This program draws from a large background network consisting of
several protein interaction datasets. It will take a list of genes
you provide as seed genes and identify all interacting genes that fall
on paths through the background network between them.
Instructions
1. Specify your gene list. This is a list of primary human entrez gene symbols which will be used to find interacting nodes. Entering other accession numbers or full names will search NCBI's gene database and provide suggestions for the appropriate gene symbol.
2. Select Max Path Length. This is the number of steps to search to find intermediate nodes on paths connecting the seed nodes. A value of 1 will only return connections between seed nodes.
3. Choose a cutoff for significance for labeling graph nodes. A z-score is calculated for each intermediate node based on the number of links to the seed nodes and the number of total links it has in the background network. Nodes above this specified threshhold will be labeled in a different color.
4. Choose colors for graph nodes based on whether or not it is in the seed list or above the significance threshhold.
5. Select databases to use for generating the background network. Information about each database is available by clicking on their names. Additional user supplied datasets may be supplied as well.
6. Select filtering options for the background dataset. Remove low quality interactions from the background dataset by removing interactions from high throughput experiments or interactions without enough evidence.
If you use Genes2Networks, please cite: Berger SI, Posner JM, Ma'ayan A. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics. 2007 Oct 4;8:372.
All questions related to Genes2Network can be directed to avi.maayan@mssm.edu at the Mount Sinai School of Medicine. For technical questions, please contact seth.berger@mssm.edu. The use of this software and data is intended for academic users only, commercial users should contact technology@mssm.edu.
