Article (Scientific journals)
Contamination detection in genomic data: more is not enough
Cornet, Luc; Baurain, Denis
2022In Genome Biology, 23 (1), p. 60
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Keywords :
Contamination detection; Genomics; Databases; Algorithms; Review; Corroboration
Abstract :
[en] 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 six years, 18 programs have been pub-lished, each with its own strengths and weaknesses. Deciding which tools to use becomes more and more difficult without an understanding of the underlying algo-rithms. We review these programs, benchmarking six of them, and present their main operating principles. This article is intended to guide researchers in the selec-tion of appropriate tools for specific applications. Finally, we present future chal-lenges in the developing field of contamination detection.
Research center :
Sciensano - BCCM - University of Liege
Disciplines :
Microbiology
Biochemistry, biophysics & molecular biology
Genetics & genetic processes
Author, co-author :
Cornet, Luc ;  Sciensano > BCCM/IHEM, Mycology and Aerobiology
Baurain, Denis  ;  Université de Liège - ULiège > Département des sciences de la vie > Phylogénomique des eucaryotes
Language :
English
Title :
Contamination detection in genomic data: more is not enough
Publication date :
21 February 2022
Journal title :
Genome Biology
ISSN :
1474-7596
eISSN :
1474-760X
Publisher :
Springer, London, United Kingdom
Volume :
23
Issue :
1
Pages :
60
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
Name of the research project :
BCCM GEN-ERA
Funders :
Politique Scientifique Fédérale (Belgique) - BELSPO
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
CÉCI - Consortium des Équipements de Calcul Intensif [BE]
Available on ORBi :
since 19 January 2022

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