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

References in zbMATH (referenced in 11 articles )

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  1. Xing, Xin; Liu, Meimei; Ma, Ping; Zhong, Wenxuan: Minimax nonparametric parallelism test (2020)
  2. Benjamini, Yuval; Taylor, Jonathan; Irizarry, Rafael A.: Selection-corrected statistical inference for region detection with high-throughput assays (2019)
  3. Gong, Boying; Purdom, Elizabeth: MethCP: differentially methylated region detection with change point models (2019)
  4. Page, Christian M.; Vos, Linda; Rounge, Trine B.; Harbo, Hanne F.; Andreassen, Bettina K.: Assessing genome-wide significance for the detection of differentially methylated regions (2018)
  5. Barturen, Guillermo; Oliver, José L.; Hackenberg, Michael: Error correction in methylation profiling from NGS bisulfite protocols (2017)
  6. Lakhal-Chaieb, Lajmi; Greenwood, Celia M. T.; Ouhourane, Mohamed; Zhao, Kaiqiong; Abdous, Belkacem; Oualkacha, Karim: A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type (2017)
  7. Ryu, Duchwan; Xu, Hongyan; George, Varghese; Su, Shaoyong; Wang, Xiaoling; Shi, Huidong; Podolsky, Robert H.: Differential methylation tests of regulatory regions (2016)
  8. Sun, Shuying; Yu, Xiaoqing: HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test (2016)
  9. Wang, Tao; Chen, Mengjie; Zhao, Hongyu: Estimating DNA methylation levels by joint modeling of multiple methylation profiles from microarray data (2016)
  10. Yu, Xiaoqing; Sun, Shuying: Comparing five statistical methods of differential methylation identification using bisulfite sequencing data (2016)
  11. Yu, Xiaoqing; Sun, Shuying: HMM-DM: identifying differentially methylated regions using a hidden Markov model (2016)