scholarly journals Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Li Zhou ◽  
Hong Kiat Ng ◽  
Daniela I. Drautz-Moses ◽  
Stephan C. Schuster ◽  
Stephan Beck ◽  
...  
2017 ◽  
Author(s):  
Nelly Olova ◽  
Felix Krueger ◽  
Simon Andrews ◽  
David Oxley ◽  
Rebecca V. Berrens ◽  
...  

AbstractBackgroundWhole-genome bisulfite sequencing (WGBS) is becoming an increasingly accessible technique, used widely for both fundamental and disease-oriented research. Library preparation methods benefit from a variety of available kits, polymerases and bisulfite conversion protocols. Although some steps in the procedure, such as PCR amplification, are known to introduce biases, a systematic evaluation of biases in WGBS strategies is missing.ResultsWe perform a comparative analysis of several commonly used pre-and post-bisulfite WGBS library preparation protocols for their performance and quality of sequencing outputs. Our results show that bisulfite conversion per se is the main trigger of pronounced sequencing biases, and PCR amplification builds on these underlying artefacts. The majority of standard library preparation methods yield a significantly biased sequence output and overestimate global methylation. Importantly, both absolute and relative methylation levels at specific genomic regions vary substantially between methods, with clear implications for DNA methylation studies.ConclusionsWe show that amplification-free library preparation is the least biased approach for WGBS. In protocols with amplification, the choice of BS conversion protocol or polymerase can significantly minimize artefacts. To aid with the quality assessment of existing WGBS datasets, we have integrated a bias diagnostic tool in the Bismark package and offer several approaches for consideration during the preparation and analysis of WGBS datasets.


2015 ◽  
Vol 10 (3) ◽  
pp. 475-483 ◽  
Author(s):  
Mark A Urich ◽  
Joseph R Nery ◽  
Ryan Lister ◽  
Robert J Schmitz ◽  
Joseph R Ecker

2019 ◽  
Author(s):  
Yadollah Shahryary ◽  
Rashmi R. Hazarika ◽  
Frank Johannes

AbstractBackground: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 Arabidopsis shows that it outperforms existing pre-processing pipelines in terms of speed and memory requirements.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


2012 ◽  
Vol 41 (4) ◽  
pp. e55-e55 ◽  
Author(s):  
Touati Benoukraf ◽  
Sarawut Wongphayak ◽  
Luqman Hakim Abdul Hadi ◽  
Mengchu Wu ◽  
Richie Soong

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