Toppgene Suite
Toppgene Suite - Related Videos
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FAQs
ToppGene compares each candidate gene with a user-defined group of genes already associated with a disease or phenotype. It evaluates similarity across several evidence types, then uses the combined similarity information to produce a prioritized ranking. This approach helps distinguish candidates that resemble the established training set from those with weaker overall support.
The platform integrates functional annotations, biological pathways, protein interactions, gene-expression information, literature associations, and phenotypic relationships. These sources capture different dimensions of gene similarity, including shared biological roles, network connections, observed expression patterns, and documented relevance. Combining them provides a broader basis for ranking than relying on a single annotation or evidence category.
The training set defines the disease- or phenotype-related reference profile against which candidate genes are compared. Because rankings are based on similarity to that set, its biological relevance directly shapes the interpretation of the results. A carefully selected training set can make the prioritization more informative for the specific genetic question being investigated.
Gene prioritization compares candidates with a reference training set and ranks them according to integrated similarity evidence. Functional enrichment instead examines whether a submitted gene list is overrepresented in particular biological functions or pathways. Visualization tools can then help represent those enriched processes and molecular networks, providing context for interpreting the gene set as a whole.
A typical workflow begins by supplying a candidate gene list and defining a training set linked to the disease or phenotype of interest. ToppGene then evaluates cross-source similarities and returns ranked candidates. Related suite tools can be applied afterward to examine functional enrichment and visualize pathways or molecular networks represented by the analyzed genes.
In disease-gene discovery, ranked candidates can highlight genes whose functional, pathway, interaction, expression, literature, or phenotype profiles resemble known disease-associated genes. For variant interpretation, those rankings add gene-level biological context to genomic findings. The resulting patterns can also support development of testable hypotheses for follow-up genetic or functional research.