MegaPath: sensitive and rapid pathogen detection using metagenomic NGS data
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MegaPath: sensitive and rapid pathogen detection using metagenomic NGS data Chi-Ming Leung1,2*†, Dinghua Li1†, Yan Xin1,2†, Wai-Chun Law2, Yifan Zhang1,2, Hing-Fung Ting1, Ruibang Luo1,2 and Tak-Wah Lam1,2 From 8th IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2018) Las Vegas, NV, USA. 18-20 October 2018
Abstract Background: Next-generation sequencing (NGS) enables unbiased detection of pathogens by mapping the sequencing reads of a patient sample to the known reference sequence of bacteria and viruses. However, for a new pathogen without a reference sequence of a close relative, or with a high load of mutations compared to its predecessors, read mapping fails due to a low similarity between the pathogen and reference sequence, which in turn leads to insensitive and inaccurate pathogen detection outcomes. Results: We developed MegaPath, which runs fast and provides high sensitivity in detecting new pathogens. In MegaPath, we have implemented and tested a combination of polishing techniques to remove non-informative human reads and spurious alignments. MegaPath applies a global optimization to the read alignments and reassigns the reads incorrectly aligned to multiple species to a unique species. The reassignment not only significantly increased the number of reads aligned to distant pathogens, but also significantly reduced incorrect alignments. MegaPath implements an enhanced maximum-exact-match prefix seeding strategy and a SIMDaccelerated Smith-Waterman algorithm to run fast. Conclusions: In our benchmarks, MegaPath demonstrated superior sensitivity by detecting eight times more reads from a low-similarity pathogen than other tools. Meanwhile, MegaPath ran much faster than the other state-of-theart alignment-based pathogen detection tools (and compariable with the less sensitivity profile-based pathogen detection tools). The running time of MegaPath is about 20 min on a typical 1 Gb dataset. Keywords: Pathogen detection, Shotgun metagenomic sequencing, Next generation sequencing, Abundance detection, Read alignment
* Correspondence: [email protected] † Chi-Ming Leung, Dinghua Li and Yan Xin are joint first authors. 1 Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong 2 L3 Bioinformatics Limited, Rm 2114, Hong Kong Plaza, 188 Connaught Road West, Sai Ying Pun, Hong Kong © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your inte
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