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Coolpup.py: versatile pile-up analysis of Hi-C data

MOTIVATION: Hi-C is currently the method of choice to investigate the global 3D organization of the genome. A major limitation of Hi-C is the sequencing depth required to robustly detect loops in the data. A popular approach used to mitigate this issue, even in single-cell Hi-C data, is genome-wide...

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Detalles Bibliográficos
Autores principales: Flyamer, Ilya M, Illingworth, Robert S, Bickmore, Wendy A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214034/
https://www.ncbi.nlm.nih.gov/pubmed/32003791
http://dx.doi.org/10.1093/bioinformatics/btaa073
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author Flyamer, Ilya M
Illingworth, Robert S
Bickmore, Wendy A
author_facet Flyamer, Ilya M
Illingworth, Robert S
Bickmore, Wendy A
author_sort Flyamer, Ilya M
collection PubMed
description MOTIVATION: Hi-C is currently the method of choice to investigate the global 3D organization of the genome. A major limitation of Hi-C is the sequencing depth required to robustly detect loops in the data. A popular approach used to mitigate this issue, even in single-cell Hi-C data, is genome-wide averaging (piling-up) of peaks, or other features, annotated in high-resolution datasets, to measure their prominence in less deeply sequenced data. However, current tools do not provide a computationally efficient and versatile implementation of this approach. RESULTS: Here, we describe coolpup.py—a versatile tool to perform pile-up analysis on Hi-C data. We demonstrate its utility by replicating previously published findings regarding the role of cohesin and CTCF in 3D genome organization, as well as discovering novel details of Polycomb-driven interactions. We also present a novel variation of the pile-up approach that can aid the statistical analysis of looping interactions. We anticipate that coolpup.py will aid in Hi-C data analysis by allowing easy to use, versatile and efficient generation of pile-ups. AVAILABILITY AND IMPLEMENTATION: Coolpup.py is cross-platform, open-source and free (MIT licensed) software. Source code is available from https://github.com/Phlya/coolpuppy and it can be installed from the Python Packaging Index.
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spelling pubmed-72140342020-05-15 Coolpup.py: versatile pile-up analysis of Hi-C data Flyamer, Ilya M Illingworth, Robert S Bickmore, Wendy A Bioinformatics Original Papers MOTIVATION: Hi-C is currently the method of choice to investigate the global 3D organization of the genome. A major limitation of Hi-C is the sequencing depth required to robustly detect loops in the data. A popular approach used to mitigate this issue, even in single-cell Hi-C data, is genome-wide averaging (piling-up) of peaks, or other features, annotated in high-resolution datasets, to measure their prominence in less deeply sequenced data. However, current tools do not provide a computationally efficient and versatile implementation of this approach. RESULTS: Here, we describe coolpup.py—a versatile tool to perform pile-up analysis on Hi-C data. We demonstrate its utility by replicating previously published findings regarding the role of cohesin and CTCF in 3D genome organization, as well as discovering novel details of Polycomb-driven interactions. We also present a novel variation of the pile-up approach that can aid the statistical analysis of looping interactions. We anticipate that coolpup.py will aid in Hi-C data analysis by allowing easy to use, versatile and efficient generation of pile-ups. AVAILABILITY AND IMPLEMENTATION: Coolpup.py is cross-platform, open-source and free (MIT licensed) software. Source code is available from https://github.com/Phlya/coolpuppy and it can be installed from the Python Packaging Index. Oxford University Press 2020-05-15 2020-01-31 /pmc/articles/PMC7214034/ /pubmed/32003791 http://dx.doi.org/10.1093/bioinformatics/btaa073 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Flyamer, Ilya M
Illingworth, Robert S
Bickmore, Wendy A
Coolpup.py: versatile pile-up analysis of Hi-C data
title Coolpup.py: versatile pile-up analysis of Hi-C data
title_full Coolpup.py: versatile pile-up analysis of Hi-C data
title_fullStr Coolpup.py: versatile pile-up analysis of Hi-C data
title_full_unstemmed Coolpup.py: versatile pile-up analysis of Hi-C data
title_short Coolpup.py: versatile pile-up analysis of Hi-C data
title_sort coolpup.py: versatile pile-up analysis of hi-c data
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214034/
https://www.ncbi.nlm.nih.gov/pubmed/32003791
http://dx.doi.org/10.1093/bioinformatics/btaa073
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