Yazar "Brostaux, Yves" seçeneğine göre listele

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    Calculating and Reporting Managed Honey Bee Colony Losses
    (Crc Press-Taylor & Francis Group, 2012) vanEngelsdorp, Dennis; Brodschneider, Robert; Brostaux, Yves; van der Zee, Romee; Pisa, Lennard; Underwood, Robyn; Lengerich, Eugene J.
    Quantifying colony losses is a two-part process. First, colony loss data needs to be collected by surveying beekeepers and then it needs to be calculated and reported in a standardized way. We propose using two different ways to calculate and communicate colony losses. The first we term the total colony losses, sometimes referred to as cumulative loss rate in other systems, which aggregates all losses suffered by all beekeepers surveyed. While the total loss calculation is straightforward, calculating a 95% CI for this metric is complicated by the need to account for the varying sizes of responding beekeeper operations and the nested nature of colony losses within those operations. The second reporting method, termed average loss, is the mean loss suffered by each responding beekeeper. The utility of these two reporting mechanisms differs, in that both are potentially biased by the demographics of the apicultural industry; total loss figures are more heavily influenced by the losses experienced by the few large operations, while average losses are more representative of the many small operations. Additionally, it is important to note that the results from this survey are representative of the responding population alone, and cannot be considered representative of the industry unless some means of identifying and adjusting for varying response is performed.
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    Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies
    (Peerj Inc, 2023) Rollin, Johan; Bester, Rachelle; Brostaux, Yves; Caglayan, Kadriye; De Jonghe, Kris; Eichmeier, Ales; Foucart, Yoika
    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.