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

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 pipel...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8097684/
https://www.ncbi.nlm.nih.gov/pubmed/33031509
http://dx.doi.org/10.1093/bioinformatics/btaa857
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author 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
author_facet 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
author_sort Allesøe, Rosa L
collection PubMed
description 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.
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spelling pubmed-80976842021-05-10 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 Bioinformatics Original Papers 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. Oxford University Press 2020-10-08 /pmc/articles/PMC8097684/ /pubmed/33031509 http://dx.doi.org/10.1093/bioinformatics/btaa857 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
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
Automated download and clean-up of family-specific databases for kmer-based virus identification
title Automated download and clean-up of family-specific databases for kmer-based virus identification
title_full Automated download and clean-up of family-specific databases for kmer-based virus identification
title_fullStr Automated download and clean-up of family-specific databases for kmer-based virus identification
title_full_unstemmed Automated download and clean-up of family-specific databases for kmer-based virus identification
title_short Automated download and clean-up of family-specific databases for kmer-based virus identification
title_sort automated download and clean-up of family-specific databases for kmer-based virus identification
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8097684/
https://www.ncbi.nlm.nih.gov/pubmed/33031509
http://dx.doi.org/10.1093/bioinformatics/btaa857
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