GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding

AbstractAllele-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 to detect 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 6 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.

Download Full-text

Positional weight matrices have sufficient prediction power for analysis of noncoding variants

F1000Research ◽

10.12688/f1000research.75471.1 ◽

2022 ◽

Vol 11 ◽

pp. 33

Author(s):

Alexandr Boytsov ◽

Sergey Abramov ◽

Vsevolod J. Makeev ◽

Ivan V. Kulakovskiy

Keyword(s):

Transcription Factor ◽

Transcription Factor Binding ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Factor Binding ◽

Regulatory Variants ◽

Scoring Matrix ◽

Regulatory Potential ◽

The Rich ◽

Positional Weight

The commonly accepted model to quantify the specificity of transcription factor binding to DNA is the position weight matrix, also called the position-specific scoring matrix. Position weight matrices are used in thousands of projects and computational tools in regulatory genomics, including prediction of the regulatory potential of single-nucleotide variants. Yet, recently Yan et al. presented new experimental method for analysis of regulatory variants and, based on its results, reported that "the position weight matrices of most transcription factors lack sufficient predictive power". Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can successfully quantify transcription factor binding to alternative alleles.

Download Full-text

Interplay of cis and trans mechanisms driving transcription factor binding and gene expression evolution

10.1101/059873 ◽

2016 ◽

Author(s):

Emily S Wong ◽

Bianca M Schmitt ◽

Anastasiya Kazachenka ◽

David Thybert ◽

Aisling Redmond ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Inbred Mouse ◽

Transcription Factor Binding ◽

Regulatory Mechanisms ◽

Mouse Strains ◽

Cis Acting ◽

Factor Binding ◽

Regulatory Variants ◽

Transcription Factor Occupancy

AbstractNoncoding regulatory variants play a central role in the genetics of human diseases and in evolution. Here we measure allele-specific transcription factor binding occupancy of three liver-specific transcription factors between crosses of two inbred mouse strains to elucidate the regulatory mechanisms underlying transcription factor binding variations in mammals. Our results highlight the pre-eminence of cis-acting variants on transcription factor occupancy divergence. Transcription factor binding differences linked to cis-acting variants generally exhibit additive inheritance, while those linked to trans-acting variants are most often dominantly inherited. Cis-acting variants lead to local coordination of transcription factor occupancies that decay with distance; distal coordination is also observed and may be modulated by long-range chromatin contacts. Our results reveal the regulatory mechanisms that interplay to drive transcription factor occupancy, chromatin state, and gene expression in complex mammalian cell states.

Download Full-text

GERV: A Statistical Method for Generative Evaluation of Regulatory Variants for Transcription Factor Binding

10.1101/017392 ◽

2015 ◽

Cited By ~ 1

Author(s):

Haoyang Zeng ◽

Tatsunori Hashimoto ◽

Daniel D. Kang ◽

David K. Gifford

Keyword(s):

Transcription Factor ◽

Specific Binding ◽

Association Studies ◽

Transcription Factor Binding ◽

Computational Method ◽

Breast Cancer Cell Lines ◽

Genome Wide Association Studies ◽

Factor Binding ◽

Regulatory Variants ◽

Causal Variants

The majority of disease-associated variants identified in genome-wide association studies (GWAS) reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of GWAS studies. We present GERV (Generative Evaluation of Regulatory Variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor's canonical motif as well as associated co-factor motifs. We show that GERV outperforms existing methods in predicting SNPs associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked SNPs, and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis.

Download Full-text