Miquel Anglada-Girotto's Avatar

Miquel Anglada-Girotto

@m1quelag.bsky.social

Love predicting genomic things. Postdoc @crgenomica.bsky.social at the Probabilistic Machine Learning and Genomics group. Creator of @splicingnews.bsky.social

358 Followers  |  1,646 Following  |  46 Posts  |  Joined: 21.10.2024
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Posts by Miquel Anglada-Girotto (@m1quelag.bsky.social)

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"La llengua de signes s'hauria d'estudiar a totes les escoles" "El 2010 la llengua de signes catalana es va reconèixer a través d'una llei però això és teòric, falta portar-ho a la pràctica" "És important veure la llengua de signes catalana des de la perspectiva...

#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.

03.03.2026 08:46 — 👍 5    🔁 1    💬 0    📌 0

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...

12.12.2025 09:29 — 👍 3    🔁 0    💬 0    📌 0

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!

08.12.2025 09:36 — 👍 0    🔁 0    💬 1    📌 0
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Proteome-wide model for human disease genetics Nature Genetics - popEVE is a proteome-wide deep generative model to identify and predict pathogenicity of missense mutations causing genetic disorders.

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

24.11.2025 13:35 — 👍 50    🔁 20    💬 2    📌 1

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...

25.11.2025 11:55 — 👍 10    🔁 1    💬 1    📌 0

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!

25.11.2025 17:53 — 👍 1    🔁 0    💬 1    📌 0
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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...

25.11.2025 07:43 — 👍 14    🔁 18    💬 0    📌 3

Very nice approach! Is the code (and pretrained weights) available? Thanks!!

21.11.2025 23:00 — 👍 1    🔁 0    💬 1    📌 0

Very nice!

21.11.2025 22:57 — 👍 1    🔁 0    💬 0    📌 0

Thank you!

26.10.2025 20:09 — 👍 1    🔁 0    💬 0    📌 0

We would also like to thank @narjournal.bsky.social 's editorial team and reviewers for their feedback and support!

25.10.2025 14:16 — 👍 0    🔁 0    💬 0    📌 0

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

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0

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.

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0

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.

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0

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.

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0

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)).

25.10.2025 14:16 — 👍 1    🔁 0    💬 1    📌 0

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.

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0

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.

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0

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.

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0

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)).

25.10.2025 14:16 — 👍 0    🔁 0    💬 1    📌 0
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Exon inclusion signatures enable accurate estimation of splicing factor activity Splicing factors control exon inclusion in messenger RNAs, shaping transcriptome and proteome diversity. Their catalytic activity is regulated by multiple layers, making single-omic measurements on th...

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.

25.10.2025 14:16 — 👍 1    🔁 0    💬 1    📌 0
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Using single-cell perturbation screens to decode the regulatory architecture of splicing factor programs Abstract. Splicing factors shape the isoform pool of most transcribed genes, playing a critical role in cellular physiology. Their dysregulation is a hallm

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/...

25.10.2025 14:16 — 👍 10    🔁 3    💬 1    📌 0

Couldn't think of a better place to make models! Come join us!

23.10.2025 12:11 — 👍 1    🔁 0    💬 0    📌 0
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Es Castell

15.07.2025 19:20 — 👍 1    🔁 0    💬 0    📌 0

@splicingnews.bsky.social

14.07.2025 03:14 — 👍 0    🔁 0    💬 0    📌 0

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

07.07.2025 10:30 — 👍 8    🔁 4    💬 0    📌 1
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

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...

06.07.2025 18:14 — 👍 59    🔁 24    💬 1    📌 2

@splicingnews.bsky.social

02.07.2025 05:55 — 👍 0    🔁 0    💬 0    📌 0

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`

18.06.2025 09:48 — 👍 38    🔁 11    💬 1    📌 1
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Tomtom-lite: Accelerating Tomtom enables large-scale and real-time motif similarity scoring Summary Pairwise sequence similarity is a core operation in genomic analysis, yet most attention has been given to sequences made up of discrete characters. With the growing prevalence of machine lear...

I wrote a quick application note on Tomtom-lite, a Python implementation of the Tomtom algorithm for comparing PWMs against each other. This implementation can be 10-1000x faster and, as a Python function, can be integrated into your workflows easier.

www.biorxiv.org/content/10.1...

03.06.2025 18:02 — 👍 58    🔁 18    💬 2    📌 2