ME/CFS Bioinformatics Repository

[tralfamadorian97]

Since the DecodeME preprint was released, I have been teaching myself bioinformatics by analyzing the summary statistics from DecodeME and other GWAS. I’ve been publishing my code on GitHub here, and documenting my results here.

While I don’t have dramatic headline results, I still thought that this work would be of interest to Science4ME forum members, because there are a few analyses that supplement recent forum discussions. For example, I ran gene-level H-MAGMA on the DecodeME summary statistics.

If anyone wants to contribute, I will happily accept fixes or additions to the documentation or codebase. For minor changes, you can just create a GitHub pull request. For major additions, it is probably better to first create a GitHub issue with a brief proposal, which we can discuss.
@forestglip already found the repo and has made some very helpful contributions

 

[forestglip]

It's been incredible watching this project being developed. I stumbled across it a few months ago, and it was immediately obvious tralfamadorian97 is remarkably motivated, organized, and intelligent.

There are a whole lot of different results and even lessons about bioinformatics tools in the documentation which I found very interesting to explore.

 

[hotblack]

Very impressive! Looks like a great resource, thanks for all your work @tralfamadorian97 and for sharing it.

 

[ME/CFS Science Blog]

Impressive @tralfamadorian97 , thanks.

Do check out Paolo Maccallini's meta-analysis which found some stronger results than DecodeME alone:

A

Preprint Thread 'Biological Insights from Genome-Wide Association Studies and Whole Genome Sequencing of [ME/CFS], 2026, Maccallini et al'

Friday at 12:27 PM

Interesting paper
Mods move if better location

• Amw66
• ampa anterior cingulate cortex brain cerebellum genes glutamate glutamatergic synapses gwas hypothalamus pituitary synapse white matter
• Replies: 64
• Forum: ME/CFS research

• 

I'm particularly interesting in the cell type eccentric medium spinal neuron that was significant in the meta-analysis:

Thread 'Eccentric medium spiny neuron (eMSN)'

Monday at 10:15 PM

Eccentric medium spiny neuron (eMSN) sometimes called eccentric spiny projection neurons (eSPNs) is the cell type that came up in Paolo Maccallini’s genetic analysis for ME/CFS. He used a meta-anaysis of DecodeME and the Million Veteran Program (Neff = 74,219). Using a tool called MAGMA, Paolo could match the DNA results with gene expression data from the human brain atlas published by Siletti et al. 2023. More about the methodology can be discussed in the thread on Paolo’s paper...

• ME/CFS Science Blog
• decodeme emsn gaba glutamate neuron paolo maccallini
• Replies: 50
• Forum: Possible causes and predisposing factor discussion

• 

We've also been trying to use tools such as FLAMES to help identify the causal genes but havent' really managed to make it work. Perhaps you might be able to do it?

Thread 'Running FLAMES on DecodeME data'

Dec 11, 2025

Hi all,

Something that has been in the back of my head since the DecodeME data was released was running the newer FLAMES analysis:

GitHub - Marijn-Schipper/FLAMES: FLAMES: Accurate gene prioritization in GWAS loci

FLAMES: Accurate gene prioritization in GWAS loci. Contribute to Marijn-Schipper/FLAMES development by creating an account on GitHub.

github.com

Somewhat discussed here: https://www.s4me.info/threads/initi...2025-decodeme-collaboration.45490/post-636145

My intention with this thread is that if enough of us get our heads together, I think we me able to run this? I wanted to make a separate thread as I believe if we start working on this it...

• ChronicallyOverIt
• decodeme flames magma
• Replies: 41
• Forum: Other research methodology topics

• 

 

[tralfamadorian97]

Do check out Paolo Maccallini's meta-analysis which found some stronger results than DecodeME alone:

Yes, I did see Paolo's paper. Impressive. I'll read it in more detail when I get a chance.

We've also been trying to use tools such as FLAMES to help identify the causal genes but havent' really managed to make it work. Perhaps you might be able to do it?

I've create a GitHub Issue to track this here. This might take a while, but at the moment I don't see any insurmountable barriers to running this.