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Cited by 35Genomics and Chromatin DynamicsGenetic Associations and EpidemiologyGenomics and Phylogenetic Studies

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

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Schwessinger, Ron, Suciu, Maria C., McGowan, Simon J., Telenius, Jelena, Taylor, Stephen, Higgs, Doug R., Hughes, Jim R.·2017·Genome Research
DOI 10.1101/gr.220202.117PMC PMC5630036
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Abstract

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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  • R2[R4], R1R2R3[R4] and R1R2HS1[R4] donors were screened with Sasquatch and JASPAR to ensure no novel accessibility sites or motifs were predicted at the newly created junctions.

    “R2[R4], R1R2R3[R4] and R1R2HS1[R4] donors were screened with Sasquatch 43and JASPAR42,43 to ensure no novel accessibility sites or motifs were predicted at the newly created junctions,43,44 prior to synthesis and transfection.”

    From Super-enhancers include classical enhancers and facilitators to fully activate gene expression (2023) · cites this paper as [44]

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Authors
Schwessinger, R., Suciu, M. C., McGowan, S. J., Telenius, J., Taylor, S., Higgs, D. R., & Hughes, J. R.
Affiliations
MRC Weatherall Institute of Molecular Medicine (GB)
ORCID
Venue
Genome Research
Publisher
Cold Spring Harbor Laboratory
Published
2017-09-13
Volume
27
Issue
10
Pages
1730-1742
ISSN
  • 1088-9051 (ISSN-L)
  • 1549-5469
DOI
10.1101/gr.220202.117
PMC
PMC5630036
OpenAlex
W2755948321
Cited by
35
References
76
Citations by year
2025: 2 · 2024: 2 · 2023: 3 · 2022: 8 · 2021: 6 · 2020: 5 · 2019: 7 · 2018: 2
Type
posted content
Language
en
Open access
Licence
other-oa
Locations
  • Genome Research (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • Medical Research Council (MC_UU_12009/4)
  • Wellcome Trust (106130/Z/14/Z, 203728/Z/16/Z, 097309/Z/11/Z)
  • Institutional Strategic Support Fund (105605/Z/14/Z)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
regulatory single nucleotide polymorphisms, transcription factor binding, DNase-seq, non-coding variants, k-mer analysis, variant effect prediction, Sasquatch, ENCODE, web-based tool
MeSH terms
Deoxyribonucleases, Humans, Predictive Value of Tests, Transcription Factors, Sequence Analysis, DNA, DNA Footprinting, Response Elements, Polymorphism, Single Nucleotide, Erythroid Cells, Nucleotide Motifs

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