GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data
| Autor: | Alina Shitrit, Yaron Mazor, Sergey Kaplan, Doron Lancet, Noa Rappaport, Iris Lieder, Asher Kohn, Ronit Shtrichman, Shani Ben-Ari Fuchs, Ron Edgar, Ella Buzhor, Liraz Shenhav, Gil Stelzer, Marilyn Safran, Yoel Bogoch, Yaron Guan-Golan, Inbar Plaschkes, David Warshawsky |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Databases Factual Computer science Big data Gene regulatory network Biochemistry GeneCards 03 medical and health sciences 0302 clinical medicine Databases Genetic Genetics Data Mining Humans Gene Regulatory Networks Molecular Biology Diseases database internet.website internet business.industry Microarray analysis techniques Genome Human Computational Biology High-Throughput Nucleotide Sequencing Original Articles Precision medicine Microarray Analysis Data science 3. Good health 030104 developmental biology Nutrigenomics Molecular Medicine User interface business 030217 neurology & neurosurgery Algorithms Metabolic Networks and Pathways Software Biotechnology |
| Zdroj: | OMICS : a Journal of Integrative Biology |
| ISSN: | 1557-8100 |
| Popis: | Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from “data-to-knowledge-to-innovation,” a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ (geneanalytics.genecards.org), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®—the human gene database; the MalaCards—the human diseases database; and the PathCards—the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®—the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene–tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell “cards” in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon. |
| Databáze: | OpenAIRE |
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