Title |
Confero: an integrated contrast data and gene set platform for computational analysis and biological interpretation of omics data
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Published in |
BMC Genomics, July 2013
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DOI | 10.1186/1471-2164-14-514 |
Pubmed ID | |
Authors |
Leandro Hermida, Carine Poussin, Michael B Stadler, Sylvain Gubian, Alain Sewer, Dimos Gaidatzis, Hans-Rudolf Hotz, Florian Martin, Vincenzo Belcastro, Stéphane Cano, Manuel C Peitsch, Julia Hoeng |
Abstract |
High-throughput omics technologies such as microarrays and next-generation sequencing (NGS) have become indispensable tools in biological research. Computational analysis and biological interpretation of omics data can pose significant challenges due to a number of factors, in particular the systems integration required to fully exploit and compare data from different studies and/or technology platforms. In transcriptomics, the identification of differentially expressed genes when studying effect(s) or contrast(s) of interest constitutes the starting point for further downstream computational analysis (e.g. gene over-representation/enrichment analysis, reverse engineering) leading to mechanistic insights. Therefore, it is important to systematically store the full list of genes with their associated statistical analysis results (differential expression, t-statistics, p-value) corresponding to one or more effect(s) or contrast(s) of interest (shortly termed as " contrast data") in a comparable manner and extract gene sets in order to efficiently support downstream analyses and further leverage data on a long-term basis. Filling this gap would open new research perspectives for biologists to discover disease-related biomarkers and to support the understanding of molecular mechanisms underlying specific biological perturbation effects (e.g. disease, genetic, environmental, etc.). |
X Demographics
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 50% |
United States | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Germany | 1 | 1% |
Malaysia | 1 | 1% |
Uruguay | 1 | 1% |
France | 1 | 1% |
Estonia | 1 | 1% |
Brazil | 1 | 1% |
Unknown | 68 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 28% |
Student > Ph. D. Student | 19 | 25% |
Professor | 8 | 11% |
Professor > Associate Professor | 6 | 8% |
Student > Master | 6 | 8% |
Other | 10 | 13% |
Unknown | 6 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 30% |
Biochemistry, Genetics and Molecular Biology | 16 | 21% |
Medicine and Dentistry | 7 | 9% |
Computer Science | 7 | 9% |
Engineering | 4 | 5% |
Other | 6 | 8% |
Unknown | 13 | 17% |