» Articles » PMID: 25777524

WGBSSuite: Simulating Whole-genome Bisulphite Sequencing Data and Benchmarking Differential DNA Methylation Analysis Tools

Overview
Journal Bioinformatics
Specialty Biology
Date 2015 Mar 18
PMID 25777524
Citations 16
Authors
Affiliations
Soon will be listed here.
Abstract

Motivation: As the number of studies looking at differences between DNA methylation increases, there is a growing demand to develop and benchmark statistical methods to analyse these data. To date no objective approach for the comparison of these methods has been developed and as such it remains difficult to assess which analysis tool is most appropriate for a given experiment. As a result, there is an unmet need for a DNA methylation data simulator that can accurately reproduce a wide range of experimental setups, and can be routinely used to compare the performance of different statistical models.

Results: We have developed WGBSSuite, a flexible stochastic simulation tool that generates single-base resolution DNA methylation data genome-wide. Several simulator parameters can be derived directly from real datasets provided by the user in order to mimic real case scenarios. Thus, it is possible to choose the most appropriate statistical analysis tool for a given simulated design. To show the usefulness of our simulator, we also report a benchmark of commonly used methods for differential methylation analysis.

Availability And Implementation: WGBS code and documentation are available under GNU licence at http://www.wgbssuite.org.uk/

Contact: : owen.rackham@imperial.ac.uk or l.bottolo@imperial.ac.uk

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

Calibrating epigenetic clocks with training data error.

Mayne B, Berry O, Jarman S Evol Appl. 2023; 16(8):1496-1502.

PMID: 37622096 PMC: 10445086. DOI: 10.1111/eva.13582.


Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms.

Budkina A, Medvedeva Y, Stupnikov A Int J Mol Sci. 2023; 24(10).

PMID: 37239934 PMC: 10218268. DOI: 10.3390/ijms24108591.


Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate.

Peters T, Buckley M, Chen Y, Smyth G, Goodnow C, Clark S Nucleic Acids Res. 2021; 49(19):e109.

PMID: 34320181 PMC: 8565305. DOI: 10.1093/nar/gkab637.


Potential evidence for transgenerational epigenetic memory in Arabidopsis thaliana following spaceflight.

Xu P, Chen H, Hu J, Cai W Commun Biol. 2021; 4(1):835.

PMID: 34215844 PMC: 8253727. DOI: 10.1038/s42003-021-02342-4.


Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify between-group differences in DNA methylation.

Vellame D, Castanho I, Dahir A, Mill J, Hannon E BMC Genomics. 2021; 22(1):446.

PMID: 34126923 PMC: 8204428. DOI: 10.1186/s12864-021-07721-z.


References
1.
Jones P . Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012; 13(7):484-92. DOI: 10.1038/nrg3230. View

2.
Li S, Garrett-Bakelman F, Akalin A, Zumbo P, Levine R, To B . An optimized algorithm for detecting and annotating regional differential methylation. BMC Bioinformatics. 2013; 14 Suppl 5:S10. PMC: 3622633. DOI: 10.1186/1471-2105-14-S5-S10. View

3.
Akalin A, Kormaksson M, Li S, Garrett-Bakelman F, Figueroa M, Melnick A . methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012; 13(10):R87. PMC: 3491415. DOI: 10.1186/gb-2012-13-10-r87. View

4.
Hansen K, Langmead B, Irizarry R . BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 2012; 13(10):R83. PMC: 3491411. DOI: 10.1186/gb-2012-13-10-r83. View

5.
Barrero M, Boue S, Izpisua Belmonte J . Epigenetic mechanisms that regulate cell identity. Cell Stem Cell. 2010; 7(5):565-70. DOI: 10.1016/j.stem.2010.10.009. View