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- Publisher Website: 10.1038/s41467-019-09025-z
- Scopus: eid_2-s2.0-85062266874
- PMID: 30824707
- WOS: WOS:000459988600010
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Article: A multi-task convolutional deep neural network for variant calling in single molecule sequencing
Title | A multi-task convolutional deep neural network for variant calling in single molecule sequencing |
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Authors | |
Issue Date | 2019 |
Publisher | Nature Research (part of Springer Nature): Fully open access journals. The Journal's web site is located at http://www.nature.com/ncomms/index.html |
Citation | Nature Communications, 2019, v. 10, article no. 998 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 2 h 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 available open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model. |
Persistent Identifier | http://hdl.handle.net/10722/272475 |
ISSN | 2021 Impact Factor: 17.694 2020 SCImago Journal Rankings: 5.559 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Luo, R | - |
dc.contributor.author | Sedlazeck, FJ | - |
dc.contributor.author | Lam, TW | - |
dc.contributor.author | Schatz, MC | - |
dc.date.accessioned | 2019-07-20T10:43:01Z | - |
dc.date.available | 2019-07-20T10:43:01Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Nature Communications, 2019, v. 10, article no. 998 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | http://hdl.handle.net/10722/272475 | - |
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 2 h 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 available open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model. | - |
dc.language | eng | - |
dc.publisher | Nature Research (part of Springer Nature): Fully open access journals. The Journal's web site is located at http://www.nature.com/ncomms/index.html | - |
dc.relation.ispartof | Nature Communications | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | A multi-task convolutional deep neural network for variant calling in single molecule sequencing | - |
dc.type | Article | - |
dc.identifier.email | Luo, R: rbluo@cs.hku.hk | - |
dc.identifier.email | Lam, TW: twlam@cs.hku.hk | - |
dc.identifier.authority | Luo, R=rp02360 | - |
dc.identifier.authority | Lam, TW=rp00135 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1038/s41467-019-09025-z | - |
dc.identifier.pmid | 30824707 | - |
dc.identifier.pmcid | PMC6397153 | - |
dc.identifier.scopus | eid_2-s2.0-85062266874 | - |
dc.identifier.hkuros | 299464 | - |
dc.identifier.volume | 10 | - |
dc.identifier.spage | article no. 998 | - |
dc.identifier.epage | article no. 998 | - |
dc.identifier.isi | WOS:000459988600010 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 2041-1723 | - |