Automated download and clean-up of family-specific databases for kmer-based virus identification

Rosa L. Allesøe*, Camilla K. Lemvigh, My V.T. Phan, Philip T.L.C. Clausen, Alfred F. Florensa, Marion P.G. Koopmans, Ole Lund, Matthew Cotten

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

Summary: Here, we present an automated pipeline for Download Of NCBI Entries (DONE) and continuous updating of a local sequence database based on user-specified queries. The database can be created with either protein or nucleotide sequences containing all entries or complete genomes only. The pipeline can automatically clean the database by removing entries with matches to a database of user-specified sequence contaminants. The default contamination entries include sequences from the UniVec database of plasmids, marker genes and sequencing adapters from NCBI, an E.coli genome, rRNA sequences, vectors and satellite sequences. Furthermore, duplicates are removed and the database is automatically screened for sequences from green fluorescent protein, luciferase and antibiotic resistance genes that might be present in some GenBank viral entries, and could lead to false positives in virus identification. For utilizing the database, we present a useful opportunity for dealing with possible human contamination. We show the applicability of DONE by downloading a virus database comprising 37 virus families. We observed an average increase of 16 776 new entries downloaded per month for the 37 families. In addition, we demonstrate the utility of a custom database compared to a standard reference database for classifying both simulated and real sequence data. Availabilityand implementation: The DONE pipeline for downloading and cleaning is deposited in a publicly available repository (https://bitbucket.org/genomicepidemiology/done/src/master/).

Original languageEnglish
Pages (from-to)705-710
Number of pages6
JournalBioinformatics
Volume37
Issue number5
DOIs
Publication statusPublished - 1 Mar 2021

Bibliographical note

Funding:
This work was supported by the European Union’s Horizon 2020 research
and innovation program [643476] (COMPARE). M.V.T.P. was supported by
Marie Sklodowska-Curie Individual Fellowship, funded by European Union’s
Horizon 2020 research and innovation programme [799417]. This work was
supported by the European Union’s Horizon 2020 research and innovation
programme, VEO [874735]. Novo Nordisk Foundation Center for Protein
Research, University of Copenhagen. The center was supported financially by
the Novo Nordisk Foundation [NNF14CC0001].

Publisher Copyright: © 2021 Oxford University Press. All rights reserved.

Fingerprint

Dive into the research topics of 'Automated download and clean-up of family-specific databases for kmer-based virus identification'. Together they form a unique fingerprint.

Cite this