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Conference Paper: Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing
Title | Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing |
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Authors | |
Issue Date | 2018 |
Citation | IAS Focused Program on New Technologies and Translational Applications in Genomics, HKUST, Hong Kong, 7-9 November 2018 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 achieved 99.73%, 97.68% and 95.36% precision on known variants, and 98.65%, 92.57%, 77.89% 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 two hours on a standard server. Furthermore, we identified 3,135 variants that are not yet indexed but are strongly supported by both PacBio and Oxford Nanopore data. Clairvoyante is available open-source
(https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the
model. |
Description | Session III: Computational Genomics Host: The Hong Kong University of Science and Technology, HKUST Jockey Club Institute for Advanced Study |
Persistent Identifier | http://hdl.handle.net/10722/298651 |
DC Field | Value | Language |
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dc.contributor.author | Luo, R | - |
dc.date.accessioned | 2021-04-09T08:53:47Z | - |
dc.date.available | 2021-04-09T08:53:47Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IAS Focused Program on New Technologies and Translational Applications in Genomics, HKUST, Hong Kong, 7-9 November 2018 | - |
dc.identifier.uri | http://hdl.handle.net/10722/298651 | - |
dc.description | Session III: Computational Genomics | - |
dc.description | Host: The Hong Kong University of Science and Technology, HKUST Jockey Club Institute for Advanced Study | - |
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 achieved 99.73%, 97.68% and 95.36% precision on known variants, and 98.65%, 92.57%, 77.89% 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 two hours on a standard server. Furthermore, we identified 3,135 variants that are not yet indexed but are strongly supported by both PacBio and Oxford Nanopore data. Clairvoyante is available open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model. | - |
dc.language | eng | - |
dc.relation.ispartof | IAS Focused Program on New Technologies and Translational Applications in Genomics, 2018 | - |
dc.title | Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Luo, R: rbluo@cs.hku.hk | - |
dc.identifier.authority | Luo, R=rp02360 | - |
dc.identifier.hkuros | 302247 | - |