Contamination detection in genomic data: more is not enough.
Algorithms
Contamination detection
Corroboration
Databases
Genomics
Review
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
21 02 2022
21 02 2022
Historique:
received:
14
10
2021
accepted:
18
01
2022
entrez:
22
2
2022
pubmed:
23
2
2022
medline:
3
5
2022
Statut:
epublish
Résumé
The decreasing cost of sequencing and concomitant augmentation of publicly available genomes have created an acute need for automated software to assess genomic contamination. During the last 6 years, 18 programs have been published, each with its own strengths and weaknesses. Deciding which tools to use becomes more and more difficult without an understanding of the underlying algorithms. We review these programs, benchmarking six of them, and present their main operating principles. This article is intended to guide researchers in the selection of appropriate tools for specific applications. Finally, we present future challenges in the developing field of contamination detection.
Identifiants
pubmed: 35189924
doi: 10.1186/s13059-022-02619-9
pii: 10.1186/s13059-022-02619-9
pmc: PMC8862208
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
60Informations de copyright
© 2022. The Author(s).
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