Contamination detection in genomic data: more is not enough.
Luc CornetDenis BaurainPublished in: Genome biology (2022)
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.
Keyphrases
- risk assessment
- drinking water
- health risk
- machine learning
- loop mediated isothermal amplification
- public health
- deep learning
- human health
- copy number
- liver failure
- label free
- high throughput
- single cell
- electronic health record
- big data
- respiratory failure
- randomized controlled trial
- current status
- gene expression
- dna methylation
- drug induced
- artificial intelligence
- systematic review
- soft tissue
- extracorporeal membrane oxygenation
- aortic dissection
- meta analyses
- quantum dots
- sensitive detection