Virus Detection by High-Throughput Sequencing of Small RNAs: Large-Scale Performance Testing of Sequence Analysis Strategies
Open Access
- 1 March 2019
- journal article
- research article
- Published by Scientific Societies in Phytopathology®
- Vol. 109 (3), 488-497
- https://doi.org/10.1094/phyto-02-18-0067-r
Abstract
Recent developments in high-throughput sequencing (HTS), also called next-generation sequencing (NGS), technologies and bioinformatics have drastically changed research on viral pathogens and spurred growing interest in the field of virus diagnostics. However, the reliability of HTS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at detecting viruses in HTS data have been reported but little attention has been paid thus far to their sensitivity and reliability for diagnostic purposes. Therefore, we compared the ability of 21 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 12 plant viruses through a double-blind large-scale performance test using 10 datasets of 21- to 24-nucleotide small RNA (sRNA) sequences from three different infected plants. The sensitivity of virus detection ranged between 35 and 100% among participants, with a marked negative effect when sequence depth decreased. The false-positive detection rate was very low and mainly related to the identification of host genome-integrated viral sequences or misinterpretation of the results. Reproducibility was high (91.6%). This work revealed the key influence of bioinformatics strategies for the sensitive detection of viruses in HTS sRNA datasets and, more specifically (i) the difficulty in detecting viral agents when they are novel or their sRNA abundance is low, (ii) the influence of key parameters at both assembly and annotation steps, (iii) the importance of completeness of reference sequence databases, and (iv) the significant level of scientific expertise needed when interpreting pipeline results. Overall, this work underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.Keywords
Funding Information
- European Cooperation in Science and Technology (COST Action FA1407 (DIVAS))
This publication has 29 references indexed in Scilit:
- Historical Perspective, Development and Applications of Next-Generation Sequencing in Plant VirologyViruses, 2014
- MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence readsNucleic Acids Research, 2012
- Oases:robustde novoRNA-seq assembly across the dynamic range of expression levelsBioinformatics, 2012
- Fast and accurate short read alignment with Burrows–Wheeler transformBioinformatics, 2009
- ABySS: A parallel assembler for short read sequence dataGenome Research, 2009
- PAnnBuilder: an R package for assembling proteomic annotation dataBioinformatics, 2009
- Database indexing for production MegaBLAST searchesBioinformatics, 2008
- Velvet: Algorithms for de novo short read assembly using de Bruijn graphsGenome Research, 2008
- Antiviral Immunity Directed by Small RNAsCell, 2007
- Basic local alignment search toolJournal of Molecular Biology, 1990