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

Abstract

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.

Long Noncoding RNA MALAT1 Promotes Hepatocellular Carcinoma Development by SRSF1 Upregulation and mTOR Activation

“…A list of 880 statistically significant differentially expressed genes was subjected to pathway enrichment analysis using QIAGEN’s Ingenuity Pathway Analysis (IPA, QIAGEN Redwood City, www.qiagen.com/ingenuity), GeneAnalytics (32) and EnrichR (33) as well as functions/diseases enrichment analysis using IPAA list of 880 genes whose expression shown the most intensive change was subjected to enrichment analysis using IPA, GeneAnalytics (32) and EnrichR (33). …”

miR-200b downregulates Kruppel Like Factor 2 (KLF2) during acute hypoxia in human endothelial cells

“…GeneAnalytics™ (geneanalytics.genecards.org), is a comprehensive 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 (Ben-Ari Fuchs et al, 2016). The webserver was used to place NGS results into physiological context using built in biological pathway algorithm.…”

MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search

“…(iii) Related diseases, where B is defined to be a related disease for disease A if the disease name A is mentioned verbatim, and in any section, in the MalaCard for disease B. Additional relatedness links are discovered by seeking significant gene sharing between the two diseases as seen in GeneAnaltytics (7). A composite relatedness score is generated and defines the top 20 related diseases portrayed in each MalaCard's weighted network image (see (8) for details).…”

“…This is done in the case of disease-related drugs obtained from FDA.gov. (v) Set enrichment analysis via GeneAnalytics (7), by probing the overlap between genes associated with an entity in GeneCards (e.g. pathways, GO terms and mouse phenotypes) and disease-related genes.…”

“…As in the case of related diseases, we show here GO terms scored by relevance, based on GeneAnalytics gene-set analysis, which calculates significant enrichment of GO terms in the gene set associated with the disease (7,26). …”