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Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints

In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specifi...

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Autores principales: Schwessinger, Ron, Suciu, Maria C., McGowan, Simon J., Telenius, Jelena, Taylor, Stephen, Higgs, Doug R., Hughes, Jim R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630036/
https://www.ncbi.nlm.nih.gov/pubmed/28904015
http://dx.doi.org/10.1101/gr.220202.117
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author Schwessinger, Ron
Suciu, Maria C.
McGowan, Simon J.
Telenius, Jelena
Taylor, Stephen
Higgs, Doug R.
Hughes, Jim R.
author_facet Schwessinger, Ron
Suciu, Maria C.
McGowan, Simon J.
Telenius, Jelena
Taylor, Stephen
Higgs, Doug R.
Hughes, Jim R.
author_sort Schwessinger, Ron
collection PubMed
description In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k-mer-based analysis of DNase footprints to determine any k-mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome.
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spelling pubmed-56300362018-04-01 Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints Schwessinger, Ron Suciu, Maria C. McGowan, Simon J. Telenius, Jelena Taylor, Stephen Higgs, Doug R. Hughes, Jim R. Genome Res Method In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k-mer-based analysis of DNase footprints to determine any k-mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome. Cold Spring Harbor Laboratory Press 2017-10 /pmc/articles/PMC5630036/ /pubmed/28904015 http://dx.doi.org/10.1101/gr.220202.117 Text en © 2017 Schwessinger et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Method
Schwessinger, Ron
Suciu, Maria C.
McGowan, Simon J.
Telenius, Jelena
Taylor, Stephen
Higgs, Doug R.
Hughes, Jim R.
Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
title Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
title_full Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
title_fullStr Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
title_full_unstemmed Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
title_short Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints
title_sort sasquatch: predicting the impact of regulatory snps on transcription factor binding from cell- and tissue-specific dnase footprints
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630036/
https://www.ncbi.nlm.nih.gov/pubmed/28904015
http://dx.doi.org/10.1101/gr.220202.117
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