Miquel Anglada-Girotto
@m1quelag.bsky.social
340 followers 1.6K following 27 posts
Love predicting genomic things. Postdoc @crgenomica.bsky.social at the Probabilistic Machine Learning and Genomics group. Creator of @splicingnews.bsky.social
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Reposted by Miquel Anglada-Girotto
biorxivpreprint.bsky.social
An organoid model of the menstrual cycle reveals the role of the luminal epithelium in regeneration of the human endometrium https://www.biorxiv.org/content/10.1101/2025.07.03.663000v1
Reposted by Miquel Anglada-Girotto
aelek.bsky.social
I am very happy to have posted my first bioRxiv preprint. A long time in the making - and still adding a few final touches to it - but we're excited to finally have it out there in the wild:
www.biorxiv.org/content/10.1...
Read below for a few highlights...
Decoding cnidarian cell type gene regulation
Animal cell types are defined by differential access to genomic information, a process orchestrated by the combinatorial activity of transcription factors that bind to cis -regulatory elements (CREs) to control gene expression. However, the regulatory logic and specific gene networks that define cell identities remain poorly resolved across the animal tree of life. As early-branching metazoans, cnidarians can offer insights into the early evolution of cell type-specific genome regulation. Here, we profiled chromatin accessibility in 60,000 cells from whole adults and gastrula-stage embryos of the sea anemone Nematostella vectensis. We identified 112,728 CREs and quantified their activity across cell types, revealing pervasive combinatorial enhancer usage and distinct promoter architectures. To decode the underlying regulatory grammar, we trained sequence-based models predicting CRE accessibility and used these models to infer ontogenetic relationships among cell types. By integrating sequence motifs, transcription factor expression, and CRE accessibility, we systematically reconstructed the gene regulatory networks that define cnidarian cell types. Our results reveal the regulatory complexity underlying cell differentiation in a morphologically simple animal and highlight conserved principles in animal gene regulation. This work provides a foundation for comparative regulatory genomics to understand the evolutionary emergence of animal cell type diversity. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, https://ror.org/0472cxd90, ERC-StG 851647 Ministerio de Ciencia e Innovación, https://ror.org/05r0vyz12, PID2021-124757NB-I00, FPI Severo Ochoa PhD fellowship European Union, https://ror.org/019w4f821, Marie Skłodowska-Curie INTREPiD co-fund agreement 75442, Marie Skłodowska-Curie grant agreement 101031767
www.biorxiv.org
Reposted by Miquel Anglada-Girotto
jmschreiber91.bsky.social
Last week I released bpnet-lite v0.5.0.

BPNet/ChromBPNet are powerful models for understanding regulatory genomics from @anshulkundaje.bsky.social's group, and now it's way easier to go from raw data to trained models and analysis + results in PyTorch

Try it out with `pip install bpnet-lite`
Reposted by Miquel Anglada-Girotto
Reposted by Miquel Anglada-Girotto
ebi.embl.org
Polygenic scores (PGS) offer insights into a person’s inherited risk of disease.

GeneticScores.org is a new platform that enables secure, cloud-based calculation of polygenic scores to make genomic risk prediction more accessible.

www.ebi.ac.uk/about/news/u...

🖥️🧬
m1quelag.bsky.social
Today I learned artists study primitive art to understand how art was made out of the art business context.

This made me wonder how science would be made nowadays out of the journal publishing context. Would we try to answer different questions?
m1quelag.bsky.social
Leveraging evolution to make fitness estimation scale with model size again! Great experiencing the making of this one behind the scenes 🙌
Reposted by Miquel Anglada-Girotto
stephenturner.us
polars-bio - fast, scalable and out-of-core operations on large genomic interval datasets www.biorxiv.org/content/10.1... 🧬🖥️🧪 github.com/biodatageeks...
m1quelag.bsky.social
I would like to thank Samuel Miravet-Verde and Luis Serrano for their supervision, and the support of @crg.eu !
m1quelag.bsky.social
Our carcinogenesis use case is just an example of how single-cell perturbation screens can be leveraged.

This goes along the lines of a recent study by Ota et al bsky.app/profile/jkpr...

We believe our approach is flexible enough to study the architecture of many other splicing factor programs.
jkpritch.bsky.social
Modern GWAS can identify 1000s of significant hits but it can be hard to turn this into biological insight. What key cellular functions link genetic variation to disease?

I'm very excited to present our new work combining associations and Perturb-seq to build interpretable causal graphs! A 🧵
m1quelag.bsky.social
So, being able to query SF regulation systematically using single-cell perturbation screens revealed a model of their regulation during carcinogenesis:
- oncogenic lesions activate MYC
- activation of oncogenic-like and inactivation tumor suppressor-like SFs through their cross-regulation
m1quelag.bsky.social
We then asked which pathways could be linking carcinogenic mutations with SF regulation.

Among the candidates, MYC stands out as the top one!

MYC had already been shown to be a regulator of SFs, but it had never been confirmed unbiasedly.
m1quelag.bsky.social
Now we can look at how each Perturb-seq KD changes the activity of cancer splicing programs.

Perturbations that activate one program inactivate the other, and those causing carcinogenic regulation are enriched in SFs themselves.

Cross-regulation among SFs drives their carcinogenic regulation.
m1quelag.bsky.social
While directly porting our method to GE did not fully recapitulate the regulation of SFs observed with exon inclusion, adjusting gene-based SF activity with a single fully-connected NN layer did the job!

Actually, we confirmed the coordinated regulation of SF programs in melanoma carcinogenesis!
m1quelag.bsky.social
How can we study the regulation of these cancer SF programs systematically?

Single-cell perturbation screens have great throughput but don’t have exon inclusion resolution to estimate SF activity with our approach.

So we need to estimate SF activity from GE to analyze Perturb-seq data.
m1quelag.bsky.social
We previously showed that changes in target exon inclusion are highly informative of the activity of SFs: doi.org/10.1101/2024...

We identified two pan-cancer SF programs that show coordinated regulation during carcinogenesis: oncogenic SFs become more active than tumor suppressor-like SFs.
m1quelag.bsky.social
How can we leverage Perturb-seq screens to study splicing factor (SF) regulation systematically?

Here’s our approach: bsky.app/profile/bior...
biorxiv-sysbio.bsky.social
Using single-cell perturbation screens to decode the regulatory architecture of splicing factor programs https://www.biorxiv.org/content/10.1101/2025.02.07.637061v1
m1quelag.bsky.social
For example, running the notebooks using the WT and the MUT sequence of LMNA causing limb girdle muscular dystrophy 1B ( doi.org/10.1038/nrg.... ), Pangolin predicts a decrease in exon 9's 5' splice site strength:
IGV visualization of wild-type and mutant LMNA sequences with their predicted splice site strengths by Pangolin.
m1quelag.bsky.social
Inputs:
- sequence coordinates
- or custom sequence

Outputs:
- predictions dataframe
- predictions bigwigs
- seaborn and IGV visualizations
m1quelag.bsky.social
Hi all! Inspired by how easy ColabFold ( @sokrypton.org ) made prot structure prediction for me, I have started ColabRNA to facilitate making predictions with RNA-based models!

Currently, the following models are available:
- SpliceAI
- Pangolin
- SpliceTransformer
- Borzoi

Happy to get feedback!
GitHub - MiqG/ColabRNA: Making RNA-based models accessible to all.
Making RNA-based models accessible to all. Contribute to MiqG/ColabRNA development by creating an account on GitHub.
github.com