Bioinformatic; Contamination; Detection; Metagenomic; Sequencing; Virus; RNA, Viral; Humans; Reproducibility of Results; Metagenomics/methods; Computational Biology; High-Throughput Nucleotide Sequencing/methods; RNA, Viral/analysis; RNA, Viral/genetics; Viruses/genetics; High-Throughput Nucleotide Sequencing; Metagenomics; Viruses; Biotechnology; Structural Biology; Ecology, Evolution, Behavior and Systematics; Physiology; Biochemistry, Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all); Plant Science; Developmental Biology; Cell Biology; General Agricultural and Biological Sciences; General Biochemistry, Genetics and Molecular Biology
Abstract :
[en] [en] BACKGROUND: High-throughput sequencing (HTS) technologies completed by the bioinformatic analysis of the generated data are becoming an important detection technique for virus diagnostics. They have the potential to replace or complement the current PCR-based methods thanks to their improved inclusivity and analytical sensitivity, as well as their overall good repeatability and reproducibility. Cross-contamination is a well-known phenomenon in molecular diagnostics and corresponds to the exchange of genetic material between samples. Cross-contamination management was a key drawback during the development of PCR-based detection and is now adequately monitored in routine diagnostics. HTS technologies are facing similar difficulties due to their very high analytical sensitivity. As a single viral read could be detected in millions of sequencing reads, it is mandatory to fix a detection threshold that will be informed by estimated cross-contamination. Cross-contamination monitoring should therefore be a priority when detecting viruses by HTS technologies.
RESULTS: We present Cont-ID, a bioinformatic tool designed to check for cross-contamination by analysing the relative abundance of virus sequencing reads identified in sequence metagenomic datasets and their duplication between samples. It can be applied when the samples in a sequencing batch have been processed in parallel in the laboratory and with at least one specific external control called Alien control. Using 273 real datasets, including 68 virus species from different hosts (fruit tree, plant, human) and several library preparation protocols (Ribodepleted total RNA, small RNA and double-stranded RNA), we demonstrated that Cont-ID classifies with high accuracy (91%) viral species detection into (true) infection or (cross) contamination. This classification raises confidence in the detection and facilitates the downstream interpretation and confirmation of the results by prioritising the virus detections that should be confirmed.
CONCLUSIONS: Cross-contamination between samples when detecting viruses using HTS (Illumina technology) can be monitored and highlighted by Cont-ID (provided an alien control is present). Cont-ID is based on a flexible methodology relying on the output of bioinformatics analyses of the sequencing reads and considering the contamination pattern specific to each batch of samples. The Cont-ID method is adaptable so that each laboratory can optimise it before its validation and routine use.
Disciplines :
Biochemistry, biophysics & molecular biology
Author, co-author :
Rollin, Johan; Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, 5030, Gembloux, Belgium ; DNAVision, 6041, Gosselies, Belgium
Rong, Wei; Plant Pathology Laboratory, Gembloux Agro-Bio Tech, University of Liège, 5030, Gembloux, Belgium
Massart, Sébastien ; Université de Liège - ULiège > TERRA Research Centre > Gestion durable des bio-agresseurs
Language :
English
Title :
Cont-ID: detection of sample cross-contamination in viral metagenomic data.
H2020 - 813542 - INEXTVIR - Innovative Network for Next Generation Training and Sequencing of Virome
Funders :
EU - European Union
Funding text :
We thank Angelo Locicero for technical support and Gladys Rufflard for administrative support. Delphine Masse (ANSES, La Réunion, France), Kathy Crew and John Thomas (Queensland Alliance for Agriculture and Food Innovation, Brisbane, Australia) and Mathieu Chabannes and Marilyne Caruana (CIRAD, Montpellier, France) are also acknowledged for kindly providing Musa reference samples. Special thanks to Marie-Emilie Gauthier and Roberto Barrero for providing dataset G and discussing cross-contamination in viral metagenomes with us.The work has been supported by (1) the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 813542 T (INEXTVIR) and (2) the NGS cross-centre project from the CGIAR Fund and in particular by the Germplasm Health Unit (GHU) of the CGIAR Genebank Platform.
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