Jacob Schreiber

The @impvienna.bsky.social is a unique place where talented researchers are doing amazing science. I'm glad I had an opportunity to spend some time there, and am looking forward to the next excuse to visit Vienna!

That sounds too biologically important for me to be involved.

My first @umasschan.bsky.social/@impvienna.bsky.social affiliated paper is up!

tomtom-lite is a re-implementation of tomtom targeting the ML age of genomics. Fast annotations ("what is this motif?") and simple large-scale discovery of motifs.

Check it out!

academic.oup.com/bioinformati...

Why is it a problem to translate C++ to changng standards when you can just use Agenic AI?

Tune in for a great MIA talk on ML for regulatory genomics by @jmschreiber91.bsky.social and Gregory Andrews now! 🧪
broad.io/mia

(Will also be available online on our YouTube playlist later: www.youtube.com/watch?v=sMTO...)

gReLU: a comprehensive framework for DNA sequence modeling and design - Nature Methods gReLU advances deep-learning-based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications.

I'm happy to share that our gReLU package is now published in Nature Methods!

www.nature.com/articles/s41...

they told the flight attendants to sit down for the second half of the flight (an hour) because it was entirely turbulence

had to land in a nor'eastern storm in boston and i'm ready to move back to the west coast forever

Now that I'm settled in at @umasschan.bsky.social, I'm hiring at all levels: grad students, post-docs, and software engineers/bioinformaticians!

The goal of my lab is to understand the regulatory role of every nucleotide in our genomes and how this changes across every cell in our bodies.

I figure I can spend the time until I get tenure on answering the question, and then the time after tenure arguing about what "regulatory" means

If you're interested, please reach out with your CV and which topics you'd be interested in working on!

- Genomics Software Ecosystem: A major obstacle to our goal is the lack of simple+scalable software that everyone can use. Come build this with me. Training a lightweight deep learning model and using it for design/interpretability/VE prediction should be no more challenging than mapping reads.

- Foundation Models: As someone involved in ML, I am legally required to be working on this topic.

Programmatic design and editing of cis-regulatory elements The development of modern genome editing tools has enabled researchers to make such edits with high precision but has left unsolved the problem of designing these edits. As a solution, we propose Ledi...

We have an array of ML-based projects for going after this, focusing on the following topics:

- DNA Design ( 🧬 ) We have shown that Ledidi (www.biorxiv.org/content/10.1...) can precisely design DNA, and now it's time to push the boundaries in several directions w/ some very cool collaborations.

Now that I'm settled in at @umasschan.bsky.social, I'm hiring at all levels: grad students, post-docs, and software engineers/bioinformaticians!

The goal of my lab is to understand the regulatory role of every nucleotide in our genomes and how this changes across every cell in our bodies.

It was suggested that the audience may not appreciate/understand :(

is it a good idea to wear a "join, or die!" hat to a big talk in europe? please say yes

the greatest productivity hack is having a grant deadline. there's so much other stuff you can do when you're supposed to be working on a grant.

I was delighted to have the unexpected opportunity to give a keynote at MLCB 2025 in NYC last week. I used it to explain how I view deep learning models in genomics not as "uninterpretable black boxes" but as indispensable tools for understanding genomics + designing the next gen of synthetic DNA.

for some reason i thought being a professor would involve more mentoring and research and less filling out disclosures concerning whether plants and seeds were used in my computational study

stocking up the new apartment with essentials

In the genomics community, we have focused pretty heavily on achieving state-of-the-art predictive performance.

While undoubtedly important, how we *use* these models after training is potentially even more important.

tangermeme v1.0.0 is out now. Hope you find it useful!

For some reason, hitting "comment" on GitHub is significantly more responsive than a month ago and it freaks me out. Surely there are some important calculations that need to be done before letting my thoughts into the wild?

Thanks! Let me know if you want me to stop in virtually, we can try to figure out a time.

Hope you find tangermeme helpful in your work! Please reach out if you have any comments + questions.

Because everything is automatic, we can probe models.

What motifs are driving model predictions? Calculate attributions, call + annotate seqlets, and count the annotations!

BPNet is relying on MYC, whereas Beluga is relying on many more TFs. Easy comparison now.

Frequently, people manually annotate seqlets and draw bars or boxes around these high-attribution characters themselves. This is not really a problem, but it's just slow and does not scale genome-wide.

In the above picture, everything is automatically done.

People *talk* about seqlets a lot but tangermeme is the first package for complete functionality.

Here is a complete example of using tangermeme for attributions, seqlet calling + annotation, and plotting, to visualize what five models think of the same locus

Expanding past these implementations, tangermeme has a large focus on automatic seqlet calling and usage. Seqlets are short contiguous spans of high-attribution characters that usually correspond to the binding of a TF.

By considering attributions you can see how variants disrupt or change usage of motifs. Maybe you'll even find that a variant causes alternative binding by inducing a new motif or slightly changing competition! That would be challenging to see from the predictions alone.