A Bayesian Assignment Method for Ambiguous Bisulfite Short Reads

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dc.contributor.authorTran, H.en
dc.contributor.authorWu, X.en
dc.contributor.authorTithi, S.en
dc.contributor.authorSun, M.-A.en
dc.contributor.authorXie, H.en
dc.contributor.authorZhang, L.en
dc.contributor.departmentBiological Systems Engineeringen
dc.date.accessioned2017-02-02T23:07:51Zen
dc.date.available2017-02-02T23:07:51Zen
dc.date.issued2016-03-24en
dc.description.abstractDNA 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.en
dc.description.versionPublished versionen
dc.format.extent? - ? (17) page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0151826en
dc.identifier.issn1932-6203en
dc.identifier.issue3en
dc.identifier.urihttp://hdl.handle.net/10919/74911en
dc.identifier.volume11en
dc.language.isoenen
dc.publisherPLOSen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000372708000049&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectdna methylationen
dc.subjectsequencing dataen
dc.subjecthuman genomeen
dc.subjectalignmenten
dc.titleA Bayesian Assignment Method for Ambiguous Bisulfite Short Readsen
dc.title.serialPLOS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/Statisticsen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Instituteen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Institute/Researchersen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Institute/SelectedFaculty1en

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