HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential canc...

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Autores principales: Chen, Haojie, Tu, Shiqi, Yuan, Chongze, Tian, Feng, Zhang, Yijing, Sun, Yihua, Shao, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883642/
https://www.ncbi.nlm.nih.gov/pubmed/35227282
http://dx.doi.org/10.1186/s13059-022-02627-9
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author Chen, Haojie
Tu, Shiqi
Yuan, Chongze
Tian, Feng
Zhang, Yijing
Sun, Yihua
Shao, Zhen
author_facet Chen, Haojie
Tu, Shiqi
Yuan, Chongze
Tian, Feng
Zhang, Yijing
Sun, Yihua
Shao, Zhen
author_sort Chen, Haojie
collection PubMed
description Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true hypervariable regions on model fitting. A pan-cancer case study illustrates the practical utility of HyperChIP. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02627-9.
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spelling pubmed-88836422022-03-07 HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples Chen, Haojie Tu, Shiqi Yuan, Chongze Tian, Feng Zhang, Yijing Sun, Yihua Shao, Zhen Genome Biol Method Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true hypervariable regions on model fitting. A pan-cancer case study illustrates the practical utility of HyperChIP. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02627-9. BioMed Central 2022-02-28 /pmc/articles/PMC8883642/ /pubmed/35227282 http://dx.doi.org/10.1186/s13059-022-02627-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Chen, Haojie
Tu, Shiqi
Yuan, Chongze
Tian, Feng
Zhang, Yijing
Sun, Yihua
Shao, Zhen
HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples
title HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples
title_full HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples
title_fullStr HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples
title_full_unstemmed HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples
title_short HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples
title_sort hyperchip: identification of hypervariable signals across chip-seq or atac-seq samples
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8883642/
https://www.ncbi.nlm.nih.gov/pubmed/35227282
http://dx.doi.org/10.1186/s13059-022-02627-9
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