I developed a bioinformatics tool to visualize human genome pathways. http://www.iprotein.info
When I was doing molecular biology research myself, I always had difficulty to get an overview of some genes in the vast human genome network. We often search it through Google image for pathways maps, or search from antibody companies, but these sources of pathways maps are hardly customized for our genes of interest, and was hardly complete for all the pathways the gene of interest leads to. From this point, I developed this website to provide a light-weight, quick tool for researchers to lookup human genome pathways based on their genes of interest.
It involves 2 modules:
Module 1. Single gene pathway analysis:
Given a gene, find the up-stream and/or downstream molecular pathways. You may choose the length of pathways to search for. I think it is self-explanatory from the first sight on this map. I input Ago2, and got its upstream and downstream genes. You may choose how many genes to search for and how many levels down into the molecular pathway. The search algorithm is based on academic reports: the gene interactions that are reported most will come first.
There is a button below that can generate a list of references from PubMed for all the genes listed.
Module 2: Pathway enrichment analysis:
Given a list of genes, find the pathways that connect them. If a gene cannot be connected, it will not be shown in the figure.
For example, if I ran some PCR in my cell model and found TP53, AKT1, and PIK3R3 were the most significant genes, I would want to know what other genes could be involved. In a quick search on this website I would find how these genes are related in the human genome network, and figure out ERBB3, RB1, PTK2, and PIK3CA may be worth for further investigation.
Same as Single gene pathway analysis, there will also be a button appearing below the graph, which will generate a list of references for you:
You must have noticed that some genes are reported repeatedly, so they can generate a long list.
The database of this website currently is based on BioGrid's open dataset for 1 Dec 2017. I will keep updating when I get new data sources. It currently records 16,760 genes and 369,824 papers.
Serena (Feng) Yu, Ph.D.