A universal SNP and small-indel variant caller using deep neural networks
A universal SNP and small-indel variant caller using deep neural networks
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DOI：
10.1038/nbt.4235
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发表时间：
2018-10-01
影响因子：
46.9
通讯作者：
DePristo, Mark A.
中科院分区：
文献类型：
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作者：
Poplin, Ryan;Chang, Pi-Chuan;DePristo, Mark A.
尽管测序技术发展迅速，但从数十亿个错误的短序列中准确地识别个体基因组中存在的遗传变异仍然具有挑战性。在这里，我们展示了深度卷积神经网络可以通过学习围绕假定变体和真实基因型调用的读取堆图像之间的统计关系来调用对齐的下一代测序读取数据中的遗传变异。这种被称为DeepVariant的方法优于现有的最先进的工具。该学习模型可推广到基因组构建和哺乳动物物种，使非人类测序项目受益于丰富的人类基础事实数据。我们进一步表明，DeepVariant可以学习调用各种测序技术和实验设计中的变体，包括来自10X Genomics和Ion Ampliseq的深层全基因组外显子组，突出了使用更自动化和通用的变体调用技术的好处。
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.