Small RNA sequencing and VirusDetect a bioinformatics pipeline that assist in the detection of known and new and novel viruses is being used beyond RTB

For communication use

Efficient and accurate detection of viruses in plants and animals is essential to develop effective disease management strategies. Conventional detection methods are not very efficient in detecting new viruses. When a plant is attacked by a virus it produces a unique set of molecules called small RNA (Ribonucleic acid). A new approach, virus discovery by high throughput sequencing and reconstructing all of the small RNA (sRNA) in a plant has been widely adopted for virus detection. In 2017 a team at Cornell University and CIP developed VirusDetect, an automated bioinformatics pipeline.  This can efficiently analyze the information about sRNA in a sample to identify existing and new viruses, by comparing the sample results with an existing database about all known viruses. VirusDetect provides tabulated results of identified viruses with information about the DNA sequences it identifies and the likelihood that these correspond to a known or new virus. A new Windows version of VirusDetect has been developed including various sequence quality checking and control options and has been downloaded 22 times from the CIP hosted website (2).

VirusDetect can be applied to detect viruses in various crops, fungi or animals.  It has been taken up by researchers in a number of countries including in Sub-Saharan Africa and is being evaluated by CGIAR germplasm health units as a standard tool to certify virus free status of RTB crops. As Tanzania is the largest producer of common bean in SSA, and viruses are major constraints, researchers there turned to VirusDetect. The management of viral diseases requires information on types, distribution, incidence, and genetic variation of the causal viruses, which was very limited. Consequently, a countrywide, comprehensive survey was conducted in Tanzania using VirusDetect and 15 viruses belonging to 11 genera were identified (3).  Furthermore, in a collaboration between researchers from Finland, Nicaragua and Tanzania, VirusDetect was used to identify seed-borne viruses in common bean varieties grown in Nicaragua and Tanzania (4). 

In a study comparing bio-informatic approaches by 21 plant virology laboratories, to detect 12 plant viruses from three different infected plants, including from a potato sample provided by CIP, VirusDetect was amongst the best performing pipelines and easiest to use (5).

Elaboration of Outcome

Efficient and accurate detection of viruses in plants and animals is essential for the development of effective strategies for disease management. Conventional detection methods require prior knowledge or sequence information of the potential pathogens and are not very efficient in detecting novel viruses or virus variants. A new approach, virus discovery by high throughput sequencing and assembly of total small RNAs (1), has proven to be highly efficient in virus detection and been widely adopted. This was enhanced by use of VirusDetect, an automated bioinformatics pipeline operating under Linux developed by Cornell University and CIP in 2017 that can efficiely analyze large-scale small RNA (sRNA) datasets for both known and novel virus identification. VirusDetect performs both reference-guided assemblies through aligning sRNA sequences to a curated virus reference database and de novo assemblies of sRNA sequences with automated parameter optimization and the option of host sRNA subtraction (1). VirusDetect provides tabulated results of identified viruses with percentages of genome coverage together with total and normalized sequencing depths as standard outputs to aid users in evaluating the significance of identified contigs. A new Windows version of VirusDetect has been developed including various sequence quality checking and control options and has been downloaded 22 times from the CIP hosted website (2).

VirusDetect can be applied to detect viruses in various crops, fungi or animals.  It has been taken up by researchers in a number of countries including in Sub-Saharan Africa and is being evaluated by CGIAR germplasm health units as a standard tool to certify virus free status of RTB crops. As Tanzania is the largest producer of common bean in SSA, and viruses are major constraints, researchers there turned to VirusDetect. The management of viral diseases requires information on types, distribution, incidence, and genetic variation of the causal viruses, which was very limited. Consequently, a countrywide, comprehensive survey was conducted in Tanzania using VirusDetect and 15 viruses belonging to 11 genera were identified (3).  Furthermore, in a collaboration between researchers from Finland, Nicaragua and Tanzania, VirusDetect was used to identify seed-borne viruses in common bean varieties grown in Nicaragua and Tanzania (4). 

In a study comparing bio-informatic approaches by 21 plant virology laboratories, to detect 12 plant viruses from three different infected plants, including from a potato sample provided by CIP, VirusDetect was amongst the best performing pipelines and easiest to use (5).