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Automated download and clean-up of family-specific databases for kmer-based virus identification.
Allesøe, Rosa L; Lemvigh, Camilla K; Phan, My V T; Clausen, Philip T L C; Florensa, Alfred F; Koopmans, Marion P G; Lund, Ole; Cotten, Matthew.
Afiliação
  • Allesøe RL; National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
  • Lemvigh CK; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
  • Phan MVT; National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
  • Clausen PTLC; Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
  • Florensa AF; Department of Viroscience, Erasmus University Medical Centre, 3000 CA Rotterdam, The Netherlands.
  • Koopmans MPG; National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
  • Lund O; National Food Institute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
  • Cotten M; Department of Viroscience, Erasmus University Medical Centre, 3000 CA Rotterdam, The Netherlands.
Bioinformatics ; 37(5): 705-710, 2021 05 05.
Article em En | MEDLINE | ID: mdl-33031509
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bases de Dados Genéticas / Bases de Dados de Ácidos Nucleicos Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bases de Dados Genéticas / Bases de Dados de Ácidos Nucleicos Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article