MethylStar: A fast and robust pre-processing pipeline for bulk or single-cell whole-genome bisulfite sequencing data.

Autor: Shahryary Y; Technical University of Munich, Institute for Advanced Study (IAS), Lichtenbergstr. 2a, Garching, 85748, Germany.; Technical University of Munich, Department of Plant Sciences, Liesel-Beckmann-Str. 2, Freising, 85354, Germany., Hazarika RR; Technical University of Munich, Institute for Advanced Study (IAS), Lichtenbergstr. 2a, Garching, 85748, Germany.; Technical University of Munich, Department of Plant Sciences, Liesel-Beckmann-Str. 2, Freising, 85354, Germany., Johannes F; Technical University of Munich, Institute for Advanced Study (IAS), Lichtenbergstr. 2a, Garching, 85748, Germany. frank@johanneslab.org.; Technical University of Munich, Department of Plant Sciences, Liesel-Beckmann-Str. 2, Freising, 85354, Germany. frank@johanneslab.org.
Jazyk: angličtina
Zdroj: BMC genomics [BMC Genomics] 2020 Jul 13; Vol. 21 (1), pp. 479. Date of Electronic Publication: 2020 Jul 13.
DOI: 10.1186/s12864-020-06886-3
Abstrakt: Background: Whole-Genome Bisulfite Sequencing (WGBS) is a Next Generation Sequencing (NGS) technique for measuring DNA methylation at base resolution. Continuing drops in sequencing costs are beginning to enable high-throughput surveys of DNA methylation in large samples of individuals and/or single cells. These surveys can easily generate hundreds or even thousands of WGBS datasets in a single study. The efficient pre-processing of these large amounts of data poses major computational challenges and creates unnecessary bottlenecks for downstream analysis and biological interpretation.
Results: To offer an efficient analysis solution, we present MethylStar, a fast, stable and flexible pre-processing pipeline for WGBS data. MethylStar integrates well-established tools for read trimming, alignment and methylation state calling in a highly parallelized environment, manages computational resources and performs automatic error detection. MethylStar offers easy installation through a dockerized container with all preloaded dependencies and also features a user-friendly interface designed for experts/non-experts. Application of MethylStar to WGBS from Human, Maize and A. thaliana shows favorable performance in terms of speed and memory requirements compared with existing pipelines.
Conclusions: MethylStar is a fast, stable and flexible pipeline for high-throughput pre-processing of bulk or single-cell WGBS data. Its easy installation and user-friendly interface should make it a useful resource for the wider epigenomics community. MethylStar is distributed under GPL-3.0 license and source code is publicly available for download from github https://github.com/jlab-code/MethylStar . Installation through a docker image is available from http://jlabdata.org/methylstar.tar.gz.
Databáze: MEDLINE
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