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Article: CONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning

TitleCONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning
Authors
Issue Date2020
PublisherCell Press. The Journal's web site is located at https://www.cell.com/iscience.home
Citation
iScience, 2020, v. 23, p. article no. 101128 How to Cite?
AbstractSingle-molecule sequencing technologies produce much longer reads compared with next-generation sequencing, greatly improving the contiguity of de novo as- sembly of genomes. However, the relatively high error rates in long reads make it challenging to obtain high-quality assemblies. A computationally intensive consensus step is needed to resolve the discrepancies in the reads. Efficient consensus tools have emerged in the recent past, based on partial-order align- ment. In this study, we discovered that the spatial relationship of alignment pileup is crucial to high-quality consensus and developed a deep learning-based consensus tool, CONNET, which outperforms the fastest tools in terms of both accuracy and speed. We tested CONNET using a 903 dataset of E. coli and a 373 human dataset. In addition to achieving high-quality consensus results, CONNET is capable of delivering phased diploid genome consensus. Diploid consensus on the above-mentioned human assembly further reduced 12% of the consensus errors made in the haploid results.
Persistent Identifierhttp://hdl.handle.net/10722/284227
ISSN
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Y-
dc.contributor.authorLiu, CM-
dc.contributor.authorLeung, HCM-
dc.contributor.authorLuo, R-
dc.contributor.authorLam, TW-
dc.date.accessioned2020-07-20T05:57:04Z-
dc.date.available2020-07-20T05:57:04Z-
dc.date.issued2020-
dc.identifier.citationiScience, 2020, v. 23, p. article no. 101128-
dc.identifier.issn2589-0042-
dc.identifier.urihttp://hdl.handle.net/10722/284227-
dc.description.abstractSingle-molecule sequencing technologies produce much longer reads compared with next-generation sequencing, greatly improving the contiguity of de novo as- sembly of genomes. However, the relatively high error rates in long reads make it challenging to obtain high-quality assemblies. A computationally intensive consensus step is needed to resolve the discrepancies in the reads. Efficient consensus tools have emerged in the recent past, based on partial-order align- ment. In this study, we discovered that the spatial relationship of alignment pileup is crucial to high-quality consensus and developed a deep learning-based consensus tool, CONNET, which outperforms the fastest tools in terms of both accuracy and speed. We tested CONNET using a 903 dataset of E. coli and a 373 human dataset. In addition to achieving high-quality consensus results, CONNET is capable of delivering phased diploid genome consensus. Diploid consensus on the above-mentioned human assembly further reduced 12% of the consensus errors made in the haploid results.-
dc.languageeng-
dc.publisherCell Press. The Journal's web site is located at https://www.cell.com/iscience.home-
dc.relation.ispartofiScience-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleCONNET: Accurate Genome Consensus in Assembling Nanopore Sequencing Data via Deep Learning-
dc.typeArticle-
dc.identifier.emailLiu, CM: imcx@hku.hk-
dc.identifier.emailLeung, HCM: cmleung3@hku.hk-
dc.identifier.emailLuo, R: rbluo@cs.hku.hk-
dc.identifier.emailLam, TW: twlam@cs.hku.hk-
dc.identifier.authorityLeung, HCM=rp00144-
dc.identifier.authorityLuo, R=rp02360-
dc.identifier.authorityLam, TW=rp00135-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.isci.2020.101128-
dc.identifier.pmid32422594-
dc.identifier.pmcidPMC7229283-
dc.identifier.scopuseid_2-s2.0-85084479170-
dc.identifier.hkuros310900-
dc.identifier.volume23-
dc.identifier.spagearticle no. 101128-
dc.identifier.epagearticle no. 101128-
dc.publisher.placeUnited States-

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