Metagenomic classification with KrakenUniq on low-memory computers.


Journal

Journal of open source software
ISSN: 2475-9066
Titre abrégé: J Open Source Softw
Pays: United States
ID NLM: 101708638

Informations de publication

Date de publication:
2022
Historique:
medline: 1 1 2022
pubmed: 1 1 2022
entrez: 21 8 2023
Statut: ppublish

Résumé

Kraken and KrakenUniq are widely-used tools for classifying metagenomics sequences. A key requirement for these systems is a database containing all The KrakenUniq software classifies reads from metagenomic samples to establish which organisms are present in the samples and estimate their abundance. The software is widely used used by researchers and clinicians in medical diagnostics, microbiome and environmental studies.Typical databases used by KrakenUniq are tens to hundreds of gigabytes in size. The original KrakenUniq code required loading the entire database in RAM, which demanded expensive high-memory servers to run it efficiently. If a user did not have enough physical RAM to load the entire database, KrakenUniq resorted to memory-mapping the database, which significantly increased run times, frequently by a factor of more than 100. The new functionality described in this paper enables users who do not have access to high-memory servers to run KrakenUniq efficiently, with a CPU time performance increase of 3 to 4-fold, down from 100+.

Identifiants

pubmed: 37602140
doi: 10.21105/joss.04908
pmc: PMC10438097
mid: NIHMS1920964
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : NHGRI NIH HHS
ID : R01 HG006677
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM130151
Pays : United States

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Auteurs

Christopher Pockrandt (C)

Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Aleksey V Zimin (AV)

Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Steven L Salzberg (SL)

Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA.

Classifications MeSH