title: A Bayesian Assignment Method for Ambiguous Bisulfite Short Reads.
abstract: DNA methylation is an epigenetic modification critical for normal development and diseases. The determination of genome-wide DNA methylation at single-nucleotide resolution is made possible by sequencing bisulfite treated DNA with next generation high-throughput sequencing. However, aligning bisulfite short reads to a reference genome remains challenging as only a limited proportion of them (around 50-70%) can be aligned uniquely; a significant proportion, known as multireads, are mapped to multiple locations and thus discarded from downstream analyses, causing financial waste and biased methylation inference. To address this issue, we develop a Bayesian model that assigns multireads to their most likely locations based on the posterior probability derived from information hidden in uniquely aligned reads. Analyses of both simulated data and real hairpin bisulfite sequencing data show that our method can effectively assign approximately 70% of the multireads to their best locations with up to 90% accuracy, leading to a significant increase in the overall mapping efficiency. Moreover, the assignment model shows robust performance with low coverage depth, making it particularly attractive considering the prohibitive cost of bisulfite sequencing. Additionally, results show that longer reads help improve the performance of the assignment model. The assignment model is also robust to varying degrees of methylation and varying sequencing error rates. Finally, incorporating prior knowledge on mutation rate and context specific methylation level into the assignment model increases inference accuracy. The assignment model is implemented in the BAM-ABS package and freely available at https://github.com/zhanglabvt/BAM_ABS.
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States of America.
authors.forename: Hong
authors.initials: H
authors.lastname: Tran
authors.affiliation: Department of Statistics, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States of America.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States of America.
authors.forename: Saima
authors.initials: S
authors.lastname: Tithi
authors.affiliation: Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States of America.
authors.forename: Ming-an
authors.initials: MA
authors.lastname: Sun
authors.affiliation: Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States of America.|Department of Biological Sciences, Virginia Polytechnic Institute and State University(Virginia Tech), Blacksburg, Virginia, United States of America.
authors.forename: Hehuang
authors.initials: H
authors.lastname: Xie
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States of America.
authors.forename: Liqing
authors.initials: L
authors.lastname: Zhang
mesh_terms: D001499:Bayes Theorem; D004247:DNA / Q000737:chemistry*; D019175:DNA Methylation; D015226:Dinucleoside Phosphates / Q000032:analysis; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D011336:Probability; D017422:Sequence Analysis, DNA / Q000379:methods; D013447:Sulfites / Q000032:analysis*
chemical_list: D015226:Dinucleoside Phosphates; D013447:Sulfites; C018309:guanylyl-(3'-5')-guanosine; D004247:DNA; C042345:hydrogen sulfite