Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan.
Bioinformatics. 2020 May 1;36(9):2725-2730. doi: 10.1093/bioinformatics/btaa059.
Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives.
Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy.
Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect.
Supplementary data are available at Bioinformatics online.
高通量测序数据的遗传变异调用已被认为是更好地理解疾病机制和检测基因组编辑中潜在脱靶位点的有用工具。由于大多数变异调用算法依赖于初始映射到参考基因组，并倾向于预测许多变异候选者，因此在预测低假阳性变异方面，变异调用仍然具有挑战性。
在这里，我们提出了 Bivartect，这是一种基于正常和突变样本之间短序列读取直接比较的简单而通用的变异调用器。Bivartect 不仅可以检测单核苷酸变异，还可以检测插入/缺失、倒位及其复合物。Bivartect 通过精心设计的节省内存的机制实现了高预测性能，允许 Bivartect 在单个节点的计算机上运行，用于分析小型组学数据。使用模拟基准和真实基因组编辑数据的测试表明，Bivartect 在检测单核苷酸变异的阳性预测值方面与最先进的变异调用器相当，尽管它产生的候选者数量要少得多。这些结果表明，Bivartect 是一种无参考的方法，将有助于以高精度识别生殖系突变以及基因组编辑过程中引入的脱靶位点。
Bivartect 是用 C++编写的，并在 https://github.com/ykat0/bivartect 上提供了虚拟模拟数据。
补充数据可在 Bioinformatics 在线获取。
