Serpentine: a flexible 2D binning method for differential Hi-C analysis

Author:

Baudry Lyam12,Millot Gaël A3,Thierry Agnes1,Koszul Romain1ORCID,Scolari Vittore F1ORCID

Affiliation:

1. Institut Pasteur, Unité Régulation Spatiale des Génomes, UMR3525 CNRS, Paris 75015, France

2. Sorbonne Université, Collège Doctoral, Paris 75005, France

3. Département Biologie Computationnelle, Hub de Bioinformatique et Biostatistique, Institut Pasteur, USR 3756 CNRS, Paris 75015, France

Abstract

Abstract Motivation Hi-C contact maps reflect the relative contact frequencies between pairs of genomic loci, quantified through deep sequencing. Differential analyses of these maps enable downstream biological interpretations. However, the multi-fractal nature of the chromatin polymer inside the cellular envelope results in contact frequency values spanning several orders of magnitude: contacts between loci pairs separated by large genomic distances are much sparser than closer pairs. The same is true for poorly covered regions, such as repeated sequences. Both distant and poorly covered regions translate into low signal-to-noise ratios. There is no clear consensus to address this limitation. Results We present Serpentine, a fast, flexible procedure operating on raw data, which considers the contacts in each region of a contact map. Binning is performed only when necessary on noisy regions, preserving informative ones. This results in high-quality, low-noise contact maps that can be conveniently visualized for rigorous comparative analyses. Availability and implementation Serpentine is available on the PyPI repository and https://github.com/koszullab/serpentine; documentation and tutorials are provided at https://serpentine.readthedocs.io/en/latest/. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

European Research Council

Horizon 2020 Program

Pasteur Roux Cantarini fellowship

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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