BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

By Kasper D. Hansen, Benjamin Langmead, Rafael A. Irizarry. Genome Biology, 2012

745 citations recorded by the source corpus.

DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/51dc46ace9ffd1f716fa7d4e8d8181c2ff113f01

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Citations in the corpus

• Reduced H3K27me3 and DNA hypomethylation are major drivers of gene expression in K27M mutant pediatric high-grade gliomas. (2013, methodology): “Differentially methylated regions (DMRs) were identified using the bsseq Bioconductor package, version 0.6.2 (Hansen et al., 2012).”
• A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data (2014, methodology): “These methods first estimate the mean methylation levels through smoothing, and compare methylation across conditions via either a ttest (BSmooth) (20) or a Wald test based on a generalized linear-model framework (BiSeq) (22).”
• Next-Generation Sequencing Technology: Current Trends and Advancements (2023, background): “MethylDackel * Differential methylation Metilene [138], BSsmooth [139], methylKit [140] Epigenomics-ChIP [147], MAnorm [148], MMDiff [149]”
• metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data (2016, neutral): “2.9) (Sun et al. 2014), BSmooth (v.1.0.0) (Hansen et al. 2012), and Genome Research 257 www.genome.org BiSeq (Hebestreit et al. 2013) (v1.”
• De novo identification of differentially methylated regions in the human genome (2015, methodology): “Adipocyte methylomes are largely unexplored, and so in the absence of a ground truth for validation, groupwise DMRs were called from the WGBS assays using the BSmooth algorithm [46] and defined as ground truth.”
• Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates (2015, methodology): “To incorporate this information, it is often assumed that the underlying true methylation levels can be represented by a smooth curve genome-wide, and smoothing techniques are used to estimate the curve (7).”
• Detecting DNA cytosine methylation using nanopore sequencing (2017, background): “This assumption is reasonable as it is expected that the methylation status of nearby sites is correlated [6, 7].”
• Differential methylation analysis for BS-seq data under general experimental design (2016, methodology): “It has been reported that the methylation levels are spatially correlated across the genome (Lister et al., 2009), and smoothing-based methods were developed to account for the correlation (Hansen et al., 2012; Park et al., 2014).”