NetCrafter
Smart tool for creating & interpreting multi-functional networks

What is NetCrafter?

NetCrafter is an intuitive, user-friendly tool for automatically visualizing gene and function networks, and predicting representative functions for each networked cluster. Users can easily obtain visualized networks with interpretation by simply providing a list of genes or functions, without the need for additional relationship data. NetCrafter utilizes predefined gene-to-function relationships retrieved from Gene Ontology, Human Phenotype Ontology and Reactome DBs to generate the gene or function networks and their annotations.

Network analysis in NetCrafter

User-provided gene or function lists can be directly used to generate multi-functional networks, allowing for the analysis of representitive functions within each networked cluster.

NetCrafter enables network generation and functional interpretation for RNAs, Proteins and CRISPRs data.

How NetCrafter creates gene networks

1. 1. Input data: a list of genes
2. 2. Quantify the functional similarity for each gene pair
3. 3. Network visualization of connected genes defined by the functional similarity
4. 4. Assign representative function to each cluster based on the dominant function most contributing to edges within that cluster

• ▪ Nodes represent genes and edges represent the functional similarity (weight sum of shared functions)
• ▪ Node size indicates how many functions are associated with the corresponding gene and can be adjusted based on node data
• ▪ Edge length is inversely propoortional to the Tanimoto score
• ▪ The representative function of a network cluster is the function most contributing to the formation of edges

Calculating the functional similarity between genes

NetCrafter calculates functional similarity between genes using weighted Tanimoto coefficient (Jaccard Index) based on the overlap of shared functions.
Over 7,000 functions, each containing 3 to 300 genes, are retrieved from Gene Ontology Biological Process (GOBP) terms, Human Phenotype Ontology (HPO) terms and Reactome DB.

We measured functional similarity between genes using weighted Tanimoto index (Tw(G1,G2)), which calculated based on the -log(p-value)s of all overlapping ontology terms. The statistical significance is determined by Fisher’s exact test.

The more overlap between the input genes and genes annotated to a function, the higher the weight. As a result, two genes sharing functions will have a greater functional similarity.

How NetCrafter creates function networks

1. 1. Input data: a list of functions
2. 2. Quantify the functional similarity for each function pair
3. 3. Network visualization of connected functions defined by the gene similarity
4. 4. Nodes represent functions and edges represent the gene similarity (Standard Tanimoto score)
5. 5. Assigning a representative function to each network based on the size of node

• ▪ Node size indicates how many genes are associated with the corresponding function
• ▪ Edge length is inversely propoortional to the Tanimoto score
• ▪ The representative function of a network cluster is determined by the function with the largest number of associated genes
      (i.e., the function represented by the largest node)

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