Haodong Xu1, Ruifeng Hu1, Peilin Jia1, Zhongming Zhao1,2,3. 1. School of Biomedical Informatics, Center for Precision Health. 2. MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA. 3. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
Abstract
MOTIVATION: DNA N6-methyladenine (6 mA) has recently been found as an essential epigenetic modification, playing its roles in a variety of cellular processes. The abnormal status of DNA 6 mA modification has been reported in cancer and other disease. The annotation of 6 mA marks in genome is the first crucial step to explore the underlying molecular mechanisms including its regulatory roles. RESULTS: We present a novel online DNA 6 mA site tool, 6 mA-Finder, by incorporating seven sequence-derived information and three physicochemical-based features through recursive feature elimination strategy. Our multiple cross-validations indicate the promising accuracy and robustness of our model. 6 mA-Finder outperforms its peer tools in general and species-specific 6 mA site prediction, suggesting it can provide a useful resource for further experimental investigation of DNA 6 mA modification. AVAILABILITY AND IMPLEMENTATION: https://bioinfo.uth.edu/6mA_Finder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: DNA N6-methyladenine (6 mA) has recently been found as an essential epigenetic modification, playing its roles in a variety of cellular processes. The abnormal status of DNA 6 mA modification has been reported in cancer and other disease. The annotation of 6 mA marks in genome is the first crucial step to explore the underlying molecular mechanisms including its regulatory roles. RESULTS: We present a novel online DNA 6 mA site tool, 6 mA-Finder, by incorporating seven sequence-derived information and three physicochemical-based features through recursive feature elimination strategy. Our multiple cross-validations indicate the promising accuracy and robustness of our model. 6 mA-Finder outperforms its peer tools in general and species-specific 6 mA site prediction, suggesting it can provide a useful resource for further experimental investigation of DNA 6 mA modification. AVAILABILITY AND IMPLEMENTATION: https://bioinfo.uth.edu/6mA_Finder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Tao P Wu; Tao Wang; Matthew G Seetin; Yongquan Lai; Shijia Zhu; Kaixuan Lin; Yifei Liu; Stephanie D Byrum; Samuel G Mackintosh; Mei Zhong; Alan Tackett; Guilin Wang; Lawrence S Hon; Gang Fang; James A Swenberg; Andrew Z Xiao Journal: Nature Date: 2016-03-30 Impact factor: 49.962