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Strategies for analyzing bisulfite sequencing data

Katarzyna Wreczycka, Alexander Gosdschan, Dilmurat Yusuf, Björn Grüening, Yassen Assenov, View ORCID ProfileAltuna Akalin
doi: https://doi.org/10.1101/109512
Katarzyna Wreczycka
1Bioinformatics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin
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Alexander Gosdschan
1Bioinformatics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin
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Dilmurat Yusuf
1Bioinformatics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin
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Björn Grüening
2Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg
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Yassen Assenov
3Division of Epigenomics and Cancer Risk Factors at the German Cancer Research Center (DKFZ), Heidelberg, Germany
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Altuna Akalin
1Bioinformatics Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin
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  • ORCID record for Altuna Akalin
  • For correspondence: altuna.akalin@mdc-berlin.de
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Abstract

DNA methylation is one of the main epigenetic modifications in the eukaryotic genome; it has been shown to play a role in cell-type specific regulation of gene expression, and therefore cell-type identity. Bisulfite sequencing is the gold-standard for measuring methylation over the genomes of interest. Here, we review several techniques used for the analysis of high-throughput bisulfite sequencing. We introduce specialized short-read alignment techniques as well as pre/post-alignment quality check methods to ensure data quality. Furthermore, we discuss subsequent analysis steps after alignment. We introduce various differential methylation methods and compare their performance using simulated and real bisulfite sequencing datasets. We also discuss the methods used to segment methylomes in order to pinpoint regulatory regions. We introduce annotation methods that can be used for further classification of regions returned by segmentation and differential methylation methods. Finally, we review software packages that implement strategies to efficiently deal with large bisulfite sequencing datasets locally and we discuss online analysis workflows that do not require any prior programming skills. The analysis strategies described in this review will guide researchers at any level to the best practices of bisulfite sequencing analysis.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 09, 2017.
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Strategies for analyzing bisulfite sequencing data
Katarzyna Wreczycka, Alexander Gosdschan, Dilmurat Yusuf, Björn Grüening, Yassen Assenov, Altuna Akalin
bioRxiv 109512; doi: https://doi.org/10.1101/109512
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Strategies for analyzing bisulfite sequencing data
Katarzyna Wreczycka, Alexander Gosdschan, Dilmurat Yusuf, Björn Grüening, Yassen Assenov, Altuna Akalin
bioRxiv 109512; doi: https://doi.org/10.1101/109512

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