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MICC: an R package for identifying chromatin interactions from ChIA-PET data

Summary: ChIA-PET is rapidly emerging as an important experimental approach to detect chromatin long-range interactions at high resolution. Here, we present Model based Interaction Calling from ChIA-PET data (MICC), an easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing d...

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Detalles Bibliográficos
Autores principales: He, Chao, Zhang, Michael Q., Wang, Xiaowo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653385/
https://www.ncbi.nlm.nih.gov/pubmed/26231426
http://dx.doi.org/10.1093/bioinformatics/btv445
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author He, Chao
Zhang, Michael Q.
Wang, Xiaowo
author_facet He, Chao
Zhang, Michael Q.
Wang, Xiaowo
author_sort He, Chao
collection PubMed
description Summary: ChIA-PET is rapidly emerging as an important experimental approach to detect chromatin long-range interactions at high resolution. Here, we present Model based Interaction Calling from ChIA-PET data (MICC), an easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate. Availability and implementation: http://bioinfo.au.tsinghua.edu.cn/member/xwwang/MICCusage Contact: michael.zhang@utdallas.edu or xwwang@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-46533852015-11-20 MICC: an R package for identifying chromatin interactions from ChIA-PET data He, Chao Zhang, Michael Q. Wang, Xiaowo Bioinformatics Applications Notes Summary: ChIA-PET is rapidly emerging as an important experimental approach to detect chromatin long-range interactions at high resolution. Here, we present Model based Interaction Calling from ChIA-PET data (MICC), an easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate. Availability and implementation: http://bioinfo.au.tsinghua.edu.cn/member/xwwang/MICCusage Contact: michael.zhang@utdallas.edu or xwwang@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-12-01 2015-07-31 /pmc/articles/PMC4653385/ /pubmed/26231426 http://dx.doi.org/10.1093/bioinformatics/btv445 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
He, Chao
Zhang, Michael Q.
Wang, Xiaowo
MICC: an R package for identifying chromatin interactions from ChIA-PET data
title MICC: an R package for identifying chromatin interactions from ChIA-PET data
title_full MICC: an R package for identifying chromatin interactions from ChIA-PET data
title_fullStr MICC: an R package for identifying chromatin interactions from ChIA-PET data
title_full_unstemmed MICC: an R package for identifying chromatin interactions from ChIA-PET data
title_short MICC: an R package for identifying chromatin interactions from ChIA-PET data
title_sort micc: an r package for identifying chromatin interactions from chia-pet data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653385/
https://www.ncbi.nlm.nih.gov/pubmed/26231426
http://dx.doi.org/10.1093/bioinformatics/btv445
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