title: Modeling kinetic rate variation in third generation DNA sequencing data to detect putative modifications to DNA bases.
abstract: Current generation DNA sequencing instruments are moving closer to seamlessly sequencing genomes of entire populations as a routine part of scientific investigation. However, while significant inroads have been made identifying small nucleotide variation and structural variations in DNA that impact phenotypes of interest, progress has not been as dramatic regarding epigenetic changes and base-level damage to DNA, largely due to technological limitations in assaying all known and unknown types of modifications at genome scale. Recently, single-molecule real time (SMRT) sequencing has been reported to identify kinetic variation (KV) events that have been demonstrated to reflect epigenetic changes of every known type, providing a path forward for detecting base modifications as a routine part of sequencing. However, to date no statistical framework has been proposed to enhance the power to detect these events while also controlling for false-positive events. By modeling enzyme kinetics in the neighborhood of an arbitrary location in a genomic region of interest as a conditional random field, we provide a statistical framework for incorporating kinetic information at a test position of interest as well as at neighboring sites that help enhance the power to detect KV events. The performance of this and related models is explored, with the best-performing model applied to plasmid DNA isolated from Escherichia coli and mitochondrial DNA isolated from human brain tissue. We highlight widespread kinetic variation events, some of which strongly associate with known modification events, while others represent putative chemically modified sites of unknown types.
authors.affiliation: Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York 10029, USA. eric.schadt@mssm.edu
authors.forename: Eric E
authors.initials: EE
authors.lastname: Schadt
authors.forename: Onureena
authors.initials: O
authors.lastname: Banerjee
authors.forename: Gang
authors.initials: G
authors.lastname: Fang
authors.forename: Zhixing
authors.initials: Z
authors.lastname: Feng
authors.forename: Wing H
authors.initials: WH
authors.lastname: Wong
authors.forename: Xuegong
authors.initials: X
authors.lastname: Zhang
authors.forename: Andrey
authors.initials: A
authors.lastname: Kislyuk
authors.forename: Tyson A
authors.initials: TA
authors.lastname: Clark
authors.forename: Khai
authors.initials: K
authors.lastname: Luong
authors.forename: Alona
authors.initials: A
authors.lastname: Keren-Paz
authors.forename: Andrew
authors.initials: A
authors.lastname: Chess
authors.forename: Vipin
authors.initials: V
authors.lastname: Kumar
authors.forename: Alice
authors.initials: A
authors.lastname: Chen-Plotkin
authors.forename: Neal
authors.initials: N
authors.lastname: Sondheimer
authors.forename: Jonas
authors.initials: J
authors.lastname: Korlach
authors.forename: Andrew
authors.initials: A
authors.lastname: Kasarskis
mesh_terms: D004269:DNA, Bacterial / Q000737:chemistry; D004272:DNA, Mitochondrial / Q000737:chemistry; D004926:Escherichia coli / Q000737:chemistry; D006151:Guanosine / Q000031:analogs & derivatives / Q000737:chemistry; D006801:Humans; D007700:Kinetics; D010084:Oxidation-Reduction; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004269:DNA, Bacterial; D004272:DNA, Mitochondrial; D006151:Guanosine; C046215:8-hydroxyguanosine