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Bioinformatics. 2019 Jul 15;35(14):i145-i153. doi: 10.1093/bioinformatics/btz362.

Selfish: discovery of differential chromatin interactions via a self-similarity measure.

Author information

1
Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, USA.
2
Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, UC San Diego, La Jolla, CA, USA.
3
School of Medicine, Department of Pediatrics, UC San Diego, La Jolla, CA, USA.

Abstract

MOTIVATION:

High-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.

RESULTS:

We present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION:

https://github.com/ucrbioinfo/Selfish.

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