Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: 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_____%2F23%3A00584330" target="_blank" >RIV/60077344:_____/23:00584330 - isvavai.cz</a>
Alternative codes found
RIV/62156489:43510/23:43923996 RIV/61989592:15310/23:73620376 RIV/00027006:_____/23:10176332
Result on the web
<a href="https://peerj.com/articles/15816/" target="_blank" >https://peerj.com/articles/15816/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj.15816" target="_blank" >10.7717/peerj.15816</a>
Alternative languages
Result language
angličtina
Original language name
Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies
Original language description
Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research in virology, especially for virus discovery. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS has greatly facilitated the sequencing of new genomes of detected viruses and their comparison. However, bioinformatics analyses allowing reconstruction of genome sequences and detection of single nucleotide polymorphisms (SNPs) can potentially create bias and has not been widely addressed so far. Therefore, more knowledge is required on the limitations of predicting SNPs based on HTS-generated sequence samples. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) in three samples through large-scale performance testing (PT) using three artificially designed datasets. To evaluate the impact of bioinformatics analyses, they were divided into three key steps: reads pre-processing, virus-isolate identification, and variant calling. Each step was evaluated independently through an original, PT design including discussion and validation between participants at each step. Overall, this work underlines key parameters influencing SNPs detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the detection were recognized as the most impactful analysis steps for the success of the SNPs detections. Strategies to improve the prediction of SNPs are also discussed.
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
10607 - Virology
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PeerJ
ISSN
2167-8359
e-ISSN
2167-8359
Volume of the periodical
11
Issue of the periodical within the volume
AUG 16
Country of publishing house
US - UNITED STATES
Number of pages
6
Pages from-to
e15816
UT code for WoS article
001053389700002
EID of the result in the Scopus database
2-s2.0-85172734988