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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.

ABC transporters are often found to be inherently expressed in a wide variety of stem cells, where they provide improved protection from toxins. We found a subpopulation of human melanoma cells expressing multidrug-resistance gene product 1 (MDR1). This fraction co-expresses the ABC transporters, ABCB5 and ABCC2 in addition to the stem cell markers, nanog and human telomerase reverse transcriptase (hTERT). The clonogenicity and self-renewal capacity of MDR1(+) melanoma cells were investigated in single cell settings using the limiting dilution assay. We found that the MDR1(+) cells, isolated by FACS sorting, demonstrated a higher self-renewal capacity than the MDR1(-) fraction, a key stem cell feature. Moreover, MDR1(+) cells had higher ability to form spheres in low attachment conditions, a hallmark of cancer. In conclusion, these novel findings imply that the MDR1(+) cells represent melanoma stem cells and thus should be considered as a unique cellular target for future anti-melanoma therapies.