Welcome to HUBGENE (Human Brain Genome Network) viewer

This resource comprises a robust co-expression network data of the human brain that we have organized and analyzed based on the GTEX consortium RNAseq data (GTEX). Here you can explore the dataset by regional or brain-wide modules using a graphic interface, or by gene name, which will illustrate the relationship between that gene and the modules in which it is a member, along with statistical summary data. In addition, you can explore using the regional contrast test which genes are differentially expressed across conserved modules representing different cell types or regions.

* In the text, we interchangeably use the term "NCBL" and "ALL" to represent brain networks without cerebellum.
Please click on a region to explore!


This browser is a companion to the Brain-wide human co-expression resource published in Nature Neuroscience and originally posted on BioRxiv. The abstract is below:

Gene networks have yielded numerous neurobiological insights, yet an integrated view across brain regions is lacking. We leverage RNA-sequencing in 864 samples representing 12 brain regions to robustly identify 12 brain-wide, 50 cross-regional and 114 region-specific co-expression modules. Nearly 40% of genes fall into brain-wide modules, while 25% comprise region-specific modules reflecting regional biology, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. Schizophrenia and autism genetic risk is enriched in brain-wide and multi-regional modules, indicative of broad impact; these modules implicate neuronal proliferation and activity-dependent processes, including endocytosis and splicing in disease pathophysiology. We find that cell-type-specific lncRNA and gene isoforms contribute substantially to regional synaptic diversity and that constrained, mutation intolerant genes are primarily enriched in neurons. We leverage these data using an omnigenic-inspired network framework to characterize how co-expression and gene regulatory networks reflect neuropsychiatric disease risk, supporting polygenic models.