TY - JOUR AU - Srivastava, Akanksha AU - Karpievitch, Yuliya V. AU - Eichten, Steven R. AU - Borevitz, Justin O. AU - Lister, Ryan PY - 2019 DA - 2019/05/16 TI - HOME: a histogram based machine learning approach for effective identification of differentially methylated regions JO - BMC Bioinformatics SP - 253 VL - 20 IS - 1 AB - The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-019-2845-y DO - 10.1186/s12859-019-2845-y ID - Srivastava2019 ER -