Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect

Abstract Background A key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient’s disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses. Results VarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness—aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status—high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards’ diverse gene-to-gene relationships, including SuperPaths—integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of “disease SuperPaths”, generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease–disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources. Conclusions MalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation

Additional file 1: Figure S1. of VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Merging direct and indirect modes. For each tested interlacing factor (x-axis) the red line represents the count of phenotype searches (out of ~300 relevant searches) for which the highest ranking result was direct (left y-axis). The blue line represents the average “interlacing score” (right y-axis) of all relevant phenotype searches. (PDF 52 kb

Additional file 7:Â Table S6. of VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Vcf file produced from the exome sequencing of the Arthrogryposis Multiplex Congenita clinical case described in the results section. (GZ 1665 kb

Additional file 2: Table S1. of VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Gene lists and corresponding phenotypes for Phenolyzer. (XLSX 32.0 kb

Additional file 6:Â Table S5. of VarElect: the phenotype-based variation prioritizer of the GeneCards Suite

Three vcf files produced from the exome sequencing of a trio belonging to the Apnea, Leigh-Like clinical case described in the results section. (GZ 13334 kb