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

Ilya M Flyamer,Robert S Illingworth,Wendy A Bickmore,Peter Robinson

doi:10.1093/bioinformatics/btaa073

Ilya M Flyamer, Robert S Illingworth + Show 2 more

Open Access

https://doi.org/10.1093/bioinformatics/btaa073

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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.

Highlights

Major advances in the study of 3D genome organization have come from the development of a family of Chromosome Conformation Capture (3C) methods (Dekker et al, 2002)
The output of Hi-C is a square matrix of interactions and requires a vastly greater sequencing depth than most sequencing-based approaches that look for enrichment of reads linearly along the genome (Lajoie et al, 2014)
Using published single-cell Hi-C data we investigate the dynamics of polycomb-associated looping revealing a different dynamics of looping across the cell cycle compared with CTCF loops

Summary

Introduction

Major advances in the study of 3D genome organization have come from the development of a family of Chromosome Conformation Capture (3C) methods (Dekker et al, 2002). While these all rely on the same principle of in situ proximity ligation of crosslinked and digested chromatin, the scope of each method varies depending on experimental processing and the method of quantification of the 3C library (Barutcu et al, 2016). The output of Hi-C is a square matrix of interactions and requires a vastly greater sequencing depth than most sequencing-based approaches that look for enrichment of reads linearly along the genome (Lajoie et al, 2014). This limits the resolution at which genomes can be analysed in 3D, since going beyond ~5 kbp resolution requires billions of read pairs for a mammalian genome

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