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Thanks! Really useful, I appreciate all the info, I was a bit unclear from the thread I linked to and a quick search.

Such a waste, 20m is absolutely nothing in government budget terms. They say they want to cut costs on welfare and health and yet when given the opportunity to do so for a fraction of what they spend on those, they don’t. Complete failure. They spent vast sums over the pandemic, this is a...

I know very little about them, tried a search and found broken links, any pointers to who they are, how to contact them and work with them if you’re just an individual? The thread here talks about contact if you’re a group leader, what if there are no groups of any use near you? We could do...

Looking at the gene cards for the non coding elements a number of these seem to be promoters/enhancers related to NEGR1. I guess not surprising given the proximity. But could here be something here (and potentially with other variants or near misses on significance) which means we’re seeing...

LocusZoom for this location on DecodeME data with some things going on just along to the right if you scroll along or zoom out… And the Genecard

I also put together a visualisation tool to go with my earlier script, on the dev branch, that can be run locally and accessed through a browser. I can guarantee this will be full of incorrect scientific assumptions, behaviour which mischaracterises data and misunderstandings of APIs as well as...

Looks like there were some remaining bugs which explains some of what I was seeing. And there are probably still more! Anyway, I’ve not tested everything (particularly running from scratch without cached data or the options to replace hardcoded values I was using) and it’s a blag of borrowed...

I need to catch up with this thread but looks like an interesting discussion. I’ve been throwing some ideas around and playing with some approaches to look at lots of SNPs at once. I got loads of data to start so have been trying to be a bit smarter and narrow things down. Not sure what it all...

Agree. And it’s one of the things that those involved in the MEA’s health and social care team have tried to use to explain or justify their approach when questioned on it. Utterly misguided.

Interesting reading the peer review letters and understanding changes made too. Agree with @Simon M and it’s great to see this published. Congratulations to Audrey, @chillier all involved. Thanks also to AfME for funding it!

I’ve put together a web app and a printable template so people can give this idea a go if they want. Hopefully it will allow people to experiment with the idea here and see what works in principle. If the idea is useful then hopefully others who are better able can take this on and develop it...

Yeah, that was what I was starting to think with my comments yesterday and it seemed implied by comments on the roundtable discussion. Although I’ve got a lot more to read/listen to and hadn’t got things to work to be sure. Nice work there, thanks for sharing your code snippets and findings (and...

That’s my understanding. All the groups of variants and all the different scorers (not just RNA_SEQ) and different tissues… Then visualise and interpret it and… there’s a lot of work I think!

From what I can tell - Focus is on scoring and visualising a single variant, you can do batches of variants though - Apparently it currently predicts many many scores per variant, so there’s a lot of data to go through and interpret - It looks like the team are hoping to support large scale...

Well that’s incredibly disappointing. Thrown under the bus again. I guess we should wait for official word on what this is rather than interpret too much. But equally plan on what to do next and what to focus on that we may still be able to influence.

I fed the API documentation and details of the structure of the DecodeME summary statistics files to Gemini to get an idea of what may be needed, outline follows for those interested, treat with caution, etc etc. The first steps should be familiar from our experiments last year.

The API uses hg38 which is what DecodeME summary stats use so we don’t even need to do liftover There’s a video tutorial here, lots more reading to do but definitely looks like something someone should give a go! And a roundtable from the team

Will definitely be interesting to see how this is used. Deepmind and Isomorphic are doing really interesting things I think and great that this has now been released after being talked about last year.There’s an API available too with keys available for free for non commercial use...

Agree. There’s no excuses. Hospitals and their staff have repeatedly failed people. We’ve had enquiries. If the medics won’t act voluntarily (they haven’t) they should be compelled to by government. If there were more public attention and acceptance of the condition there would be an outcry and...

How can this move the field forward when there is (1) no new evidence, it’s a review and (2) no new ideas, just claims of things like neuroinflammation and t-cell exhaustion we’ve heard before.. Repeating something does not move a field forward. New evidence and new ideas do.