scholarly journals Landscape of allele-specific transcription factor binding in the human genome

Author(s):  
Sergey Abramov ◽  
Alexandr Boytsov ◽  
Dariia Bykova ◽  
Dmitry D. Penzar ◽  
Ivan Yevshin ◽  
...  

AbstractSequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sergey Abramov ◽  
Alexandr Boytsov ◽  
Daria Bykova ◽  
Dmitry D. Penzar ◽  
Ivan Yevshin ◽  
...  

AbstractSequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.


PLoS Genetics ◽  
2012 ◽  
Vol 8 (9) ◽  
pp. e1002982 ◽  
Author(s):  
Falk Butter ◽  
Lucy Davison ◽  
Tar Viturawong ◽  
Marion Scheibe ◽  
Michiel Vermeulen ◽  
...  

2016 ◽  
Vol 135 (5) ◽  
pp. 485-497 ◽  
Author(s):  
Marco Cavalli ◽  
Gang Pan ◽  
Helena Nord ◽  
Ola Wallerman ◽  
Emelie Wallén Arzt ◽  
...  

Genomics ◽  
2016 ◽  
Vol 107 (6) ◽  
pp. 248-254 ◽  
Author(s):  
Marco Cavalli ◽  
Gang Pan ◽  
Helena Nord ◽  
Emelie Wallén Arzt ◽  
Ola Wallerman ◽  
...  

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Ines de Santiago ◽  
Wei Liu ◽  
Ke Yuan ◽  
Martin O’Reilly ◽  
Chandra Sekhar Reddy Chilamakuri ◽  
...  

2018 ◽  
Author(s):  
Omar Wagih ◽  
Daniele Merico ◽  
Andrew Delong ◽  
Brendan J Frey

ABSTRACTGenetic 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 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 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.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sebastian Carrasco Pro ◽  
Katia Bulekova ◽  
Brian Gregor ◽  
Adam Labadorf ◽  
Juan Ignacio Fuxman Bass

An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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