Powerful and easy to use tool

GeneAnalytics was generated by biologists, for biologists.

Our team of biology experts is tuned into the types of results most biologists are seeking and designed this tool to require no bioinformatics background or expertise.

Simple input of the gene symbol(s) activates a rapid identification and analysis process. GeneAnalytics provides the user with clearly organized results, links to more information, powerful filters, gene annotations and download capacities.


 Multi-source integration

GeneAnalytics is powered by more than 100 data sources modeled and integrated into the LifeMap Integrated Biomedical Knowledgebase.

The gene expression analysis leverages the extensive tissue and cell gene expression data available in LifeMap Discovery® for normal tissues and cells, and in MalaCards for diseased tissues.

Function-based analysis leverages the information integrated in GeneCards®Malacards and PathCards which provide gene associations with diseases, pathways, compounds and Gene Ontology (GO) terms.


Categorized results output

To better understand the context of your gene set, the matching results in GeneAnalytics are divided into the following categories:

  • Tissues and cells: matches your gene set to normal tissues and cells based on gene expression profiles.
  • Diseases: matches your gene set to genes associated with diseases either by genetic associations (e.g., causative mutation, risk factor), by differential expression in diseased tissues and/or by associations derived from GeneCards (‘GeneCards-inferred’)
  • Pathways: matches your gene set to the gene contents of SuperPaths, which are clusters of pathways, from many pathways sources. Both the SuperPath and its constituent pathways are presented in the match results.
  • Gene Ontologies (GO): matches your gene set to enriched GO terms, in attempt to define gene set functionality.
  • Compounds: matches your gene set to the aggregated gene lists of compounds unified from several data sources.

Each matched entity is displayed with its matching score, a link to the source of evidence and to more information about the entity, as well as with a list of matched genes. 


Access to supporting information

The robust GeneAnalytics interface links each matched entity to its specific card in the LifeMap Integrated Biomedical Knowledgebase, or to external data sources, providing extensive and comprehensive information for tissues, cells, diseases and pathways. The available interlinks to our knowledgebase suite save you valuable time. 


Proprietary expression data

Expression-based analysis results in GeneAnalytics is based on data which were manually collected, filtered, modeled, annotated and integrated into LifeMap Discovery.

The gene expression data are obtained from the scientific literature, high throughput experiments, in situ hybridization and immunostaining large scale datasets. High throughput data undergoes statistical analysis and filtration to ensure inclusion of reliable and significant genes only.

The matched gene expression data were collected for normal, unaffected developing and adult cells, anatomical compartments, organs and tissues in vivo, as well as for in vitro studies of stem, progenitor and primary cells and their differentiated derivatives. These data can be viewed in the Tissues & Cells section of GeneAnalytics.

Gene expression data  for diseased tissues and cells are separated from the normal expression data and are displayed in the disease section of GeneAnalytics. These data include genes that were found to be significantly up- or down regulated in the diseased tissues in comparison to identical tissues obtained from unaffected subjects. 


Proprietary and unique matching algorithm

The Tissues & Cells matching algorithm considers gene selectivity, specificity, enrichment and abundance information for each gene at each specific tissue or cell. The Diseases matching algorithm considers gene annotations, such as; gene-disease associations, and data sources. 

The function-based, Pathways, GO terms and Compounds analysis algorithm is based on the binomial distribution.

Start analyzing your gene sets