A survey of the approaches for identifying differential methylation using bisulfite sequencing data

AbstractDNA 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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An evaluation of methods to test predefined genomic regions for differential methylation in bisulfite sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbv095 ◽

2015 ◽

Vol 17 (5) ◽

pp. 796-807 ◽

Cited By ~ 23

Author(s):

Hans-Ulrich Klein ◽

Katja Hebestreit

Keyword(s):

Bisulfite Sequencing ◽

Differential Methylation ◽

Sequencing Data ◽

Bisulfite Sequencing Data ◽

Genomic Regions ◽

Evaluation Of Methods

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Comparing five statistical methods of differential methylation identification using bisulfite sequencing data

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2015-0078 ◽

2016 ◽

Vol 15 (2) ◽

Cited By ~ 11

Author(s):

Xiaoqing Yu ◽

Shuying Sun

Keyword(s):

Comparative Analysis ◽

False Positive ◽

Bisulfite Sequencing ◽

Differential Methylation ◽

Sequencing Data ◽

Identification Methods ◽

Comparison Results ◽

Bisulfite Sequencing Data ◽

Group Variation ◽

Higher Sensitivity

AbstractWe are presenting a comprehensive comparative analysis of five differential methylation (DM) identification methods: methylKit, BSmooth, BiSeq, HMM-DM, and HMM-Fisher, which are developed for bisulfite sequencing (BS) data. We summarize the features of these methods from several analytical aspects and compare their performances using both simulated and real BS datasets. Our comparison results are summarized below. First, parameter settings may largely affect the accuracy of DM identification. Different from default settings, modified parameter settings yield higher sensitivity and/or lower false positive rates. Second, all five methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance, probably due to the different approaches they have used for DM identification. Third, HMM-DM and HMM-Fisher yield relatively higher sensitivity and lower false positive rates than others, especially in DM regions with large variation. Finally, we have found that among the three methods that involve methylation estimation (methylKit, BSmooth, and BiSeq), BiSeq can best present raw methylation signals. Therefore, based on these results, we suggest that users select DM identification methods based on the characteristics of their data and the advantages of each method.

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Comprehensive Evaluation of Differential Methylation Analysis Methods for Bisulfite Sequencing Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18157975 ◽

2021 ◽

Vol 18 (15) ◽

pp. 7975

Author(s):

Yongjun Piao ◽

Wanxue Xu ◽

Kwang Ho Park ◽

Keun Ho Ryu ◽

Rong Xiang

Keyword(s):

Bisulfite Sequencing ◽

Comprehensive Evaluation ◽

Differential Methylation ◽

Sequencing Depth ◽

Sequencing Data ◽

Methylation Analysis ◽

Sequencing Technologies ◽

Analysis Methods ◽

Bisulfite Sequencing Data ◽

Differential Methylation Analysis

Background: With advances in next-generation sequencing technologies, the bisulfite conversion of genomic DNA followed by sequencing has become the predominant technique for quantifying genome-wide DNA methylation at single-base resolution. A large number of computational approaches are available in literature for identifying differentially methylated regions in bisulfite sequencing data, and more are being developed continuously. Results: Here, we focused on a comprehensive evaluation of commonly used differential methylation analysis methods and describe the potential strengths and limitations of each method. We found that there are large differences among methods, and no single method consistently ranked first in all benchmarking. Moreover, smoothing seemed not to improve the performance greatly, and a small number of replicates created more difficulties in the computational analysis of BS-seq data than low sequencing depth. Conclusions: Data analysis and interpretation should be performed with great care, especially when the number of replicates or sequencing depth is limited.

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BiSulfite Bolt: A bisulfite sequencing analysis platform

GigaScience ◽

10.1093/gigascience/giab033 ◽

2021 ◽

Vol 10 (5) ◽

Author(s):

Colin Farrell ◽

Michael Thompson ◽

Anela Tosevska ◽

Adewale Oyetunde ◽

Matteo Pellegrini

Keyword(s):

Data Aggregation ◽

Bisulfite Sequencing ◽

Low Complexity ◽

Sequencing Analysis ◽

Command Line ◽

Sequencing Data ◽

Bisulfite Sequencing Data ◽

Analysis Platform ◽

Python Package ◽

Bisulfite Sequencing Analysis

Abstract Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

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