HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

Kulakovskiy, Ivan V.; Vorontsov, Ilya E.; Yevshin, Ivan S.; Soboleva, Anastasiia V.; Kasianov, Artem S.; Ashoor, Haitham; Ba-Alawi, Wail; Bajic, Vladimir B.; Medvedeva, Yulia A.; Kolpakov, Fedor A.; Makeev, Vsevolod J.

doi:10.1093/nar/gkv1249

Cited by 227 publications

(243 citation statements)

References 45 publications

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“…Additionally, the GWAS annotation was expanded to other SNPs using the tag SNP and linkage disequilibrium (r 2 > 0.8 and a maximum distance of 500 kb). The SNPs were mapped to transcription factor-binding sites (TFBSs) using an in-house TFBS mapping tool tfbsFrag, which integrates Jaspar (Mathelier et al, 2016), CIS-BP (Weirauch et al, 2014), SwissRegulon (Pachkov et al, 2013), HOCOMOCO (Kulakovskiy et al, 2016) and UniPROBE (Hume et al, 2015) databases of TFBSs. SNPDelScore backend is comprised of a set of in-house python scripts and a modular designed MS-SQL database.…”

Section: Methodsmentioning

confidence: 99%

SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome

Alvarez

Landsman

et al. 2017

Bioinformatics

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Summary: Addressing deleterious effects of noncoding mutations is an essential step towards the identification of disease-causal mutations of gene regulatory elements. Several methods for quantifying the deleteriousness of noncoding mutations using artificial intelligence, deep learning and other approaches have been recently proposed. Although the majority of the proposed methods have demonstrated excellent accuracy on different test sets, there is rarely a consensus. In addition, advanced statistical and artificial learning approaches used by these methods make it difficult porting these methods outside of the labs that have developed them. To address these challenges and to transform the methodological advances in predicting deleterious noncoding mutations into a practical resource available for the broader functional genomics and population genetics communities, we developed SNPDelScore, which uses a panel of proposed methods for quantifying deleterious effects of noncoding mutations to precompute and compare the deleteriousness scores of all common SNPs in the human genome in 44 cell lines. The panel of deleteriousness scores of a SNP computed using different methods is supplemented by functional information from the GWAS Catalog, libraries of transcription factor-binding sites, and genic characteristics of mutations. SNPDelScore comes with a genome browser capable of displaying and comparing large sets of SNPs in a genomic locus and rapidly identifying consensus SNPs with the highest deleteriousness scores making those prime candidates for phenotype-causal polymorphisms. Availability and implementation: https://www.ncbi.nlm.nih.gov/research/snpdelscore/ Contact: ovcharen@nih.gov

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Section: Methodsmentioning

confidence: 99%

SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome

Alvarez

Landsman

et al. 2017

Bioinformatics

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“…Accession numbers for all ENCODE data utilized are provided in Supplemental Table S1. Motif models were obtained from the HOCOMOCO v. 10 (Kulakovskiy et al 2013(Kulakovskiy et al , 2016 database and scanned against the genome. For complete details on the processing and remapping of these data sets, refer to the Supplemental Methods.…”

Section: Public Data Setsmentioning

confidence: 99%

Enhancer RNA profiling predicts transcription factor activity

et al. 2018

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Transcription factors (TFs) exert their regulatory influence through the binding of enhancers, resulting in coordination of gene expression programs. Active enhancers are often characterized by the presence of short, unstable transcripts termed enhancer RNAs (eRNAs). While their function remains unclear, we demonstrate that eRNAs are a powerful readout of TF activity. We infer sites of eRNA origination across hundreds of publicly available nascent transcription data sets and show that eRNAs initiate from sites of TF binding. By quantifying the colocalization of TF binding motif instances and eRNA origins, we derive a simple statistic capable of inferring TF activity. In doing so, we uncover dozens of previously unexplored links between diverse stimuli and the TFs they affect.

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“…We downloaded the set of non 116 redundant TFs for vertebrates from Jaspar (version of 26.10.2015) and vertebrate TF 117 data from Uniprobe. In addition, we obtained the full set of human mononucleotide 118 profiles from Hocomoco, version 10, which includes 641 pwms [38]. However, we found 119 this collection of pwms to perform worse than the other one (see discussion,…”

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confidence: 96%

“…38 Instead, the generated epigenetic data is considered to predict TF binding, as it contains 39 a wealth of information to simplify this task. Especially data on open-chromatin, as 40 produced for example by DNaseI seq [34], ATAC-seq [6] or NOMe-seq [35], was shown 41 to be well tailored for this purpose [47] and has become the standard for the analysis of 42 tissue-specific TF binding in absence of TF-ChIP data.…”

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confidence: 99%

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Schmidt

Gasparoni

et al. 2016

Preprint

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The binding and contribution of transcription factors (TF) to cell specific gene Using machine learning, we find low affinity binding sites to improve our ability to 10 explain gene expression variability compared to the standard presence/absence 11 classification of binding sites. Further, we show that both footprints and peaks capture 12 essential TF binding events and lead to a good prediction performance. In our nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly 15

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HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

Cited by 227 publications

References 45 publications

SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome

SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome

Enhancer RNA profiling predicts transcription factor activity

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

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