Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies

Basit Öğe Kaydı
dc.authoridvan der Vlugt, Rene/0000-0001-9094-685X
dc.authoridEichmeier, Ales/0000-0001-7358-3903
dc.authoridULUBAS SERCE, CIGDEM/0000-0001-5337-5883
dc.authoridHaegeman, Annelies/0000-0002-8192-5368
dc.authoridMaree, Hano/0000-0001-9639-4558
dc.authoridDe Jonghe, Kris/0000-0003-1763-5654
dc.authoridTamisier, Lucie/0000-0002-9231-2997
dc.contributor.authorRollin, Johan
dc.contributor.authorBester, Rachelle
dc.contributor.authorBrostaux, Yves
dc.contributor.authorCaglayan, Kadriye
dc.contributor.authorDe Jonghe, Kris
dc.contributor.authorEichmeier, Ales
dc.contributor.authorFoucart, Yoika
dc.date.accessioned2024-11-07T13:35:36Z
dc.date.available2024-11-07T13:35:36Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractRecent 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.
dc.description.sponsorshipCOST (European Cooperation in Science and Technology); European Union [FA1407]; [813542T]
dc.description.sponsorshipThis study is the follow-up of the work on COST Action FA1407 (DIVAS) , supported by COST (European Cooperation in Science and Technology) . This was supported by European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813542T (INEXTVIR) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.identifier.doi10.7717/peerj.15816
dc.identifier.issn2167-8359
dc.identifier.pmid37601254
dc.identifier.scopus2-s2.0-85172734988
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.7717/peerj.15816
dc.identifier.urihttps://hdl.handle.net/11480/16567
dc.identifier.volume11
dc.identifier.wosWOS:001053389700002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPeerj Inc
dc.relation.ispartofPeerj
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectBioinformatic
dc.subjectGenomic
dc.subjectVirus
dc.subjectPlant
dc.subjectVariant
dc.titleDetection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu