Paper Info
Title
Coolpup.py: versatile pile-up analysis of Hi-C data.
Abstract
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.
Year
DOI
Venue
2020
10.1093/bioinformatics/btaa073
BIOINFORMATICS
DocType
Volume
Issue
Journal
36
10
ISSN
Citations 
PageRank 
1367-4803
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Ilya M Flyamer100.34
Robert S Illingworth200.34
W Bickmore300.68