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
Single nucleotide variant detection in Jaffrabadi buffalo (Bubalus bubalis) using high-throughput targeted sequencing
The water buffalo is among the most important livestock species of southern Asia, contributing greatly to the ecosystem and rural livelihood of the region. The identification of large-scale single nucleotide polymorphisms in this species would greatly facilitate our understanding of the genetic basis of economically important traits such as milk production, fertility traits and general health traits. The present study investigated the cost-effective method of exome capture and single nucleotide variant (SNV) identification from genomic DNA of Jaffrabadi buffalo using biotin-labelled cDNA as probes. Sequencing of enriched fragments generated 608 Mb of data, which was mapped to a Bos taurus genome assembly followed by variant calling and annotation. Furthermore, 393 coding SNVs were identified, leading to 143 non-synonymous substitutions (nsSNVs) in 75 genes. Of the 75 nsSNV-containing genes, four matched the genes that have previously been reported to be potentially associated with economically important traits such as milk production and meat production. Furthermore, functional annotation using gene ontology (GO) enrichment identified categories such as glutamate receptor activity (GO: 0008066) enriched in the fertility trait samples. These results provide a framework for the application of cost-effective methods of target capture in SNV detection from non-model organisms such as the water buffalo.
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