NODE

NODE brings together multi-omic datasets so scientists can upload, compare, and visualize findings across studies and species. The platform was assembled by CMTRF to accelerate research into Charcot-Marie-Tooth (CMT) disease, but the datasets it hosts can extend well beyond CMT, bringing together relevant data from all corners of peripheral nerve research.  This allows CMT researchers to learn from the latest findings in other related neuropathies, like amyotrophic lateral sclerosis (ALS) or diabetic peripheral neuropathy.

Why NODE

Every dataset on NODE gets vetted and confirmed to be scientifically valuable, so researchers spend their time analyzing data rather than evaluating whether it’s worth using. That vetting process is ongoing: CMTRF is actively funding the generation of new, vetted datasets specifically to fill gaps in the peripheral neuropathy research ecosystem, with priority given to data that connects pre-clinical model biology to human disease, one of the field’s most persistent translational challenges.

NODE is built for CMT research, but it is not limited to it. Any peripheral neuropathy researcher, regardless of subtype or indication, is welcome to use and contribute to the platform. We learn faster together.

We are hosting a webinar to introduce people to the platform on October 26, 2026, at 7 PM Eastern. Supporters will have the chance to learn why this tool is so valuable for researchers; scientists will get to see the tool in action and understand its capabilities. Register now.

What NODE Brings Together

NODE is built to include multi-omic data relevant to peripheral nerve biology, spanning:

• Human peripheral nerve tissue data, such as sural nerve, skin biopsy samples, dorsal root ganglion (DRG) tissue and primary cells in culture.
• Pre-clinical model data, including transcriptomic and epigenomic profiling of sciatic nerve, DRG, Schwann cells, and neuromuscular junction (NMJ) tissue from CMT and other peripheral neuropathy models
• iPSC-derived model data, including human iPSC-derived sensory and motor neurons, Schwann cells, and organoid systems that bridge pre-clinical and human research
• A range of additional data types, including bulk RNA-seq, single-cell/single-nucleus RNA-seq, and epigenomic datasets

Datasets come from several sources: published research literature, including data deposited in the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO), a widely used public repository for gene expression data; national research consortia focused on the peripheral nervous system and human tissue biology; NIH-funded initiatives; and research awards funded directly by CMTRF.

What Shared Research Infrastructure Has Done Elsewhere

NODE is built on the same underlying technology as gEAR (the gene Expression Analysis Resource, umgear.org), a multi-omic data platform developed by bioinformatics experts at the University of Maryland Baltimore’s Institute for Genome Sciences. gEAR was created to serve the hearing research community, and it has grown into one of that field’s central shared resources: it now hosts hundreds of datasets, has thousands of registered users worldwide, and has been cited in well over a hundred published studies. Its underlying technology has also been adapted for other research communities, including the NIH BRAIN Initiative’s NeMO Analytics platform for neuroscience.

That track record is the reason CMTRF chose to build NODE on this same foundation: proven technology, adapted for peripheral nerve and neuropathy research, with the same goal of turning scattered datasets into a shared resource that accelerates the entire field. With the gEAR platform underpinning it, NODE will be able to provide tools to perform complex data analysis directly.

Get Involved

CMTRF funds targeted research awards to help grow NODE’s dataset library and close gaps in peripheral neuropathy research. Datasets generated through CMTRF-funded awards are deposited into NODE, making them available for the broader research community to reuse and build on.

Have a dataset to contribute, or questions about how NODE fits your research? Contact us at [email protected].