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

Author:

Allesøe Rosa L12,Lemvigh Camilla K13,Phan My V T4ORCID,Clausen Philip T L C1,Florensa Alfred F1,Koopmans Marion P G4,Lund Ole1,Cotten Matthew456

Affiliation:

1. National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

2. Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, Denmark

3. Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark

4. Department of Viroscience, Erasmus University Medical Centre, 3000 CA Rotterdam, The Netherlands

5. MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda

6. MRC-University of Glasgow Centre for Virus Research, G61 1QH Scotland, UK

Abstract

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/). Supplementary information Supplementary data are available at Bioinformatics online.

Funder

European Union’s Horizon 2020 research and innovation program

Marie Sklodowska-Curie Individual Fellowship

European Union’s Horizon 2020 research and innovation programme

Novo Nordisk Foundation Center for Protein Research

Novo Nordisk Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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