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Algorithmic considerations when analysing capture Hi-C data

Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological releva...

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Detalles Bibliográficos
Autores principales: Disney-Hogg, Linden, Kinnersley, Ben, Houlston, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699993/
https://www.ncbi.nlm.nih.gov/pubmed/36474805
http://dx.doi.org/10.12688/wellcomeopenres.16394.2
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author Disney-Hogg, Linden
Kinnersley, Ben
Houlston, Richard
author_facet Disney-Hogg, Linden
Kinnersley, Ben
Houlston, Richard
author_sort Disney-Hogg, Linden
collection PubMed
description Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promoter capture Hi-C data generated on 28 different cell lines as a case study.
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spelling pubmed-96999932022-12-05 Algorithmic considerations when analysing capture Hi-C data Disney-Hogg, Linden Kinnersley, Ben Houlston, Richard Wellcome Open Res Method Article Chromosome conformation capture methodologies have provided insight into the effect of 3D genomic architecture on gene regulation. Capture Hi-C (CHi-C) is a recent extension of Hi-C that improves the effective resolution of chromatin interactions by enriching for defined regions of biological relevance. The varying targeting efficiency between capture regions, however, introduces bias not present in conventional Hi-C, making analysis more complicated. Here we consider salient features of an algorithm that should be considered in evaluating the performance of a program used to analyse CHi-C data in order to infer meaningful interactions. We use the program CHICAGO to analyse promoter capture Hi-C data generated on 28 different cell lines as a case study. F1000 Research Limited 2022-11-11 /pmc/articles/PMC9699993/ /pubmed/36474805 http://dx.doi.org/10.12688/wellcomeopenres.16394.2 Text en Copyright: © 2022 Disney-Hogg L et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method Article
Disney-Hogg, Linden
Kinnersley, Ben
Houlston, Richard
Algorithmic considerations when analysing capture Hi-C data
title Algorithmic considerations when analysing capture Hi-C data
title_full Algorithmic considerations when analysing capture Hi-C data
title_fullStr Algorithmic considerations when analysing capture Hi-C data
title_full_unstemmed Algorithmic considerations when analysing capture Hi-C data
title_short Algorithmic considerations when analysing capture Hi-C data
title_sort algorithmic considerations when analysing capture hi-c data
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699993/
https://www.ncbi.nlm.nih.gov/pubmed/36474805
http://dx.doi.org/10.12688/wellcomeopenres.16394.2
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