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

Allesøe, R. L., Lemvigh, C. K., Phan, M. V.T., Clausen, P. T. L.C., Florensa, A. F., Koopmans, M. P.G., Lund, O. and Cotten, M. (2021) Automated download and clean-up of family-specific databases for kmer-based virus identification. Bioinformatics, 37(5), pp. 705-710. (doi: 10.1093/bioinformatics/btaa857) (PMID:33031509) (PMCID:PMC8097684)

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. Availability and implementation: The DONE pipeline for downloading and cleaning is deposited in a publicly available repository (https://bitbucket.org/genomicepidemiology/done/src/master/). Supplementary information: Supplementary data are available at Bioinformatics online.

• Item Type: Articles
• Additional Information: 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].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cotten, Professor Matthew
• Authors: Allesøe, R. L., Lemvigh, C. K., Phan, M. V.T., Clausen, P. T. L.C., Florensa, A. F., Koopmans, M. P.G., Lund, O., and Cotten, M.
• College/School: College of Medical Veterinary and Life Sciences > School of Infection & Immunity; College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
• Journal Name: Bioinformatics
• Publisher: Oxford University Press
• ISSN: 1367-4803
• ISSN (Online): 1460-2059
• Published Online: 08 October 2020
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in Bioinformatics 37(5): 705-710
• Publisher Policy: Reproduced under a Creative Commons License