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Article: MegaPath: sensitive and rapid pathogen detection using metagenomic NGS data

TitleMegaPath: sensitive and rapid pathogen detection using metagenomic NGS data
Authors
KeywordsAbundance detection
Next generation sequencing
Pathogen detection
Read alignment
Shotgun metagenomic sequencing
Issue Date2020
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcgenomics/
Citation
BMC Genomics, 2020, v. 21 n. suppl. 6, p. article no. 500 How to Cite?
AbstractBackground 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.
DescriptionSelected articles from the 8th IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2018): genomics
Persistent Identifierhttp://hdl.handle.net/10722/301331
ISSN
2023 Impact Factor: 3.5
2023 SCImago Journal Rankings: 1.047
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLeung, CM-
dc.contributor.authorLI, D-
dc.contributor.authorXin, Y-
dc.contributor.authorLaw, WC-
dc.contributor.authorZhang, Y-
dc.contributor.authorTing, HF-
dc.contributor.authorLuo, R-
dc.contributor.authorLam, TW-
dc.date.accessioned2021-07-27T08:09:31Z-
dc.date.available2021-07-27T08:09:31Z-
dc.date.issued2020-
dc.identifier.citationBMC Genomics, 2020, v. 21 n. suppl. 6, p. article no. 500-
dc.identifier.issn1471-2164-
dc.identifier.urihttp://hdl.handle.net/10722/301331-
dc.descriptionSelected articles from the 8th IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2018): genomics-
dc.description.abstractBackground 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.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcgenomics/-
dc.relation.ispartofBMC Genomics-
dc.rightsBMC Genomics. Copyright © BioMed Central Ltd.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectAbundance detection-
dc.subjectNext generation sequencing-
dc.subjectPathogen detection-
dc.subjectRead alignment-
dc.subjectShotgun metagenomic sequencing-
dc.titleMegaPath: sensitive and rapid pathogen detection using metagenomic NGS data-
dc.typeArticle-
dc.identifier.emailTing, HF: hfting@cs.hku.hk-
dc.identifier.emailLuo, R: rbluo@cs.hku.hk-
dc.identifier.emailLam, TW: twlam@cs.hku.hk-
dc.identifier.authorityLeung, CM=rp00144-
dc.identifier.authorityTing, HF=rp00177-
dc.identifier.authorityLuo, R=rp02360-
dc.identifier.authorityLam, TW=rp00135-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s12864-020-06875-6-
dc.identifier.pmid33349238-
dc.identifier.pmcidPMC7751095-
dc.identifier.scopuseid_2-s2.0-85097903116-
dc.identifier.hkuros323497-
dc.identifier.volume21-
dc.identifier.issuesuppl. 6-
dc.identifier.spagearticle no. 500-
dc.identifier.epagearticle no. 500-
dc.identifier.isiWOS:000602687200003-
dc.publisher.placeUnited Kingdom-

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