 101 transcription factors (TFs). We then assessed the accuracy of TF binding models from five different methods on these high-confidence measurements, finding that deep learning methods were the best performing yet still have room for improvement. Importantly, machine learning methods were consistently better than the venerable position weight matrix (PWM). Finally, predictions for certain TFs were consistently poor, and our investigation supports efforts to use features beyond sequence, such as methylation, DNA shape, and post-translational modifications. We submit that ASB data is a valuable benchmark for variant impact on TF binding. 23/3624/36

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Section: A Compendium Of Allele-specific Binding Eventsmentioning

confidence: 99%

“…ASB data were collected from five studies [14][15][16][17][18] . In each study, ChIP-seq reads are mapped to both alleles of heterozygous variants in individuals or cell lines.…”

Section: Collection Of Allele-specific Binding Datamentioning

confidence: 99%

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Wagih

Merico

Delong

et al. 2018

Preprint

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“…We assessed the degree of allele bias, measured as the fraction of ChIP-seq reads containing the reference sequence (hg19), for each DAP at all heterozygous single-nucleotide variants (SNVs) that overlapped with an adult DAP binding site (DeSantiago et al 2017). Correlation analyses of the degree of allele bias at each SNV between all possible pairs of DAPs revealed that factors possessed highly correlated allele biases, indicating that groups of DAPs bound near one another show preference for the same allele ( Fig.…”

Section: Daps Display Extensive Colocalizationmentioning

confidence: 99%

“…Allele bias in DAP occupancy was assessed with ChIP-seq data using the R package "BaalChIP" (DeSantiago et al 2017). Allele-specific expression was calculated for each expressed heterozygous SNP using the GATK "ASEReadCounter" function according to previously described best practices (Castel et al 2015).…”

Section: Allele-specific Binding and Expression Analysismentioning

confidence: 99%

A genome-wide interactome of DNA-associated proteins in the human liver

et al. 2017

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Large-scale efforts like the ENCODE Project have made tremendous progress in cataloging the genomic binding patterns of DNA-associated proteins (DAPs), such as transcription factors (TFs). However, most chromatin immunoprecipitation-sequencing (ChIP-seq) analyses have focused on a few immortalized cell lines whose activities and physiology differ in important ways from endogenous cells and tissues. Consequently, binding data from primary human tissue are essential to improving our understanding of in vivo gene regulation. Here, we identify and analyze more than 440,000 binding sites using ChIP-seq data for 20 DAPs in two human liver tissue samples. We integrated binding data with transcriptome and phased WGS data to investigate allelic DAP interactions and the impact of heterozygous sequence variation on the expression of neighboring genes. Our tissue-based data set exhibits binding patterns more consistent with liver biology than cell lines, and we describe uses of these data to better prioritize impactful noncoding variation. Collectively, our rich data set offers novel insights into genome function in human liver tissue and provides a valuable resource for assessing disease-related disruptions.

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“…Our method is developed based on the established strategy for conducting allele-specific analysis on many types of sequencing data, which specifically focus on the sequencing reads on heterozygous loci. This includes allele-specific gene expression ( Zhang et al, 2014 ; Castel et al, 2015 ), allele-specific alternative splicing analysis ( Nembaware et al, 2008 ; Li et al, 2012 ), allele-specific binding of ChIP-seq ( de Santiago et al, 2017 ) and CLIP-seq data analysis ( Yang et al, 2019 ), allele-specific chromatin interaction ( Cavalli et al, 2019 ), and allele-specific chromatin accessibility ( Harvey et al, 2014 ; Zhang et al, 2019 ). To our knowledge, our method is the first to analyze allele-specific footprint.…”

Section: Discussionmentioning

confidence: 99%

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Feng

et al. 2020

Front. Bioeng. Biotechnol.

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Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposasecleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of

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

Cited by 29 publications

References 48 publications

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

A genome-wide interactome of DNA-associated proteins in the human liver

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

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