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
A Novel Dynamic Impact Approach (DIA) for Functional Analysis of Time-Course Omics Studies: Validation Using the Bovine Mammary Transcriptome
The overrepresented approach (ORA) is the most widely-accepted method for functional analysis of microarray datasets. The ORA is computationally-efficient and robust; however, it suffers from the inability of comparing results from multiple gene lists particularly with time-course experiments or those involving multiple treatments. To overcome such limitation a novel method termed Dynamic Impact Approach (DIA) is proposed. The DIA provides an estimate of the biological impact of the experimental conditions and the direction of the impact. The impact is obtained by combining the proportion of differentially expressed genes (DEG) with the log2 mean fold change and mean -log P-value of genes associated with the biological term. The direction of the impact is calculated as the difference of the impact of up-regulated DEG and down-regulated DEG associated with the biological term. The DIA was validated using microarray data from a time-course experiment of bovine mammary gland across the lactation cycle. Several annotation databases were analyzed with DIA and compared to the same analysis performed by the ORA. The DIA highlighted that during lactation both BTA6 and BTA14 were the most impacted chromosomes; among Uniprot tissues those related with lactating mammary gland were the most positively-impacted; within KEGG pathways 'Galactose metabolism' and several metabolism categories related to lipid synthesis were among the most impacted and induced; within Gene Ontology "lactose biosynthesis'' among Biological processes and "Lactose synthase activity'' and "Stearoyl-CoA 9-desaturase activity'' among Molecular processes were the most impacted and induced. With the exception of the terms 'Milk', 'Milk protein' and 'Mammary gland' among Uniprot tissues and SP_PIR_Keyword, the use of ORA failed to capture as significantly-enriched (i.e., biologically relevant) any term known to be associated with lactating mammary gland. Results indicate the DIA is a biologically-sound approach for analysis of time-course experiments. This tool represents an alternative to ORA for functional analysis.
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