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
Background Capra hircus is an important economic livestock animal, and therefore, it is necessary to discover transcriptome information about their reproductive performance. In this study, we performed de novo transcriptome sequencing to produce the first transcriptome dataset for the goat ovary using high-throughput sequencing technologies. The result will contribute to research on goat reproductive performance. Method and Results RNA-seq analysis generated more than 38.8 million clean paired end (PE) reads, which were assembled into 80,069 unigenes (mean size = 619 bp). Based on sequence similarity searches, 64,824 (60.6%) genes were identified, among which 29,444 and 11,271 unigenes were assigned to Gene Ontology (GO) categories and Clusters of Orthologous Groups (COG), respectively. Searches in the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG) showed that 27,766 (63.4%) unigenes were mapped to 258 KEGG pathways. Furthermore, we investigated the transcriptome differences of goat ovaries at two different ages using a tag-based digital gene expression system. We obtained a sequencing depth of over 5.6 million and 5.8 million tags for the two ages and identified a large number of genes associated with reproductive hormones, ovulatory cycle and follicle. Moreover, many antisense transcripts and novel transcripts were found; clusters with similar differential expression patterns, enriched GO terms and metabolic pathways were revealed for the first time with regard to the differentially expressed genes. Conclusions The transcriptome provides invaluable new data for a functional genomic resource and future biological research in Capra hircus, and it is essential for the in-depth study of candidate genes in breeding programs.
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