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Virus detection by high-throughput sequencing of small RNAs: Large-scale performance testing of sequence analysis strategies. / Massart, Sebastien; Chiumenti, Michela; De Jonghe, Kris; Clover, Rachel; Haegeman, Annelies; Koloniuk, Igor; Kominek, Petr; Kreuze, Jan; Kutnjak, Denis; Lotos, Leonidas; Maclot, François; Maliogka, Varvara; Maree, Hano; Olivier, Thibaut; Olmos, Antonio; Pooggin, Mikhail; Reynard, Jean-Sébastien; Ruiz-Garcia, Anna; Safarova, Dana; Schneeberger, Pierre; Sela, Noa; Turco, Sylvia; Vainio, Eeva ; Varallyay, Eva; Verdin, Eric; Westenberg, Marcel; Brosteaux, Yves; Candresse, Thierry.

In: Phytopathology, 08.02.2019.

Onderzoeksoutput: Bijdrage aan tijdschriftA1: Web of Science-artikelOnderzoekpeer review

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Massart, S, Chiumenti, M, De Jonghe, K, Clover, R, Haegeman, A, Koloniuk, I, Kominek, P, Kreuze, J, Kutnjak, D, Lotos, L, Maclot, F, Maliogka, V, Maree, H, Olivier, T, Olmos, A, Pooggin, M, Reynard, J-S, Ruiz-Garcia, A, Safarova, D, Schneeberger, P, Sela, N, Turco, S, Vainio, E, Varallyay, E, Verdin, E, Westenberg, M, Brosteaux, Y & Candresse, T 2019, 'Virus detection by high-throughput sequencing of small RNAs: Large-scale performance testing of sequence analysis strategies' Phytopathology. https://doi.org/https://apsjournals.apsnet.org/doi/10.1094/PHYTO-02-18-0067-R

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Vancouver

Author

Massart, Sebastien ; Chiumenti, Michela ; De Jonghe, Kris ; Clover, Rachel ; Haegeman, Annelies ; Koloniuk, Igor ; Kominek, Petr ; Kreuze, Jan ; Kutnjak, Denis ; Lotos, Leonidas ; Maclot, François ; Maliogka, Varvara ; Maree, Hano ; Olivier, Thibaut ; Olmos, Antonio ; Pooggin, Mikhail ; Reynard, Jean-Sébastien ; Ruiz-Garcia, Anna ; Safarova, Dana ; Schneeberger, Pierre ; Sela, Noa ; Turco, Sylvia ; Vainio, Eeva ; Varallyay, Eva ; Verdin, Eric ; Westenberg, Marcel ; Brosteaux, Yves ; Candresse, Thierry. / Virus detection by high-throughput sequencing of small RNAs: Large-scale performance testing of sequence analysis strategies. In: Phytopathology. 2019.

Bibtex

@article{13b64559eea8483083bd2b50ebffaded,
title = "Virus detection by high-throughput sequencing of small RNAs: Large-scale performance testing of sequence analysis strategies",
abstract = "Recent developments in high throughput, 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 NGS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at tracking viruses in NGS data have been reported, but little attention has been paid so far to their sensitivity and reliability for diagnostic purposes. We therefore compared the performance of existing bioinformatics pipelines through a double blind large scale performance test involving 21 participants from 16 countries and using ten datasets of 21-24 nt small (s)RNA 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 numbers 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 that (i) the complex nature of virus detection and new viruses could be discovered using sRNAs, (ii) it is difficult to detect viral agents when sRNA abundance is low, (iii) reference sequence databases can be inconsistent for virus detection, and (iv) scientific expertise is important when interpreting diagnostic results. Overall, this work brings valuable insights into the reliability of bioinformatics pipelines and the impact of end-user and database completeness on the results. It also, underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.",
author = "Sebastien Massart and Michela Chiumenti and {De Jonghe}, Kris and Rachel Clover and Annelies Haegeman and Igor Koloniuk and Petr Kominek and Jan Kreuze and Denis Kutnjak and Leonidas Lotos and Fran{\cc}ois Maclot and Varvara Maliogka and Hano Maree and Thibaut Olivier and Antonio Olmos and Mikhail Pooggin and Jean-S{\'e}bastien Reynard and Anna Ruiz-Garcia and Dana Safarova and Pierre Schneeberger and Noa Sela and Sylvia Turco and Eeva Vainio and Eva Varallyay and Eric Verdin and Marcel Westenberg and Yves Brosteaux and Thierry Candresse",
year = "2019",
month = "2",
day = "8",
doi = "https://apsjournals.apsnet.org/doi/10.1094/PHYTO-02-18-0067-R",
language = "English",
journal = "Phytopathology",
issn = "0031-949X",
publisher = "AMER PHYTOPATHOLOGICAL SOC",

}

RIS

TY - JOUR

T1 - Virus detection by high-throughput sequencing of small RNAs: Large-scale performance testing of sequence analysis strategies

AU - Massart, Sebastien

AU - Chiumenti, Michela

AU - De Jonghe, Kris

AU - Clover, Rachel

AU - Haegeman, Annelies

AU - Koloniuk, Igor

AU - Kominek, Petr

AU - Kreuze, Jan

AU - Kutnjak, Denis

AU - Lotos, Leonidas

AU - Maclot, François

AU - Maliogka, Varvara

AU - Maree, Hano

AU - Olivier, Thibaut

AU - Olmos, Antonio

AU - Pooggin, Mikhail

AU - Reynard, Jean-Sébastien

AU - Ruiz-Garcia, Anna

AU - Safarova, Dana

AU - Schneeberger, Pierre

AU - Sela, Noa

AU - Turco, Sylvia

AU - Vainio, Eeva

AU - Varallyay, Eva

AU - Verdin, Eric

AU - Westenberg, Marcel

AU - Brosteaux, Yves

AU - Candresse, Thierry

PY - 2019/2/8

Y1 - 2019/2/8

N2 - Recent developments in high throughput, 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 NGS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at tracking viruses in NGS data have been reported, but little attention has been paid so far to their sensitivity and reliability for diagnostic purposes. We therefore compared the performance of existing bioinformatics pipelines through a double blind large scale performance test involving 21 participants from 16 countries and using ten datasets of 21-24 nt small (s)RNA 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 numbers 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 that (i) the complex nature of virus detection and new viruses could be discovered using sRNAs, (ii) it is difficult to detect viral agents when sRNA abundance is low, (iii) reference sequence databases can be inconsistent for virus detection, and (iv) scientific expertise is important when interpreting diagnostic results. Overall, this work brings valuable insights into the reliability of bioinformatics pipelines and the impact of end-user and database completeness on the results. It also, underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.

AB - Recent developments in high throughput, 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 NGS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at tracking viruses in NGS data have been reported, but little attention has been paid so far to their sensitivity and reliability for diagnostic purposes. We therefore compared the performance of existing bioinformatics pipelines through a double blind large scale performance test involving 21 participants from 16 countries and using ten datasets of 21-24 nt small (s)RNA 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 numbers 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 that (i) the complex nature of virus detection and new viruses could be discovered using sRNAs, (ii) it is difficult to detect viral agents when sRNA abundance is low, (iii) reference sequence databases can be inconsistent for virus detection, and (iv) scientific expertise is important when interpreting diagnostic results. Overall, this work brings valuable insights into the reliability of bioinformatics pipelines and the impact of end-user and database completeness on the results. It also, underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.

U2 - https://apsjournals.apsnet.org/doi/10.1094/PHYTO-02-18-0067-R

DO - https://apsjournals.apsnet.org/doi/10.1094/PHYTO-02-18-0067-R

M3 - A1: Web of Science-article

JO - Phytopathology

JF - Phytopathology

SN - 0031-949X

ER -