Bivartect: accurate and memory-saving breakpoint detection by direct read comparison
Bivartect: accurate and memory-saving breakpoint detection by direct read comparison
复制标题
Bivartect：通过直接读取比较进行准确且节省内存的断点检测
DOI：
10.1093/bioinformatics/btaa059
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发表时间：
2020
期刊：
影响因子：
5.8
通讯作者：
Yuki Kato and Yukio Kawahara
中科院分区：
文献类型：
--
作者：
Keisuke Shimmura;Yuki Kato and Yukio Kawahara
动机利用高通量测序数据进行基因变异调用已被认为是更好地了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。由于大多数变体调用算法依赖于参考基因组的初始映射，并且倾向于预测许多候选变体，因此变体调用在预测误报率低的变体方面仍然具有挑战性。结果在这里，我们提出 Bivartect，一种简单但通用的变体调用器，基于正常样本和突变样本之间的短序列读取的直接比较。 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 通过精心设计的内存节省机制实现了高预测性能，这使得 Bivartect 可以在具有单个节点的计算机上运行以分析小型组学数据。使用模拟基准和真实基因组编辑数据进行的测试表明，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识别器相当，尽管它产生的候选者数量相当少。这些结果表明，Bivartect，一种无参考方法，将有助于高精度识别种系突变以及基因组编辑过程中引入的脱靶位点。可用性和实现 Bivartect 用 C++ 实现，可在 https://github.com/ykat0/bivartect 上的硅模拟数据中获得。补充信息补充数据可在 Bioinformaticsonline 上获得。
MotivationGenetic 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.ResultsHere 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.Availability and implementationBivartect is implemented in C++and available along within silicosimulated data at https://github.com/ykat0/bivartect.Supplementary informationSupplementary data are available atBioinformaticsonline.