Dataset Information

GBSA: a comprehensive software for analysing whole genome bisulfite sequencing data.

ABSTRACT: High-throughput sequencing is increasingly being used in combination with bisulfite (BS) assays to study DNA methylation at nucleotide resolution. Although several programmes provide genome-wide alignment of BS-treated reads, the resulting information is not readily interpretable and often requires further bioinformatic steps for meaningful analysis. Current post-alignment BS-sequencing programmes are generally focused on the gene-specific level, a restrictive feature when analysis in the non-coding regions, such as enhancers and intergenic microRNAs, is required. Here, we present Genome Bisulfite Sequencing Analyser (GBSA-http://ctrad-csi.nus.edu.sg/gbsa), a free open-source software capable of analysing whole-genome bisulfite sequencing data with either a gene-centric or gene-independent focus. Through analysis of the largest published data sets to date, we demonstrate GBSA's features in providing sequencing quality assessment, methylation scoring, functional data management and visualization of genomic methylation at nucleotide resolution. Additionally, we show that GBSA's output can be easily integrated with other high-throughput sequencing data, such as RNA-Seq or ChIP-seq, to elucidate the role of methylated intergenic regions in gene regulation. In essence, GBSA allows an investigator to explore not only known loci but also all the genomic regions, for which methylation studies could lead to the discovery of new regulatory mechanisms.

SUBMITTER: Benoukraf T

PROVIDER: S-EPMC3575794 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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GBSA: a comprehensive software for analysing whole genome bisulfite sequencing data.

Benoukraf Touati T Wongphayak Sarawut S Hadi Luqman Hakim Abdul LH Wu Mengchu M Soong Richie R

Nucleic acids research 20121224 4

High-throughput sequencing is increasingly being used in combination with bisulfite (BS) assays to study DNA methylation at nucleotide resolution. Although several programmes provide genome-wide alignment of BS-treated reads, the resulting information is not readily interpretable and often requires further bioinformatic steps for meaningful analysis. Current post-alignment BS-sequencing programmes are generally focused on the gene-specific level, a restrictive feature when analysis in the non-co ...[more]

PMID: 23268441

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Project description:Methylation of cytosine in genomic DNA is a well-characterized epigenetic modification involved in many cellular processes and diseases. Whole-genome bisulfite sequencing (WGBS), such as MethylC-seq and post-bisulfite adaptor tagging sequencing (PBAT-seq), uses the power of high-throughput DNA sequencers and provides genome-wide DNA methylation profiles at single-base resolution. However, the accuracy and consistency of WGBS outputs in relation to the operating conditions of high-throughput sequencers have not been explored.We have used the Illumina HiSeq platform for our PBAT-based WGBS, and found that different versions of HiSeq Control Software (HCS) and Real-Time Analysis (RTA) installed on the system provided different global CpG methylation levels (approximately 5% overall difference) for the same libraries. This problem was reproduced multiple times with different WGBS libraries and likely to be associated with the low sequence diversity of bisulfite-converted DNA. We found that HCS was the major determinant in the observed differences. To determine which version of HCS is most suitable for WGBS, we used substrates with predetermined CpG methylation levels, and found that HCS v2.0.5 is the best among the examined versions. HCS v2.0.12 showed the poorest performance and provided artificially lower CpG methylation levels when 5-methylcytosine is read as guanine (first read of PBAT-seq and second read of MethylC-seq). In addition, paired-end sequencing of low diversity libraries using HCS v2.2.38 or the latest HCS v2.2.58 was greatly affected by cluster densities.Software updates in the Illumina HiSeq platform can affect the outputs from low-diversity sequencing libraries such as WGBS libraries. More recent versions are not necessarily the better, and HCS v2.0.5 is currently the best for WGBS among the examined HCS versions. Thus, together with other experimental conditions, special care has to be taken on this point when CpG methylation levels are to be compared between different samples by WGBS.

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

GBSA: a comprehensive software for analysing whole genome bisulfite sequencing data.

Publications

GBSA: a comprehensive software for analysing whole genome bisulfite sequencing data.

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