Evidence map›Paper›PMID 32003791›Full record

ArticleBioinformatics (Oxford, England)2020

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

Ilya M Flyamer, Robert S Illingworth, Wendy A Bickmore

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers.

0numbers the graph read from it
0cells of the map it votes in
146citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

146 citing papers in PubMed.

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  9. CAD-C: An engineered nuclease enables repair-freebioRxiv : the preprint server for biology · 2026
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86 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ilya M FlyamerMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, EH4 2XU, UK.
Robert S IllingworthMRC Centre for Regenerative Medicine, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, EH16 4UU, UK.
Wendy A BickmoreMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, EH4 2XU, UK.

Funding

Medical Research Council MC_UU_00007/2Medical Research Council MR/K017047/1Medical Research Council MR/S007644/1
6 · The paper itself

Abstract

motivationHi-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.

resultsHere, 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.

Indexed as

ChromatinGenomicsGenomeSoftwareChromatin

Identifiers

PMID32003791
PMCPMC7214034

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.