GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data

Shani Ben Ari Fuchs, Iris Lieder, Gil Stelzer*, Yaron Mazor, Ella Buzhor, Sergey Kaplan, Yoel Bogoch, Inbar Plaschkes, Alina Shitrit, Noa Rappaport, Asher Kohn, Ron Edgar, Liraz Shenhav, Marilyn Safran, Doron Lancet, Yaron Guan-Golan, David Warshawsky, Ronit Shtrichman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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™ (, 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.

Original languageEnglish
Pages (from-to)139-151
Number of pages13
JournalOMICS A Journal of Integrative Biology
Issue number3
StatePublished - Mar 2016
Externally publishedYes


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