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Conference Paper: Deep Learning Algorithms for Variant Calling in Oxford Nanopore Sequencing
Title | Deep Learning Algorithms for Variant Calling in Oxford Nanopore Sequencing |
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Authors | |
Issue Date | 2019 |
Publisher | Department of Computer Science & Engineering, The Chinese University of Hong Kong. |
Citation | Seminar Series 2018-2019, Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, 12 April 2019 How to Cite? |
Abstract | The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per- nucleotide error rate of ~5–15%. Meeting this demand, we developed Clairvoyante, a multi- task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is avail- able open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model. |
Persistent Identifier | http://hdl.handle.net/10722/298650 |
DC Field | Value | Language |
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dc.contributor.author | Luo, R | - |
dc.date.accessioned | 2021-04-09T08:44:12Z | - |
dc.date.available | 2021-04-09T08:44:12Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Seminar Series 2018-2019, Department of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, 12 April 2019 | - |
dc.identifier.uri | http://hdl.handle.net/10722/298650 | - |
dc.description.abstract | The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per- nucleotide error rate of ~5–15%. Meeting this demand, we developed Clairvoyante, a multi- task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is avail- able open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model. | - |
dc.language | eng | - |
dc.publisher | Department of Computer Science & Engineering, The Chinese University of Hong Kong. | - |
dc.relation.ispartof | Chinese University of Hong Kong (CUHK), Department of Computer Science & Engineering (CSE) Seminar Series 2018-2019 | - |
dc.title | Deep Learning Algorithms for Variant Calling in Oxford Nanopore Sequencing | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Luo, R: rbluo@cs.hku.hk | - |
dc.identifier.authority | Luo, R=rp02360 | - |
dc.identifier.hkuros | 302246 | - |
dc.publisher.place | Hong Kong | - |