title: qDNAmod: a statistical model-based tool to reveal intercellular heterogeneity of DNA modification from SMRT sequencing data.
abstract: In an isogenic cell population, phenotypic heterogeneity among individual cells is common and critical for survival of the population under different environment conditions. DNA modification is an important epigenetic factor that can regulate phenotypic heterogeneity. The single molecule real-time (SMRT) sequencing technology provides a unique platform for detecting a wide range of DNA modifications, including N6-methyladenine (6-mA), N4-methylcytosine (4-mC) and 5-methylcytosine (5-mC). Here we present qDNAmod, a novel bioinformatic tool for genome-wide quantitative profiling of intercellular heterogeneity of DNA modification from SMRT sequencing data. It is capable of estimating proportion of isogenic haploid cells, in which the same loci of the genome are differentially modified. We tested the reliability of qDNAmod with the SMRT sequencing data of Streptococcus pneumoniae strain ST556. qDNAmod detected extensive intercellular heterogeneity of DNA methylation (6-mA) in a clonal population of ST556. Subsequent biochemical analyses revealed that the recognition sequences of two type I restriction-modification (R-M) systems are responsible for the intercellular heterogeneity of DNA methylation initially identified by qDNAmod. qDNAmod thus represents a valuable tool for studying intercellular phenotypic heterogeneity from genome-wide DNA modification.
authors.affiliation: MOE Key Lab of Bioinformatics, Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China.
authors.forename: Zhixing
authors.initials: Z
authors.lastname: Feng
authors.affiliation: Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing 100084, China Collaborative Innovation Center for Biotherapy, Tsinghua University, Beijing 100084, China Collaborative Innovation Center for Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.
authors.forename: Jing-Ren
authors.initials: JR
authors.lastname: Zhang
authors.affiliation: MOE Key Lab of Bioinformatics, Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China zhangxg@tsinghua.edu.cn.
authors.forename: Xuegong
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000225:Adenine / Q000378:metabolism; D001499:Bayes Theorem; D004247:DNA / Q000737:chemistry / Q000378:metabolism; D019175:DNA Methylation; D015254:DNA Modification Methylases / Q000378:metabolism; D015233:Models, Statistical; D017422:Sequence Analysis, DNA; D012984:Software; D013296:Streptococcus pneumoniae / Q000235:genetics
chemical_list: D004247:DNA; D015254:DNA Modification Methylases; D000225:Adenine