Carl de Boer
@carldeboer.bsky.social
470 followers 170 following 65 posts
Assistant Professor, UBC school of Biomedical Engineering. Trying to enable personalized medicine by solving gene regulatory code.
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Reposted by Carl de Boer
rna-ken.bsky.social
Open rank tenure-track position in comp bio at the University of Montreal. Great environment, great position. @futurepislack.bsky.social

medecine.umontreal.ca/wp-content/u...
medecine.umontreal.ca
carldeboer.bsky.social
New preprint from the lab 🎉 on the evolution of gene regulatory network complexity, where we tested whether sexual recombination promotes more complex GRNs using biochemically-inspired evolutionary simulations 🧐. Bluetorial from 1st author @chapelmadison.bsky.social 👇
Reposted by Carl de Boer
shaunmahony.bsky.social
New preprint from the lab: Dr. Daniela James led an effort to improve the ChIP-exo protocol for high-res protein-DNA interactions. Particularly focused on optimizing for use in mammalian cells and making the protocol compatible with newer Illumina sequencers.
www.biorxiv.org/content/10.1...
Optimized ChIP-exo for mammalian cells and patterned sequencing flow cells
By combining chromatin immunoprecipitation (ChIP) with an exonuclease digestion of protein-bound DNA fragments, ChIP-exo characterizes genome-wide protein-DNA interactions at near base-pair resolution...
www.biorxiv.org
Reposted by Carl de Boer
aguirre404.bsky.social
Thrilled to share the second half of my PhD work here!

We show how data on expression quantitative trait loci (eQTL) relates to the structure of gene regulatory networks (GRN). Much of the GRN / eQTL picture is unmapped, but what we do have says a lot… (1/)

doi.org/10.1101/2025...
Reposted by Carl de Boer
arnaudkr.bsky.social
This is not a HiC map! Ever wondered if multiple enhancers get activated simultaneously? We measured chromatin accessibility on thousands of molecules by nanopore to create genome-wide co-accessibility maps. Proud of @mathias-boulanger.bsky.social @kasitc.bsky.social Biology in the thread👇
Reposted by Carl de Boer
proftomellis.bsky.social
New paper from our lab on synthetic genome work in yeast is out - Iterative SCRaMbLE for Engineering Synthetic Genome Modules and Chromosomes. Exciting project led by Jane (Xinyu) Lu in our group, now online. t.co/0LuGwgAWlA
https://www.nature.com/articles/s41467-025-62356-y
t.co
Reposted by Carl de Boer
eileen-furlong.bsky.social
To all post-docs: The Genome Biology dept ‪@embl.org
has an Independent faculty position. Fantastic place to set up your lab –great package: core funding, fantastic Ph.D. students, cutting edge core facilities & great colleagues. Closing date Sept 19th
embl.wd103.myworkdayjobs.com/en-US/EMBL/j...
Group Leader - Genome Biology Unit
Are you ready to lead groundbreaking research in Genome Biology? Join us at EMBL! We are seeking a motivated scientist to lead an independent research group addressing exciting and original biological...
embl.wd103.myworkdayjobs.com
carldeboer.bsky.social
GAME was designed in consultation with many functional genomics and ML researchers. Many enthusiastically contributed their models and datasets in GAME modules. Thanks to all the coauthors and colleagues for your support in developing GAME! 🙏
carldeboer.bsky.social
As GAME builds momentum, more models and benchmarks will be created, and, because they’re all inherently cross-compatible, the easiest way to benchmark your model will be to put it in GAME, snowballing further.📈
carldeboer.bsky.social
6) Better estimates of real-world performance (since tasks and models are cleanly separated and uniformly applied)
7) GAME enables continual benchmarking; we anticipate a yearly “state of the field” to be easily produced. Here's an example for gene expression prediction using existing GAME modules.
carldeboer.bsky.social
5) Getting funding 💰and people for long term maintenance of bioinformatics projects is notoriously challenging😱. Our solution is inherently sustainable in the long term because it is distributed: anyone can add their own GAME modules!🔥
carldeboer.bsky.social
3) GAME communicates over TCP/IP sockets, enabling evaluation of even remote/proprietary models (think ChatGPT)🌐
4) Because the Matcher is modular, we can swap it with a better version and nothing will break. 🔄
carldeboer.bsky.social
GAME has many key advantages for genomics model benchmarking:
1) Via the API, all models are inherently compatible with all benchmarks
2) GAME will work across platforms – just get Apptainer installed, and away you go 💻↔️🖥️
carldeboer.bsky.social
Our Matcher containerizes an LLM, which is the basis for matching. It worked surprisingly well almost out of the box, and can match cell/tissue types, species, and molecule types. 🔮
carldeboer.bsky.social
The “Matcher” enables pairing up of the benchmark tasks and things the model can predict. Evaluator wants predictions in heart cells? The Matcher can tell you that your cardiomyocyte model is your best bet. The Matcher and Predictors also communicate via an API!
carldeboer.bsky.social
Benchmarks are containerized as “Evaluators”. We created several, including chromatin conformation, MPRA, and synthetic cis-regulatory variant effects. As new datasets come out, they can be added to GAME to see whether models can predict them.
carldeboer.bsky.social
Models are containerized in “Predictors”. We created DREAM-RNN (K562), Enformer, Orca, DeepBICCN2, and Borzoi Predictors. We anticipate as new models are created, model builders will encapsulate their models, referencing the examples we provide.
carldeboer.bsky.social
GAME uses Application Programming Interfaces, a concept introduced to the genomics model world with Kipoi, to enable uniform application of any benchmark to any model.
Diagram of the GAME modules and how they interact.
carldeboer.bsky.social
Unlike the protein folding code, gene regulation cannot have a single benchmark (e.g. CASP) because the gene regulatory code differs across species, cell types, and conditions. We need a variety of benchmarks to understand model strengths and weaknesses: enter, GAME.