Finally out! 🤩 Check out our HT- PELSA for high throughput screening！

Happy to see our HT-PELSA paper now published in @natsmb.nature.com 🎊 Big thanks for the constructive review process! 📖Read the manuscript here (www.nature.com/articles/s41...) & check the thread for additional information ⬇️

🚨 New preprint We present an extended version of ScAPE, the method that won one of the prizes 🏆 in the @neuripsconf.bsky.social 2023 Single-Cell Perturbation Prediction challenge. 📄 preprint: doi.org/10.1101/2025... 🧬 code: github.com/scapeML/scape

Our 2023 SCP model is now out as a manuscript! Curious about a lightweight baseline for your context generalization task? Check it out 👇

ScAPE: A lightweight multitask learning baseline method to predict transcriptomic responses to perturbations https://www.biorxiv.org/content/10.1101/2025.09.08.674873v1

So grateful and honored to receive the MCP Lectureship Award at the #ASBMBProteomics meeting at the Broad Institute! Huge thanks to the wonderful organizers and to everyone—past and present—who’s been part of our lab.

Our paper benchmarking foundation models for perturbation effect prediction is finally published 🎉🥳🎉 www.nature.com/articles/s41... We show that none of the available* models outperform simple linear baselines. Since the original preprint, we added more methods, metrics, and prettier figures! 🧵

This project has been in the making for quite some time. CORNETO not only integrates key concepts and methodologies in biological network inference, but also introduces a novel framework for multi-condition analysis. Congrats to the team, and especially to @pablormier.bsky.social for leading this.

🎉 The revised version of CORNETO, our unified Python framework for knowledge-driven network inference from omics data, is published in peer reviewed form 🔗 Paper: www.nature.com/articles/s42... 📖 News & Views: www.nature.com/articles/s42... 💻 Code: corneto.org 🧵 Thread 👇

How can we find out what’s really going on inside cells when we’re generating so much complex data? CORNETO is an open-source tool that uses machine learning to turn tangled omics datasets into clear maps of how genes, proteins, and signalling pathways interact. www.ebi.ac.uk/about/news/r... 🧪

I wrote a short piece on how becoming an IgA Nephropathy patient has changed my perspective on biomedical research, developing an appreciation for the challenges in data interpretability & availability and the importance of patient engagement www.nature.com/articles/s41...

The final version of our multi-omics study on kidney fibrosis is out now (tinyurl.com/kidneyfibMSB). Together w/ Pepperkok + Savitski labs @embl.org, we present a time-resolved #multiomics + computational network modeling approach in combination w/ phenotypic assays to study #kidneyfibrosis

In this Voices article, our @martingarridorc.bsky.social & @juliosaezrod.bsky.social discuss key strategies for building context-specific molecular networks: knowledge and data-driven methods, + future directions in the field bridging to structures and perturbations tinyurl.com/z8t6yych

Very excited that another chapter of my PhD is finally out! 🎉 We compared methods to infer kinase activities from #phosphoproteomics data so check it out if that sounds interesting to you. More details in the thread below 🧵👇🏼

Ever wondered which method to use to infer kinase activities from phosphoproteomics data? 💻💭Our revised comprehensive evaluation of kinase activity inference tools, done in collaboration with the Zhang lab @bcmhouston.bsky.social, is now out @natcomms.nature.com 🔬 tinyurl.com/4twuc6z4

We have shared some thoughts on the key challenges in mapping molecular interaction networks in this article. Check it out 👇! www.cell.com/cell-systems... Big thanks to @juliosaezrod.bsky.social and @cp-cellsystems.bsky.social for the opportunity to contribute!

@cp-cellsystems.bsky.social organized a set of short commentaries on: "What is the current bottleneck in mapping molecular interaction networks?" with contributions from many scientists working on these problems. www.cell.com/cell-systems...

Heading back from Boston after attending the Future Developers meeting of the May Institute. Huge thanks to @olgavitek.bsky.social for organizing this meeting, and to all the participants for the great feedback and thoughtful discussions!

Primary article: "Uncovering protein glycosylation dynamics and heterogeneity using deep quantitative glycoprofiling (DQGlyco)" from @savitski-lab.bsky.social et al - equal contribution from @miraburtscher.bsky.social, @martingarridorc.bsky.social, and Clement Potel. www.nature.com/articles/s41...

Online now - the Spotlight "Accelerating the stride toward functional #glycoproteomics" from @timveth.bsky.social and @nmriley.bsky.social. #Glycoproteins #Glycoforms #Glycoprofiling #DQGlyco #Glycotime authors.elsevier.com/a/1l3k53S6Gf...

Want to know how the ligands interact with proteins beyond model cell lines, e.g., in tissues or bacteria? Interested in membrane targets? Check out our High-Throughput PELSA method which allows you do all these cool screenings for dozens of ligands within two hours! www.biorxiv.org/content/10.1...

🚨 New preprint: Topography Aware Optimal Transport for Alignment of Spatial Omics Data We present our new alignment framework TOAST www.biorxiv.org/content/10.1...

📄 Update on our preprint about Gene Regulatory Net (GRN) benchmarking 📄 We have included the original and decoupled version of SCENIC+, added a new metric and two more databases. Dictys and SCENIC+ outperformed others, but still performed poorly in causal mechanistic tasks. doi.org/10.1101/2024... 👇

Congrats to our @juliosaezrod.bsky.social and all other elected fellows of the International Society for Computational Biology (ISCB) as part of the 2025 Class

Relying on a ticket in our support: How do we know the number of RAW files for all the public datasets in @pride-ebi.bsky.social ? response: github.com/orgs/PRIDE-A... script: gist.github.com/ypriverol/f2...

More important, if you have questions like this, other people can benefit from it. You don't need to send an email to pride support, just ask the question in our forum github.com/orgs/PRIDE-A...

Preprint just out: Our international team's perspective on proteomics and AI, led by Tiannan Guo. We outline key areas where global collaboration between AI and proteomics researchers can accelerate biological discoveries and medical applications. Check it out: arxiv.org/abs/2502.15867

Hot of the press!! Whether you feel your data can benefit from network biology, you’re a method developer, or you have some strategies to evaluate networks, check this out! NetworkCommons is actively looking for contributions :)

The first version of NetworkCommons is now published in Bioinformatics. Next, we’ll focus on involving more of the network biology community. academic.oup.com/bioinformati... In parallel, we’ll continue expanding benchmarks and developing new applications. Interested in contributing? Reach out! ⬇️

Bacteria can influence how sugars modify proteins in the brain – shown for the first time by EMBL researchers. In their study, the scientists describe a new method to study glycosylation systematically & quantitatively, leading to new biological insights. 🧪🧠📈 www.embl.org/news/science...

Excited to see this huge piece of work out 🎉 Congrats to everyone involved! If you ever wanted to read a 8in1 paper grab a cup of tea and enjoy 📖

Excited to share our new paper! If you’re interested in glycoproteomics, be sure to check it out 👇 Huge kudos to all coauthors, especially the amazing glycoteam: Clement, @miraburtscher.bsky.social, Isabelle, and Misha (@savitski-lab.bsky.social)!

We are very happy to present our work on N-glycoproteomics!🍬 Our method enables the selective enrichment and precise quantification of intact N-glycopeptides to explore the dynamics of glycosylation microheterogeneity. www.nature.com/articles/s41...

🎉 We're happy to announce that our latest project was published in @naturecomms.bsky.social this week: PTMNavigator, a #bioinformatics web platform for in-depth analysis of post-translational modification (PTM) perturbation datasets. 📄 doi.org/10.1038/s414... (1/6)

We present Gene Regulatory nETwork Analsyis (GRETA), a framework to infer, compare and evaluate gene regulatory networks #GRNs. With it, we have benchmarked multimodal and unimodal GRN inference methods. Check the results here 👇 Paper: doi.org/10.1101/2024.12.20.629764 Code: github.com/saezlab/greta

The final chapter of my PhD thesis is now out! 🎉 We compared the latest gene regulatory network (#GRN) inference methods for #single-cell multimodal datasets and evaluated their performance across various tasks. Hard to believe this journey started in March 2021 and has finally reached this point 😅🥳

NetworkCommons is out! It's been a lot of fun to work on this project, please consider contributing to this exciting initiative to democratise network biology 🪐 Thanks a lot to all coauthors especially Denes, @martingarridorc.bsky.social and @juliosaezrod.bsky.social

NetworkCommons is out! This initiative aims to enhance programmatic interoperability between data, knowledge and methods with the hope of developing more robust benchmarks in the future. Shout out to @vicpaton.bsky.social and Denes for their amazing work here 👇

We present NetworkCommons, a unified platform 🪐 for network biology, providing access to omics data, knowledge, and contextualization methods, all with a consistent API 👇🧵 Paper: doi.org/10.1101/2024... Docs: networkcommons.readthedocs.io

Very cool to see our model scAPE perform so well in this benchmark @pablormier.bsky.social ! openreview.net/forum?id=WTI... Thanks to the organizers for this amazing competition.