Colm Ryan

@colmr.bsky.social

Systems biology | Bioinformatics | Cancer | Genetic interactions https://cancerdata.ucd.ie/ (he / him)

Preprint alert! 🚨 We are very excited to share our new manuscript on Bohring-Opitz syndrome, a devastating rare monogenic disorder driven by truncating variants in ASXL1. This project was led by PhD student Emma Doyle, one of the OG Conway lab members 💪. 1/6 www.biorxiv.org/content/10.6...

Divergent Pathogenic PR-DUB Complex Variants Converge Functionally Via PRC2 Displacement From Chromatin

The PR-DUB complex is responsible for erasing the repressive histone modification, H2AK119ub1. ASXL1-3 proteins are mutually exclusive catalytic partners of BAP1 in the PR-DUB complex. Somatic heteroz...

biorxiv.org

Benchmarks of AI agentic systems remind me of bioinformatics 20 years. And not in a good way! Is anyone else getting tired of everyone reporting that they are better than everyone else according to their own new benchmark? 🤬

With lab members from both France and Spain, I have to hope for an unprecedented draw in this evening’s semifinal. Perhaps both teams will shake hands, acknowledge how good they both are, and then go for a celebratory smoothie.

My letter to Minister Lawless about Research Ireland's decision to devolve the awarding of doctoral scholarships & postdoctoral fellowships to universities. A disastrous move. Please consider raising this issue in your own institutions and with elected representatives and the media.

BildBild

If you are interested in PARP inhibitors and history, this might be of interest to you. www.nature.com/articles/s41.... An attempt to cover the work of thousands and their immense contributions to the development of a targeted approach to treating cancers.

Two decades of PARP inhibitor synthetic lethality in cancer - Nature

The past two decades of PARP inhibitor synthetic lethality in cancer is explored.

nature.com

Our preprint is now published in MSB. link.springer.com/article/10.1... We decompose multi-omics into distinct phenotypic axes (drug response vs ARID1A-driven cell state), improving interpretation and revealing how baseline cell state rewires signaling and shapes MAPK inhibitor resistance.

Integrative multi-omics defines melanoma drug response networks and ARID1A-dependent resistance mechanisms - Molecular Systems Biology

Resistance to BRAF/MAPK inhibitors is a significant challenge in melanoma treatment, driven by adaptive and acquired mechanisms allowing tumor cells to evade therapy. We explored early signaling respo...

link.springer.com

Evangelia Petsalaki@epetsalaki.bsky.social · 2y ago

New preprint from our group @ebi.embl.org and the Vidal-Puig group! We combined pseudotemporal ordering of cross sectional MASLD patient data with network analysis to describe the disease progression and identify a signature of 24 plasma biomarkers for patient staging www.biorxiv.org/content/10.1...

New paper from Narod Kebabci – “A predicted cancer dependency map for paralog pairs” www.biorxiv.org/content/10.6... Background: The Cancer Dependency Map from @depmap.org is a fantastic resource that characterises genetic dependencies at genome-wide scale across ~1,000 cancer cell lines. 1/9

A predicted cancer dependency map for paralog pairs

Background Genome-wide CRISPR screening has enabled the development of dependency maps in hundreds of cancer cell lines, facilitating the identification of genetic vulnerabilities associated with specific biomarkers. Paralogs, despite being common drug targets, are often missed in these screens as their individual disruption rarely causes a significant fitness defect. Combinatorial screens have revealed that paralog pairs are often synthetic lethal but that these effects are highly context specific. To develop paralogs as therapeutic targets we must identify which paralog pairs are synthetic lethal in which cancer contexts. Results We develop a machine learning classifier to predict cell-line specific synthetic lethality between paralog pairs. We demonstrate the utility of features derived from the cell-line specific expression and essentiality of the pair and their protein-protein interaction partners for this purpose. We evaluate our predictions across multiple scenarios: predicting for the same pairs in unseen cell lines, for new gene pairs in seen cell lines, and for entirely uncharacterized pairs in unseen cell lines. We show that we can make predictions across all scenarios. We validate our predictions using independent combinatorial CRISPR screens and show that the agreement between our predictions and published experiments approaches the agreement across experiments. Conclusions Our classifier predicts cell-line-specific synthetic lethality between paralog pairs and provides insights into the underlying features driving these interactions. We make our predictions for 1,005 cell lines available as a resource to facilitate the discovery of context-specific paralog synthetic lethalities and to guide the design of more targeted combinatorial screens. ### Competing Interest Statement The authors have declared no competing interest. Research Ireland, 20/FFP-P/8641, 18/CRT/6214

biorxiv.org

New preprint on technologies to scale up CRISPR screens. We use them to map 665,856 pairwise genetic perturbations and outline a path to comprehensive interaction mapping in human cells. We also introduce an approach for cloning lentiviral libraries with billions of elements.

Bild

Enjoying 'Why Machines Learn' by Anil Ananthaswamy, including this anecdote highlighting that there's always more that *could* be in a PhD but that you have to draw a line somewhere. Guyon here is Isabelle Guyon, who was later key to the development of SVMs (especially the kernel trick)

Photo containing the following text: "Their paper was published a year before Guyon defended her Ph.D. thesis, for which she had tested numerous algorithms for linear classification—but none of these was an optimal margin classifier, meaning the algorithms found some linear boundary, not necessarily the best one. Guyon could have used Krauth and Mézard's algorithm to implement an optimal margin classifier; she didn't. "One of the examiners of my Ph.D. asked me why I did not implement the algorithm of Mézard and Krauth and benchmark it against the other things I was trying. I said, 'Well, I didn't think it would make that much of a difference,'" Guyon told me. "But the reality is that I just wanted to graduate, and I didn't have time."

Replacing my still functioning 2017 Mac because it's no longer compatible with our two factor authentication software (preventing me logging on to any work related system). This really doesn't seem optimal.

Bild