Special thanks to all authors and founders!!
Carolina Segura-Morales
Dan F. Moakley
@chaolinzhang.bsky.social
@samuelmiver.bsky.social
Andrea Califano
Luis Serrano
@crg.eu
@columbiacancer.bsky.social
@boehringerglobal.bsky.social
Many regulatory layers modulate splicing factors at the same time impacting their activity.
How can we quantify it? Apparently "functional" target exons give us a hint and uncover two cancer programs.
Have a look at our solution: www.nature.com/articles/s41...
Why are there 20 amino acids and 4 nucleotides?
Combining Energy Landscape and Molecular Information theories provides constraints to the alphabet size of an evolving biopolymer, given its physico-chemical properties...
Read more in our new article:
www.nature.com/articles/s41...
#UBalsMitjans | 👌 @elpuntavui.cat entrevista Raúl Ruiz, estudiant de Bioquímica i professor de llengua de signes catalana, que ha coordinat un vocabulari de termes científics a la #UniBarcelona.
«És una llengua pròpia, totalment vàlida per crear terminologia en àmbits especialitzats», afirma Ruiz.
Oh! I was not aware of this literature, thanks!
Yes, I was thinking of evaluating how often using existing seq2func models with a tool like ledidi would recover the genotype of the person.
I suppose as model personalization improves we'll hit a point we cannot share model weights...
Did you try doing your privacy benchmark with other models that predict ATAC? Should we be concerned about privacy with RNA coverage models too? It was shown how bad models are at personalized predictions, but it is the first time I see a benchmark on how good they can be at identifying people!
popEVE is out in Nature Genetics! 🎉
We built a proteome-wide model that combines cross-species and human population variation to rank missense variants by disease severity and help diagnose rare genetic disorders.
rdcu.be/eRu7K
LFB is NeurIPS-bound! 🎉
Mafalda, @cwjpugh.bsky.social and I will be in San Diego next week for NeurIPS -- happy to chat variant effect prediction (or just say hi).
“From Likelihood to Fitness: Improving Variant Effect Prediction in Protein and Genome Language Models”
openreview.net/pdf/a151f62e...
Great initiative! I have used it uploading computational papers. However there's a message saying that you are not very confident on the platform's feedback for this types of paper. Why is that? What would give you more confidence? My N is low but it was fine!
Our annual PhD call is closing at the end of this week on 30 November. If you're interested in carrying out world-class scientific research in Barcelona, you still have a few days left to submit your application! www.crg.eu/en/content/t...
Very nice approach! Is the code (and pretrained weights) available? Thanks!!
Very nice!
Thank you!
We would also like to thank @narjournal.bsky.social 's editorial team and reviewers for their feedback and support!
Let us know what you think! We’re very excited to see how our approach can lead to new insights for you!
This work would not have been possible without my super supervisors, Samuel Miravet-Verde & Luis Serrano and the Serrano Lab team, at the wonderful @crg.eu
Although more validation will be needed, we believe our work will enable studying the state of splicing factors in widely available and single-cell atlases, contributing to providing a more complete picture of splicing regulation in data-scarce but experimentally very rich settings.
Interestingly, during embryogenesis, the regulation of splicing factors follows the opposite trend from that observed during carcinogenesis. MYC, G2M, and E2F prioritized pathways are downregulated during human embryogenesis, supporting their role as regulators of the carcinogenic switch of SFs.
Because our prioritization involved Perturb-seq experiments, we could ask which other pathways had also strong evidence as mediators. These were: G2M checkpoint, E2F targets, and spermatogenesis.
Long story short, the MYC pathway was the top candidate, further supporting the known importance of MYC in regulating splicing (great references: Leclair et al. 2022 ( @olgaanczukow.bsky.social lab) and Koh et al. 2015 ( @guccionelab.bsky.social lab)).
But most cancer-driver mutations don’t involve splicing factors, so how does cancer induce this aberrant regulation in splicing factors?
We came up with a strategy to isolate the best candidate pathways connecting cancer-driver mutations and carcinogenic splicing factor regulation.
A nice insight was to see that not one but many splicing factors can drive their own aberrant regulation. We found evidence that they do so through their splicing factor-exon and protein-protein interactions.
Through Perturb-seq datasets in RPE1 pre-cancerous cells (Replogle et al. 2022 ( @weissmanlab.bsky.social )), we could dissect systematically which genes drive carcinogenesis regulation of splicing factors.
This enabled us to use existing datasets to explore how splicing factors are regulated during carcinogenesis in bulk (Danielsson et al. 2013 (Emma Lundberg lab)) and single-cell models (Hodis et al. 2022 (Aviv Regev lab)).
Building on our method for splicing factor activity analysis (doi.org/10.1101/2024...), we expand our database of experiments that perturb SFs and show that adjusting SF activities with a “shallow” neural net does well at recapitulating exon-inclusion-based activities from only gene expression.
Wouldn’t it be cool to leverage the throughput of single-cell data to study splicing regulation even when we lack exon resolution? 😀
Here’s the peer-reviewed version of our paper on how we can measure changes in splicing factor activity in virtually any single-cell dataset: doi.org/10.1093/nar/...
Couldn't think of a better place to make models! Come join us!
Es Castell
@splicingnews.bsky.social
An organoid model of the menstrual cycle reveals the role of the luminal epithelium in regeneration of the human endometrium www.biorxiv.org/content/10.1101/2025.07....