Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data

Author:

Fischer Jonas12ORCID,Schulz Marcel H2345ORCID

Affiliation:

1. Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, MA 02115, United States

2. Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics , Saarbrücken 66123, Germany

3. Institute of Cardiovascular Regeneration, Department of Medicine, Goethe University , Frankfurt am Main 60590, Germany

4. Cardio-Pulmonary Institute, Goethe University , Frankfurt am Main, Germany

5. German Centre for Cardiovascular Research, Partner Site Rhein-Main , Frankfurt am Main 60590, Germany

Abstract

Abstract Motivation DNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the mammalian gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding. Results We present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy. Availability and implementation An implementation of FAME is open source and licensed under GPL-3.0 at https://github.com/FischerJo/FAME.

Funder

German Centre for Cardiovascular Research

National Cancer Institute

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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