Apollo: A sequencing-technology-independent, scalable and accurate assembly polishing algorithm

buir.contributor.authorÇiçek, A. Ercüment
buir.contributor.authorAlkan, Can
buir.contributor.authorMutlu, Onur
dc.citation.epage3679en_US
dc.citation.issueNumber12en_US
dc.citation.spage3669en_US
dc.citation.volumeNumber36en_US
dc.contributor.authorFırtına, C.en_US
dc.contributor.authorKim, J. S.en_US
dc.contributor.authorAlser, M.en_US
dc.contributor.authorŞenol Cali, D.en_US
dc.contributor.authorÇiçek, A. Ercümenten_US
dc.contributor.authorAlkan, Canen_US
dc.contributor.authorMutlu, Onuren_US
dc.date.accessioned2021-02-25T13:07:43Z
dc.date.available2021-02-25T13:07:43Z
dc.date.issued2020-03
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractMotivation: Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technologydependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results: We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and thirdgeneration). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward– Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts.en_US
dc.identifier.doi10.1093/bioinformatics/btaa179en_US
dc.identifier.issn1367-4803en_US
dc.identifier.urihttp://hdl.handle.net/11693/75595en_US
dc.language.isoEnglishen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttps://dx.doi.org/10.1093/bioinformatics/btaa179en_US
dc.source.titleBioinformaticsen_US
dc.titleApollo: A sequencing-technology-independent, scalable and accurate assembly polishing algorithmen_US
dc.typeArticleen_US

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