Virus Detection by High-Throughput Sequencing of Small RNAs: Large-Scale Performance Testing of Sequence Analysis Strategies
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F19%3A00511207" target="_blank" >RIV/60077344:_____/19:00511207 - isvavai.cz</a>
Alternative codes found
RIV/61989592:15310/19:73588482 RIV/00027006:_____/19:00005380
Result on the web
<a href="https://apsjournals.apsnet.org/doi/10.1094/PHYTO-02-18-0067-R" target="_blank" >https://apsjournals.apsnet.org/doi/10.1094/PHYTO-02-18-0067-R</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1094/PHYTO-02-18-0067-R" target="_blank" >10.1094/PHYTO-02-18-0067-R</a>
Alternative languages
Result language
angličtina
Original language name
Virus Detection by High-Throughput Sequencing of Small RNAs: Large-Scale Performance Testing of Sequence Analysis Strategies
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10611 - Plant sciences, botany
Result continuities
Project
<a href="/en/project/LD15163" target="_blank" >LD15163: Application of next generation sequencing for the diagnosis of viruses and virus-like diseases of grapevine</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Phytopathology
ISSN
0031-949X
e-ISSN
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Volume of the periodical
109
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
488-497
UT code for WoS article
000461380300017
EID of the result in the Scopus database
2-s2.0-85054514139