Roosaare Märt, Puustusmaa Mikk, Möls Märt, Vaher Mihkel, Remm Maido
Department of Bioinformatics, IMCB, University of Tartu, Tartu, Estonia.
Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia.
PeerJ. 2018 Apr 2;6:e4588. doi: 10.7717/peerj.4588. eCollection 2018.
Plasmids play an important role in the dissemination of antibiotic resistance, making their detection an important task. Using whole genome sequencing (WGS), it is possible to capture both bacterial and plasmid sequence data, but short read lengths make plasmid detection a complex problem.
We developed a tool named PlasmidSeeker that enables the detection of plasmids from bacterial WGS data without read assembly. The PlasmidSeeker algorithm is based on -mers and uses -mer abundance to distinguish between plasmid and bacterial sequences. We tested the performance of PlasmidSeeker on a set of simulated and real bacterial WGS samples, resulting in 100% sensitivity and 99.98% specificity.
PlasmidSeeker enables quick detection of known plasmids and complements existing tools that assemble plasmids de novo. The PlasmidSeeker source code is stored on GitHub: https://github.com/bioinfo-ut/PlasmidSeeker.
质粒在抗生素耐药性传播中起重要作用，因此对其进行检测是一项重要任务。利用全基因组测序（WGS），可以获取细菌和质粒序列数据，但短读长使得质粒检测成为一个复杂问题。
我们开发了一种名为PlasmidSeeker的工具，它能够在不进行读段组装的情况下从细菌WGS数据中检测质粒。PlasmidSeeker算法基于k-mer，并利用k-mer丰度来区分质粒和细菌序列。我们在一组模拟和真实的细菌WGS样本上测试了PlasmidSeeker的性能，灵敏度达到100%，特异性达到99.98%。
PlasmidSeeker能够快速检测已知质粒，并补充了现有的从头组装质粒的工具。PlasmidSeeker的源代码存储在GitHub上：https://github.com/bioinfo-ut/PlasmidSeeker。
