MegaPath-Nano: Accurate Compositional Analysis and Drug-level Antimicrobial Resistance Detection Software for Oxford Nanopore Long-read Metagenomics
| Autor: | Yan Xin, Patrick C. Y. Woo, Henry C. M. Leung, Tak-Wah Lam, Ruibang Luo, Wui Wang Lui, Jade L. L. Teng, Amy W. S. Leung |
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| Rok vydání: | 2020 |
| Předmět: |
Profiling (computer programming)
0303 health sciences 030306 microbiology Computer science business.industry Genomics computer.software_genre Turnaround time 03 medical and health sciences Software Metagenomics Minion RefSeq Data mining Nanopore sequencing business computer 030304 developmental biology |
| Zdroj: | BIBM |
| DOI: | 10.1109/bibm49941.2020.9313313 |
| Popis: | Accurate and sensitive taxonomic profiling is essential for any metagenomic analysis to reveal microbial community structure and for potential functional prediction. Antimicrobial resistance (AMR) detection is also a critical task in the clinical diagnosis of infection and antimicrobial therapy. By incorporating Oxford Nanopore Technologies (ONT) sequencing, users benefit from the high-confidence alignment of long reads for taxonomic classification, even among bacteria with similar genomes. Portable ONT devices, such as VolTRAX with MinION, allow short turnaround time for detection and can be used in a lightweight laboratory setting. However, error-prone ONT sequencing reads are still challenging for existing software for accurate taxonomic classification of microbes and detection of AMR down to the drug level. In this paper, we present MegaPath-Nano, the successor to NGS-based MegaPath. It is a high-precision compositional analysis software with drug-level AMR detection for ONT metagenomic sequencing data. MegaPath-Nano performs 1) thorough multi-level filtering against decoy and human reads while removing noisy alignments, 2) alignment-based taxonomic classification with RefSeq down to strain-level, with an alignment-reassignment algorithm to tackle the challenge of non-unique alignments, based on global alignment distribution, and 3) comprehensive downstream drug-level AMR detection, integrating five AMR databases. In our benchmarks using the Zymo metagenomic dataset, MegaPath-Nano performed better than other existing software for taxonomic classification. We also sequenced five real patient isolates using MinION to benchmark its performance of AMR detection. MegaPath-Nano was the most accurate and provided the most comprehensive output at both the drug and class level of AMR prediction against other state-of-the-art software. MegaPath-Nano is open-source and available at https://github.com/HKU-BAL/MegaPath-Nano. |
| Databáze: | OpenAIRE |
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