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- [1] NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networksGenome Biology, 22Mian Umair AhsanQian LiuLi FangKai Wang
NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks
被引：42
|作者：
Ahsan, Mian Umair
[1
]
Liu, Qian
[1
]
Fang, Li
[1
]
Wang, Kai
[1
,2
]
机构：
[1] Childrens Hosp Philadelphia, Raymond G Perelman Ctr Cellular & Mol Therapeut, Philadelphia, PA 19104 USA
[2] Univ Penn, Perelman Sch Med, Dept Pathol & Lab Med, Philadelphia, PA 19104 USA
关键词：
Variant calling;
Long-range haplotype;
Deep learning;
Difficult-to-map regions;
HUMAN GENOME;
D O I：
10.1186/s13059-021-02472-2
中图分类号：
Q81 [生物工程学（生物技术）];
Q93 [微生物学];
学科分类号：
071005 ;
0836 ;
090102 ;
100705 ;
摘要：
Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs using long-range haplotype information, then phases long reads with called SNPs and calls indels with local realignment. Evaluation on 8 human genomes demonstrates that NanoCaller generally achieves better performance than competing approaches. We experimentally validate 41 novel variants in a widely used benchmarking genome, which could not be reliably detected previously. In summary, NanoCaller facilitates the discovery of novel variants in complex genomic regions from long-read sequencing.
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页数：33
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