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MegaPath: sensitive and rapid pathogen detection using metagenomic NGS data

Authors :
Chi-Ming Leung
Dinghua Li
Yan Xin
Wai-Chun Law
Yifan Zhang
Hing-Fung Ting
Ruibang Luo
Tak-Wah Lam
Source :
BMC Genomics, Vol 21, Iss S6, Pp 1-9 (2020)
Publication Year :
2020
Publisher :
BMC, 2020.

Abstract

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 SIMD-accelerated 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-the-art 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.

Details

Language :
English
ISSN :
14712164
Volume :
21
Issue :
S6
Database :
Directory of Open Access Journals
Journal :
BMC Genomics
Publication Type :
Academic Journal
Accession number :
edsdoj.69803fca3ca6439dbe50efc33aebce2f
Document Type :
article
Full Text :
https://doi.org/10.1186/s12864-020-06875-6