versatile pile-up analysis of Hi-C data

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Hi-C is currently the method of choice to investigate the global 3D organisation 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.

Here we describe - 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 in statistical analysis of looping interactions. We anticipate that will aid in Hi-C data analysis by allowing easy to use, versatile and efficient generation of pileups.

AVAILABILITY: is cross-platform, open-source and free (MIT licensed) software. Source code is available from and it can be installed from the Python Packaging Index.
Original languageEnglish
Early online date31 Jan 2020
Publication statusE-pub ahead of print - 31 Jan 2020


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