TY - JOUR
T1 - GeneAnalytics
T2 - An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data
AU - Fuchs, Shani Ben Ari
AU - Lieder, Iris
AU - Stelzer, Gil
AU - Mazor, Yaron
AU - Buzhor, Ella
AU - Kaplan, Sergey
AU - Bogoch, Yoel
AU - Plaschkes, Inbar
AU - Shitrit, Alina
AU - Rappaport, Noa
AU - Kohn, Asher
AU - Edgar, Ron
AU - Shenhav, Liraz
AU - Safran, Marilyn
AU - Lancet, Doron
AU - Guan-Golan, Yaron
AU - Warshawsky, David
AU - Shtrichman, Ronit
N1 - Publisher Copyright:
© Shani Ben-Ari Fuchs, et al., 2016. Published by Mary Ann Liebert, Inc. 2016.
PY - 2016/3
Y1 - 2016/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84962292552&partnerID=8YFLogxK
U2 - 10.1089/omi.2015.0168
DO - 10.1089/omi.2015.0168
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C2 - 26983021
AN - SCOPUS:84962292552
SN - 1536-2310
VL - 20
SP - 139
EP - 151
JO - OMICS A Journal of Integrative Biology
JF - OMICS A Journal of Integrative Biology
IS - 3
ER -
