Dataset Information

Methy-Pipe: an integrated bioinformatics pipeline for whole genome bisulfite sequencing data analysis.

ABSTRACT: DNA methylation, one of the most important epigenetic modifications, plays a crucial role in various biological processes. The level of DNA methylation can be measured using whole-genome bisulfite sequencing at single base resolution. However, until now, there is a paucity of publicly available software for carrying out integrated methylation data analysis. In this study, we implemented Methy-Pipe, which not only fulfills the core data analysis requirements (e.g. sequence alignment, differential methylation analysis, etc.) but also provides useful tools for methylation data annotation and visualization. Specifically, it uses Burrow-Wheeler Transform (BWT) algorithm to directly align bisulfite sequencing reads to a reference genome and implements a novel sliding window based approach with statistical methods for the identification of differentially methylated regions (DMRs). The capability of processing data parallelly allows it to outperform a number of other bisulfite alignment software packages. To demonstrate its utility and performance, we applied it to both real and simulated bisulfite sequencing datasets. The results indicate that Methy-Pipe can accurately estimate methylation densities, identify DMRs and provide a variety of utility programs for downstream methylation data analysis. In summary, Methy-Pipe is a useful pipeline that can process whole genome bisulfite sequencing data in an efficient, accurate, and user-friendly manner. Software and test dataset are available at http://sunlab.lihs.cuhk.edu.hk/methy-pipe/.

SUBMITTER: Jiang P

PROVIDER: S-EPMC4063866 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Methy-Pipe: an integrated bioinformatics pipeline for whole genome bisulfite sequencing data analysis.

Jiang Peiyong P Sun Kun K Lun Fiona M F FM Guo Andy M AM Wang Huating H Chan K C Allen KC Chiu Rossa W K RW Lo Y M Dennis YM Sun Hao H

PloS one 20140619 6

DNA methylation, one of the most important epigenetic modifications, plays a crucial role in various biological processes. The level of DNA methylation can be measured using whole-genome bisulfite sequencing at single base resolution. However, until now, there is a paucity of publicly available software for carrying out integrated methylation data analysis. In this study, we implemented Methy-Pipe, which not only fulfills the core data analysis requirements (e.g. sequence alignment, differential ...[more]

PMID: 24945300

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Project description:BACKGROUND:Analysing whole genome bisulfite sequencing datasets is a data-intensive task that requires comprehensive and reproducible workflows to generate valid results. While many algorithms have been developed for tasks such as alignment, comprehensive end-to-end pipelines are still sparse. Furthermore, previous pipelines lack features or show technical deficiencies, thus impeding analyses. RESULTS:We developed wg-blimp (whole genome bisulfite sequencing methylation analysis pipeline) as an end-to-end pipeline to ease whole genome bisulfite sequencing data analysis. It integrates established algorithms for alignment, quality control, methylation calling, detection of differentially methylated regions, and methylome segmentation, requiring only a reference genome and raw sequencing data as input. Comparing wg-blimp to previous end-to-end pipelines reveals similar setups for common sequence processing tasks, but shows differences for post-alignment analyses. We improve on previous pipelines by providing a more comprehensive analysis workflow as well as an interactive user interface. To demonstrate wg-blimp's ability to produce correct results we used it to call differentially methylated regions for two publicly available datasets. We were able to replicate 112 of 114 previously published regions, and found results to be consistent with previous findings. We further applied wg-blimp to a publicly available sample of embryonic stem cells to showcase methylome segmentation. As expected, unmethylated regions were in close proximity of transcription start sites. Segmentation results were consistent with previous analyses, despite different reference genomes and sequencing techniques. CONCLUSIONS:wg-blimp provides a comprehensive analysis pipeline for whole genome bisulfite sequencing data as well as a user interface for simplified result inspection. We demonstrated its applicability by analysing multiple publicly available datasets. Thus, wg-blimp is a relevant alternative to previous analysis pipelines and may facilitate future epigenetic research.

Project description:BackgroundIn large-scale high-throughput sequencing projects and biobank construction, sample tagging is essential to prevent sample mix-ups. Despite the availability of fingerprint panels for DNA data, little research has been conducted on sample tagging of whole genome bisulfite sequencing (WGBS) data. This study aims to construct a pipeline and identify applicable fingerprint panels to address this problem.ResultsUsing autosome-wide A/T polymorphic single nucleotide variants (SNVs) obtained from whole genome sequencing (WGS) and WGBS of individuals from the Third China National Stroke Registry, we designed a fingerprint panel and constructed an optimized pipeline for tagging WGBS data. This pipeline used Bis-SNP to call genotypes from the WGBS data, and optimized genotype comparison by eliminating wildtype homozygous and missing genotypes, and retaining variants with identical genomic coordinates and reference/alternative alleles. WGS-based and WGBS-based genotypes called from identical or different samples were extensively compared using hap.py. In the first batch of 94 samples, the genotype consistency rates were between 71.01%-84.23% and 51.43%-60.50% for the matched and mismatched WGS and WGBS data using the autosome-wide A/T polymorphic SNV panel. This capability to tag WGBS data was validated among the second batch of 240 samples, with genotype consistency rates ranging from 70.61%-84.65% to 49.58%-61.42% for the matched and mismatched data, respectively. We also determined that the number of genetic variants required to correctly tag WGBS data was on the order of thousands through testing six fingerprint panels with different orders for the number of variants. Additionally, we affirmed this result with two self-designed panels of 1351 and 1278 SNVs, respectively. Furthermore, this study confirmed that using the number of genetic variants with identical coordinates and ref/alt alleles, or identical genotypes could not correctly tag WGBS data.ConclusionThis study proposed an optimized pipeline, applicable fingerprint panels, and a lower boundary for the number of fingerprint genetic variants needed for correct sample tagging of WGBS data, which are valuable for tagging WGBS data and integrating multi-omics data for biobanks.

Dataset Information

Methy-Pipe: an integrated bioinformatics pipeline for whole genome bisulfite sequencing data analysis.

Publications

Methy-Pipe: an integrated bioinformatics pipeline for whole genome bisulfite sequencing data analysis.

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