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SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs
Genetic variations of the human genome are linked to many disease phenotypes. While whole-genome sequencing and genome-wide association studies (GWAS) have uncovered a number of genotype-phenotype associations, their functional interpretation remains challenging given most single nucleotide polymorp...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417821/ https://www.ncbi.nlm.nih.gov/pubmed/30864321 |
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author | Anand, Shankara Kalesinskas, Laurynas Smail, Craig Tanigawa, Yosuke |
author_facet | Anand, Shankara Kalesinskas, Laurynas Smail, Craig Tanigawa, Yosuke |
author_sort | Anand, Shankara |
collection | PubMed |
description | Genetic variations of the human genome are linked to many disease phenotypes. While whole-genome sequencing and genome-wide association studies (GWAS) have uncovered a number of genotype-phenotype associations, their functional interpretation remains challenging given most single nucleotide polymorphisms (SNPs) fall into the non-coding region of the genome. Advances in chromatin immunoprecipitation sequencing (ChIP-seq) have made large-scale repositories of epigenetic data available, allowing investigation of coordinated mechanisms of epigenetic markers and transcriptional regulation and their influence on biological function. To address this, we propose SNPs2ChIP, a method to infer biological functions of non-coding variants through unsupervised statistical learning methods applied to publicly-available epigenetic datasets. We systematically characterized latent factors by applying singular value decomposition to ChIP-seq tracks of lymphoblastoid cell lines, and annotated the biological function of each latent factor using the genomic region enrichment analysis tool. Using these annotated latent factors as reference, we developed SNPs2ChIP, a pipeline that takes genomic region(s) as an input, identifies the relevant latent factors with quantitative scores, and returns them along with their inferred functions. As a case study, we focused on systemic lupus erythematosus and demonstrated our method’s ability to infer relevant biological function. We systematically applied SNPs2ChIP on publicly available datasets, including known GWAS associations from the GWAS catalogue and ChIP-seq peaks from a previously published study. Our approach to leverage latent patterns across genome-wide epigenetic datasets to infer the biological function will advance understanding of the genetics of human diseases by accelerating the interpretation of non-coding genomes. |
format | Online Article Text |
id | pubmed-6417821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
spelling | pubmed-64178212019-03-14 SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs Anand, Shankara Kalesinskas, Laurynas Smail, Craig Tanigawa, Yosuke Pac Symp Biocomput Article Genetic variations of the human genome are linked to many disease phenotypes. While whole-genome sequencing and genome-wide association studies (GWAS) have uncovered a number of genotype-phenotype associations, their functional interpretation remains challenging given most single nucleotide polymorphisms (SNPs) fall into the non-coding region of the genome. Advances in chromatin immunoprecipitation sequencing (ChIP-seq) have made large-scale repositories of epigenetic data available, allowing investigation of coordinated mechanisms of epigenetic markers and transcriptional regulation and their influence on biological function. To address this, we propose SNPs2ChIP, a method to infer biological functions of non-coding variants through unsupervised statistical learning methods applied to publicly-available epigenetic datasets. We systematically characterized latent factors by applying singular value decomposition to ChIP-seq tracks of lymphoblastoid cell lines, and annotated the biological function of each latent factor using the genomic region enrichment analysis tool. Using these annotated latent factors as reference, we developed SNPs2ChIP, a pipeline that takes genomic region(s) as an input, identifies the relevant latent factors with quantitative scores, and returns them along with their inferred functions. As a case study, we focused on systemic lupus erythematosus and demonstrated our method’s ability to infer relevant biological function. We systematically applied SNPs2ChIP on publicly available datasets, including known GWAS associations from the GWAS catalogue and ChIP-seq peaks from a previously published study. Our approach to leverage latent patterns across genome-wide epigenetic datasets to infer the biological function will advance understanding of the genetics of human diseases by accelerating the interpretation of non-coding genomes. 2019 /pmc/articles/PMC6417821/ /pubmed/30864321 Text en Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License. http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Anand, Shankara Kalesinskas, Laurynas Smail, Craig Tanigawa, Yosuke SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs |
title | SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of
non-coding SNPs |
title_full | SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of
non-coding SNPs |
title_fullStr | SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of
non-coding SNPs |
title_full_unstemmed | SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of
non-coding SNPs |
title_short | SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of
non-coding SNPs |
title_sort | snps2chip: latent factors of chip-seq to infer functions of
non-coding snps |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417821/ https://www.ncbi.nlm.nih.gov/pubmed/30864321 |
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