Expression-and Function-based analysis

Gene set – disease relationship analysis in GeneAnalytics leverages the information available in MalaCards, the human disease database. MalaCards unifies and integrates data from more than 60 sources, providing a comprehensive coverage of known diseases, including disease categorizations and disease-associated gene aggregation, along with gene-disease association rankings and annotations leveraging GeneCards.

Expression & function based, disease association analysis by GeneAnalytics enables you to:

    • Interpret next generation sequencing results
    • Identify known genetic variations in your gene set, and their associated diseases
    • Identify genes that are differentially expressed in specific diseased tissues
    • Explore genes related to specific disease categories
    • Explore potential biomarkers and therapeutic targets


    Data Types and Sources

    Gene- disease associations in MalaCards are divided into the following categories:

    • Genetic associations to diseases are derived from several MalaCards data sources. Since each data source has its own annotation terminology, we categorized all possible associations as shown in the genetic association table in descending order of their score.
    • Differentially expressed (DE) genes are genes that were found to be significantly up- or down regulated in the disease tissues in comparison to identical tissues obtained from unaffected subjects. These differential gene expression profiles are derived from high throughput experiments, extracted from the Gene Expression omnibus (GEO) or from the literature and analyzed using LifeMap Discovery algorithms. Each gene is tagged with a differential expression score determined by the fold-change in expression of the gene in the diseased vs. the normal tissue.
    • “GeneCards-inferred” relation indicates that the disease name appears in the gene page in GeneCards. Of note, the ‘GeneCards-inferred’ relation does not necessarily imply causality between the gene and the disease and the nature of this relation may sometimes be unclear.

    Learn more about MalaCards  and its data sources.


    Results and score calculation

    The disease matching score is determined by the following parameters:

    1. The number of genes in the gene set that match a specific disease normalized by the total number of genes specifically associated with the disease.
    2. The quality and type of the gene-disease relations. Each gene in each disease has a score derived from its relations to the disease, as annotated in MalaCards sources.

    As described above, there are three types of relation:

    • Genetic associations
    • Differentially expressed (DE) genes
    • "GeneCards-inferred"


    • The quality of the match is assessed by the GeneAnalytics algorithm and is categorized as high, medium or low. The match quality level is indicated by the color of the score bar.
    • The distribution of the matching qualities across the results list is presented as an overview of the overall matching quality. 

    Function diseases


    The matched diseases can be filtered either by the gene-disease relation or by MalaCards categories (see below). The gene-disease relation filter filters results to present only diseases that have either known differentially expressed genes or genes with genetic association to the disease.

    Importantly, there are different types of genetic associations, which can be used to further filter the results, using the “genetic association type” filter.

     Read more about Genetic association types

    Disease categorization

    MalaCards categorizes diseases into, global (fetal, genetic, cancer and infectious) and anatomical (e.g., eye, ear, liver, blood) diseases. A disease can be associated with several anatomical and global categories and is categorized as follows:

    • Mapping MalaCards diseases to existing and widely used categorization systems available via ICD10. 
    • Using an algorithm facilitating category-specific keywords contained in disease names and annotations.
    • Application of heuristics to arrange diseases into categories; for example, the existence of causative variations or a genetic test will place a disease into the genetic category.

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