Symphonizing pileup and full-alignment for deep learning-based long-read variant calling
Deep learning-based variant callers are becoming the standard and have achieved superior single nucleotide polymorphisms calling performance using long reads. Here we present Clair3, which leverages two major method categories: pileup calling handles most variant candidates with speed, and full-alig...
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| Published in | Nature Computational Science Vol. 2; no. 12; pp. 797 - 803 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Nature Publishing Group
01.12.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2662-8457 |
| DOI | 10.1038/s43588-022-00387-x |
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| Abstract | Deep learning-based variant callers are becoming the standard and have achieved superior single nucleotide polymorphisms calling performance using long reads. Here we present Clair3, which leverages two major method categories: pileup calling handles most variant candidates with speed, and full-alignment tackles complicated candidates to maximize precision and recall. Clair3 runs faster than any of the other state-of-the-art variant callers and demonstrates improved performance, especially at lower coverage. |
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| AbstractList | Deep learning-based variant callers are becoming the standard and have achieved superior single nucleotide polymorphisms calling performance using long reads. Here we present Clair3, which leverages two major method categories: pileup calling handles most variant candidates with speed, and full-alignment tackles complicated candidates to maximize precision and recall. Clair3 runs faster than any of the other state-of-the-art variant callers and demonstrates improved performance, especially at lower coverage. Deep learning-based variant callers are becoming the standard and have achieved superior single nucleotide polymorphisms calling performance using long reads. Here we present Clair3, which leverages two major method categories: pileup calling handles most variant candidates with speed, and full-alignment tackles complicated candidates to maximize precision and recall. Clair3 runs faster than any of the other state-of-the-art variant callers and demonstrates improved performance, especially at lower coverage.Leveraging both the simple pileup input and full-alignment input, small variant calling using noisy long reads has improved speed and accuracy. |
| Author | Luo, Ruibang Leung, Amy Wing-Sze Su, Junhao Lam, Tak-Wah Zheng, Zhenxian Li, Shumin |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38177392$$D View this record in MEDLINE/PubMed |
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| Title | Symphonizing pileup and full-alignment for deep learning-based long-read variant calling |
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