dv-trio：一种基于家系的使用 DeepVariant 的变异calling 流程。

dv-trio: a family-based variant calling pipeline using DeepVariant.

机构信息

Victor Chang Cardiac Research Institute, Sydney, Australia.

St. Vincent's Clinical School, UNSW Sydney, Sydney, Australia.

出版信息

Bioinformatics. 2020 Jun 1;36(11):3549-3551. doi: 10.1093/bioinformatics/btaa116.

DOI:10.1093/bioinformatics/btaa116

PMID:32315409

Abstract

MOTIVATION

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.

RESULTS

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.

AVAILABILITY AND IMPLEMENTATION

dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/).

CONTACT

e.giannoulatou@victorchang.edu.au.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

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。