Next‐generation sequencing and metagenomic analysis: a universal diagnostic tool in plant virology

Adams, Ian; Glover, R.; Monger, W.; Mumford, R. A.; Jackevičienė, E.; Navalinskienė, M.; Samuitienė, M.; Boonham, Neil

doi:10.1111/j.1364-3703.2009.00545.x

Cited by 318 publications

(217 citation statements)

References 26 publications

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“…Therefore, next-generation deep sequencing approaches and bioinformatics analysis can be used for de novo assembly of virus and viroid genomes, to perform reliable characterization and diagnostics of known and unknown viruses and viroids [112,154,155]. In the wake of NGS technologies, powerful and high-throughput novel approaches, such as metagenomics, have been developed and widely used to analyze nucleotide sequence of microbial populations in plant samples (see section 2.8) [8,105,156]. In particular, deep sequencing of small RNA families such as short interfering RNAs (siRNAs) can be used to identify and reconstruct any DNA or RNA virus genome and its microvariants with the help of bioinformatics tools [155,157].…”

Section: Disease Diagnostics and Monitoringmentioning

confidence: 99%

Perspectives on the Application of Next-generation Sequencing to the Improvement of Africa’s Staple Food Crops

Gedil¹,

Ferguson²,

Girma³

et al. 2016

Next Generation Sequencing - Advances, Applications and Challenges

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The persistent challenge of insufficient food, unbalanced nutrition, and deteriorating natural resources in the most vulnerable nations, characterized by fast population growth, calls for utilization of innovative technologies to curb constraints of crop production. Enhancing genetic gain by using a multipronged approach that combines conventional and genomic technologies for the development of stress-tolerant varieties with high yield and nutritional quality is necessary. The advent of nextgeneration sequencing (NGS) technologies holds the potential to dramatically impact the crop improvement process. NGS enables whole-genome sequencing (WGS) and re-sequencing, transcriptome sequencing, metagenomics, as well as high-throughput genotyping, which can be applied for genome selection (GS). It can also be applied to diversity analysis, genetic and epigenetic characterization of germplasm and pathogen detection, identification, and elimination. High-throughput phenotyping, integrated data management, and decision support tools form the necessary supporting environment for effective utilization of genome sequence information. It is important that these opportunities for mainstreaming innovative breeding strategies, enabled by cutting-edge "Omics" technologies, are seized in Africa; however, several constraints must be addressed before the benefit of NGS can be fully realized. African breeding programs must have access to high-throughput genotyping facilities, capacity in the application of genome selection and marker-assisted breeding must be built and supported by capacity in genomic analysis and bioinformatics. This chapter demonstrates how interventions with NGS-enabled innovative strategies can be applied to increase genetic gain with insights from the Consortium of International

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Section: Disease Diagnostics and Monitoringmentioning

confidence: 99%

Perspectives on the Application of Next-generation Sequencing to the Improvement of Africa’s Staple Food Crops

Gedil¹,

Ferguson²,

Girma³

et al. 2016

Next Generation Sequencing - Advances, Applications and Challenges

View full text Add to dashboard Cite

show abstract

“…To avoid host RNAs, ds-RNA is used instead of total RNA resulting in an enrichment of viral sequences. This metagenomic approach has been demonstrated for the first time for animal viruses (Cox-Foster et al 2007) and more recently to plant viruses in a model plant (Adams et al 2009) or in grapevine (Al Rwahnih et al 2009;Coetzee et al 2009;Engel et al 2009). Interestingly in all the cases new viral agents were found.…”

Section: Non-targeted Diagnosis: a New Paradigmmentioning

confidence: 99%

New Developments in Pathogen Detection and Identification

Nolasco¹

2012

Acta Hortic.

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A b s tractBy the turn of the century most of the methods for amplifying the pathogen detection signal had already been developed. However only a few were successfully incorporated into routine diagnosis schemes. At present, real time PCR became the predominant choice with signal transduction based on fluorescence generated by intercalating dyes or fluorescent resonant energy transfer. Real-time PCR is no more exclusively restricted to niches for which no alternative was feasible; its use is widening and is substituting or complementing other techniques like ELISA. On the low technology side and judging from the success in medical fields, Loop Mediated Isothermal Amplification (LAMP) appears as an interesting alternative. Convergence into a single platform led to the concept of crop-oriented diagnosis, e.g, a unique device or system that could be used in the detection of the relevant pathogens of a crop. However PCR by itself is not enough robust to support the degree of multiplexing needed and fluorescence detection systems are limited to a reduced number of dyes that can be used simultaneously. Initial attempts to develop alternative systems relied in the use of microarrays but suffer from limited sensitivity. Low Density Arrays, in which individual PCR reactions are spatially separated and done in parallel appear an interesting option, already available for grapevine. All these assays only provide an answer regarding the presence of certain pathogens for which there is an a priori suspicion. Recently introduced high massively parallel sequencing coupled to metagenomic analysis appears to be a major breakthrough in diagnosis, enabling the non-targeted diagnosis of bacteria, virus, fungi and novel agents in a single assay. These have been implemented among others for citrus and grapevine.

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“…Similar experiments were also carried out in plant systems. Adams et al (2009) analyzed total RNA from tomatoes infected with the Pepino mosaic virus (PepMV) and Gomphrena globosa, a frequently-used tester plant, infected with an unknown pathogen 1 . They found that, of a 16.6 mega-base sequence, 20% was from PepMV whereas 70% was of host origin.…”

Section: Sequencing-based Virus Hunting and Virus Detectionmentioning

confidence: 99%