Description: To further facilitate the general needs of online real-time co-expression analysis, we develop biolearns, a python-based co-expression network analysis web tool.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It helps to discover latent gene/molecular interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus help to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream Gene Ontology (GO) enrichment analysis, as well as modules that may share common members. To address this need, we have developed a GCN mining tool package TSUNAMI (Tools SUite for Network Analysis and MIning) which incorporates our state-of-the-art lmQCM algorithm to mine GCN modules in public and user-input data (microarray, RNA-seq, or any other numerical omics data), then performs downstream GO and enrichment analysis based on the modules identified. Since then, TSUNAMI has been used with more than 5,000 sessions from 40 different countries. To further facilitate the general needs of online real-time co-expression analysis, we develop biolearns, a python-based co-expression network analysis web tool. The biolearns has several features and advantages: (i) A faster web server than TSUNAMI 1.0 when performing co-expression analysis; (ii) allowing multiple users to run their program simultaneously; (iii) integrating differential analysis and various downstream analyses; (iv) jobs can now be retrieved with credentials and can be run in background after user closing the session.