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
SNP-based gene-set enrichment analysis from single nucleotide polymorphisms, or GSEA-SNP, is a tool to identify candidate genes based on enrichment analysis of sets of genes rather than single SNP associations. The objective of this study was to identify modest-effect genes associated with Mycobacterium avium subsp. paratuberculosis (Map) tissue infection or fecal shedding using GSEA-SNP applied to KEGG pathways or Gene Ontology (GO) gene sets. The Illumina Bovine SNP50 BeadChip was used to genotype 209 Holstein cows for the GSEA-SNP analyses. For each of 13,744 annotated genes genome-wide located within 50 kb of a Bovine SNP50 SNP, the single SNP with the highest Cochran-Armitage Max statistic was used as a proxy statistic for that gene's strength of affiliation with Map. Gene-set enrichment was tested using a weighted Kolmogorov-Smirnov-like running sum statistic with data permutation to adjust for multiple testing. For tissue infection and fecal shedding, no gene sets in KEGG pathways or in GO sets for molecular function or cellular component were enriched for signal. The GO biological process gene set for positive regulation of cell motion (GO:0051272, q = 0.039, 5/11 genes contributing to the core enrichment) was enriched for Map tissue infection, while no GO biological process gene sets were enriched for fecal shedding. GSEA-SNP complements traditional SNP association approaches to identify genes of modest effects as well as genes with larger effects as demonstrated by the identification of one locus that we previously found to be associated with Map tissue infection using a SNP-by-SNP genome-wide association study.
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