Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

Lucie Tamisier; Annelies Haegeman; Yoika Foucart; Nicolas Fouillien; Maher Al Rwahnih; Nihal  Buzkan; Thierry Candresse; Michela Chiumenti; Kris De Jonghe; Marie Lefebvre; Paolo Margaria; Jean-Sébastien Reynard; Kristian Stevens; Denis Kutnjak; Sebastien Massart

doi:10.24072/pci.genomics.100007

Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

Lucie Tamisier, Annelies Haegeman, Yoika Foucart, Nicolas Fouillien, Maher Al Rwahnih, Nihal Buzkan, Thierry Candresse, Michela Chiumenti, Kris De Jonghe, Marie Lefebvre, Paolo Margaria, Jean-Sébastien Reynard, Kristian Stevens, Denis Kutnjak, Sebastien Massart

Plant

Onderzoeksoutput: Bijdrage aan tijdschrift › A2: Artikel in een internationaal wetenschappelijk tijdschrift met peer review, dat niet inbegrepen is in A1 › peer review

Uittreksel

The widespread use of High-Throughput Sequencing (HTS) for detection of plant viruses
and sequencing of plant virus genomes has led to the generation of large amounts of data
and of bioinformatics challenges to process them. Many bioinformatics pipelines for virus
detection are available, making the choice of a suitable one difficult. A robust
benchmarking is needed for the unbiased comparison of the pipelines, but there is
currently a lack of reference datasets that could be used for this purpose. We present 7
semi-artificial datasets composed of real RNA-seq datasets from virus-infected plants
spiked with artificial virus reads. Each dataset addresses challenges that could prevent virus
detection. We also present 3 real datasets showing a challenging virus composition as well
as 8 completely artificial datasets to test haplotype reconstruction software. With these
datasets that address several diagnostic challenges, we hope to encourage virologists,
diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).

Oorspronkelijke taal	Engels
Artikel nummer	e53
Tijdschrift	Peer community Journal
Volume	1
Exemplaarnummer	1
Pagina's (van-tot)	1-15
Aantal pagina’s	15
ISSN	2804-3871
DOI's	https://doi.org/10.24072/pci.genomics.100007 https://doi.org/10.5281/zenodo.4584718
Publicatiestatus	Gepubliceerd - 2-dec.-2021

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1 Afgerond

PHBN : Plantengezondheid bioinformatica netwerk
Haegeman, A. (Onderzoeker), Van Laecke, K. (Projectverantwoordelijke), De Jonghe, K. (Onderzoeker) & Schaumont, D. (Voormalig Onderzoeker)
1/09/19 → 28/02/22
Project: Onderzoek

Dit citeren

Tamisier, L., Haegeman, A., Foucart, Y., Fouillien, N., Al Rwahnih, M., Buzkan, N., Candresse, T., Chiumenti, M., De Jonghe, K., Lefebvre, M., Margaria, P., Reynard, J.-S., Stevens, K., Kutnjak, D., & Massart, S. (2021). Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. Peer community Journal, 1(1), 1-15. Artikel e53. https://doi.org/10.24072/pci.genomics.100007, https://doi.org/10.5281/zenodo.4584718

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title = "Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection",

abstract = "The widespread use of High-Throughput Sequencing (HTS) for detection of plant virusesand sequencing of plant virus genomes has led to the generation of large amounts of dataand of bioinformatics challenges to process them. Many bioinformatics pipelines for virusdetection are available, making the choice of a suitable one difficult. A robustbenchmarking is needed for the unbiased comparison of the pipelines, but there iscurrently a lack of reference datasets that could be used for this purpose. We present 7semi-artificial datasets composed of real RNA-seq datasets from virus-infected plantsspiked with artificial virus reads. Each dataset addresses challenges that could prevent virusdetection. We also present 3 real datasets showing a challenging virus composition as wellas 8 completely artificial datasets to test haplotype reconstruction software. With thesedatasets that address several diagnostic challenges, we hope to encourage virologists,diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).",

author = "Lucie Tamisier and Annelies Haegeman and Yoika Foucart and Nicolas Fouillien and {Al Rwahnih}, Maher and Nihal Buzkan and Thierry Candresse and Michela Chiumenti and {De Jonghe}, Kris and Marie Lefebvre and Paolo Margaria and Jean-S{\'e}bastien Reynard and Kristian Stevens and Denis Kutnjak and Sebastien Massart",

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doi = "10.24072/pci.genomics.100007",

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Tamisier, L, Haegeman, A , Foucart, Y, Fouillien, N, Al Rwahnih, M, Buzkan, N, Candresse, T, Chiumenti, M, De Jonghe, K, Lefebvre, M, Margaria, P, Reynard, J-S, Stevens, K, Kutnjak, D & Massart, S 2021, 'Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection', Peer community Journal, vol. 1, nr. 1, e53, blz. 1-15. https://doi.org/10.24072/pci.genomics.100007, https://doi.org/10.5281/zenodo.4584718

Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection. / Tamisier, Lucie; Haegeman, Annelies ; Foucart, Yoika et al.
In: Peer community Journal, Vol. 1, Nr. 1, e53, 02.12.2021, blz. 1-15.

Onderzoeksoutput: Bijdrage aan tijdschrift › A2: Artikel in een internationaal wetenschappelijk tijdschrift met peer review, dat niet inbegrepen is in A1 › peer review

TY - JOUR

T1 - Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

AU - Tamisier, Lucie

AU - Haegeman, Annelies

AU - Foucart, Yoika

AU - Fouillien, Nicolas

AU - Al Rwahnih, Maher

AU - Buzkan, Nihal

AU - Candresse, Thierry

AU - Chiumenti, Michela

AU - De Jonghe, Kris

AU - Lefebvre, Marie

AU - Margaria, Paolo

AU - Reynard, Jean-Sébastien

AU - Stevens, Kristian

AU - Kutnjak, Denis

AU - Massart, Sebastien

PY - 2021/12/2

Y1 - 2021/12/2

N2 - The widespread use of High-Throughput Sequencing (HTS) for detection of plant virusesand sequencing of plant virus genomes has led to the generation of large amounts of dataand of bioinformatics challenges to process them. Many bioinformatics pipelines for virusdetection are available, making the choice of a suitable one difficult. A robustbenchmarking is needed for the unbiased comparison of the pipelines, but there iscurrently a lack of reference datasets that could be used for this purpose. We present 7semi-artificial datasets composed of real RNA-seq datasets from virus-infected plantsspiked with artificial virus reads. Each dataset addresses challenges that could prevent virusdetection. We also present 3 real datasets showing a challenging virus composition as wellas 8 completely artificial datasets to test haplotype reconstruction software. With thesedatasets that address several diagnostic challenges, we hope to encourage virologists,diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).

AB - The widespread use of High-Throughput Sequencing (HTS) for detection of plant virusesand sequencing of plant virus genomes has led to the generation of large amounts of dataand of bioinformatics challenges to process them. Many bioinformatics pipelines for virusdetection are available, making the choice of a suitable one difficult. A robustbenchmarking is needed for the unbiased comparison of the pipelines, but there iscurrently a lack of reference datasets that could be used for this purpose. We present 7semi-artificial datasets composed of real RNA-seq datasets from virus-infected plantsspiked with artificial virus reads. Each dataset addresses challenges that could prevent virusdetection. We also present 3 real datasets showing a challenging virus composition as wellas 8 completely artificial datasets to test haplotype reconstruction software. With thesedatasets that address several diagnostic challenges, we hope to encourage virologists,diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).

U2 - 10.24072/pci.genomics.100007

DO - 10.24072/pci.genomics.100007

M3 - A2: International peer reviewed article (not A1-type)

SN - 2804-3871

VL - 1

SP - 1

EP - 15

JO - Peer community Journal

JF - Peer community Journal

IS - 1

M1 - e53

ER -

Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

Uittreksel

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Projecten

PHBN : Plantengezondheid bioinformatica netwerk

Dit citeren