NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.
NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.
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DOI：
10.1186/s13059-021-02472-2
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发表时间：
2021-09-06
期刊：
影响因子：
12.3
通讯作者：
Wang K
中科院分区：
文献类型：
--
作者：
Ahsan MU;Liu Q;Fang L;Wang K
长读长测序能够在短读长测序难以定位的基因组区域中进行变异检测。为了充分利用较长读取的优势，我们在这里提出了一种深度学习方法 NanoCaller，该方法使用远程单倍型信息检测 SNP，然后使用调用的 SNP 进行长读取，并通过局部重新比对调用 indel。对 8 个人类基因组的评估表明，NanoCaller 通常比竞争方法取得更好的性能。我们通过实验验证了广泛使用的基准基因组中的 41 个新变异，这些变异以前无法可靠地检测到。总之，NanoCaller 有助于通过长读长测序发现复杂基因组区域中的新变异。在线版本包含可在 10.1186/s13059-021-02472-2 获取的补充材料。
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. The online version contains supplementary material available at 10.1186/s13059-021-02472-2.
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