The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Transcriptome Analysis of Two Vicia sativa Subspecies: Mining Molecular Markers to Enhance Genomic Resources for Vetch Improvement
The vetch (Vicia sativa) is one of the most important annual forage legumes globally due to its multiple uses and high nutritional content. Despite these agronomical benefits, many drawbacks, including cyano-alanine toxin, has reduced the agronomic value of vetch varieties. Here, we used 454 technology to sequence the two V. sativa subspecies (ssp. sativa and ssp. nigra) to enrich functional information and genetic marker resources for the vetch research community. A total of 86,532 and 47,103 reads produced 35,202 and 18,808 unigenes with average lengths of 735 and 601 bp for V. sativa sativa and V. sativa nigra, respectively. Gene Ontology annotations and the cluster of orthologous gene classes were used to annotate the function of the Vicia transcriptomes. The Vicia transcriptome sequences were then mined for simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers. About 13% and 3% of the Vicia unigenes contained the putative SSR and SNP sequences, respectively. Among those SSRs, 100 were chosen for the validation and the polymorphism test using the Vicia germplasm set. Thus, our approach takes advantage of the utility of transcriptomic data to expedite a vetch breeding program.
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