Victor Chang Cardiac Research Institute, Sydney, Australia.
St. Vincent's Clinical School, UNSW Sydney, Sydney, Australia.
Bioinformatics. 2020 Jun 1;36(11):3549-3551. doi: 10.1093/bioinformatics/btaa116.
In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery.
To further improve DeepVariant's variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant.
dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/).
e.giannoulatou@victorchang.edu.au.
Supplementary data are available at Bioinformatics online.
2018 年，谷歌发布了一种创新的变体调用器 DeepVariant，它将测序读取的堆积物转换为图像，并使用深度神经网络从下一代测序数据中识别单核苷酸变体和小插入/缺失。这种方法优于现有的最先进的工具。然而，DeepVariant 被设计用于在单个样本中调用变体。在疾病测序研究中，检查家族三胞胎（父亲-母亲-受影响的孩子）的能力为疾病突变发现提供了更大的动力。
为了进一步提高 DeepVariant 在基于家族的测序研究中的变体调用准确性，我们开发了一种基于家族的变体调用管道 dv-trio，该管道将孟德尔遗传模型中的三胞胎信息纳入基于 DeepVariant 的变体调用中。
dv-trio 通过开源 BSD3 许可证在 GitHub（https://github.com/VCCRI/dv-trio/）上提供。
例如 giannoulatou@victorchang.edu.au。
补充数据可在 Bioinformatics 在线获得。
