Dataset Information

SNPhood: investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data.

ABSTRACT: The vast majority of the many thousands of disease-associated single nucleotide polymorphisms (SNPs) lie in the non-coding part of the genome. They are likely to affect regulatory elements, such as enhancers and promoters, rather than the function of a protein. To understand the molecular mechanisms underlying genetic diseases, it is therefore increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin or transcription factor binding.We developed SNPhood, a user-friendly Bioconductor R package to investigate, quantify and visualise the local epigenetic neighbourhood of a set of SNPs in terms of chromatin marks or TF binding sites using data from NGS experiments.SNPhood is publicly available and maintained as an R Bioconductor package at http://bioconductor.org/packages/SNPhood/judith.zaugg@embl.deSupplementary data are available at Bioinformatics online.

SUBMITTER: Arnold C

PROVIDER: S-EPMC4965630 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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SNPhood: investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data.

Arnold Christian C Bhat Pooja P Zaugg Judith B JB

Bioinformatics (Oxford, England) 20160326 15

<h4>Motivation</h4>The vast majority of the many thousands of disease-associated single nucleotide polymorphisms (SNPs) lie in the non-coding part of the genome. They are likely to affect regulatory elements, such as enhancers and promoters, rather than the function of a protein. To understand the molecular mechanisms underlying genetic diseases, it is therefore increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin or transcription factor binding.<h4>Re ...[more]

PMID: 27153574

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Dataset Information

SNPhood: investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data.

Publications

SNPhood: investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data.

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