Niklas Kempynck

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...

One thousand candidate enhancers tested in vivo in the mouse brain! A massive resource and oh so useful as validation set for genome-wide enhancer prediction methods. Super fun to be involved in one of the papers: ‘the prediction challenge paper’ by Nelson&Niklas et al www.cell.com/cell-genomic...

Make sure to also check out the other studies part of the larger effort on identifying and validating enhancer tools.

This study was done together with Nelson Johansen and supervised by Trygve Bakken at the @alleninstitute.org. Thanks to all co-authors for the great inter-lab collaboration! Also a personal shoutout to the members in @steinaerts.bsky.social lab for a nice team effort and to Stein for guidance.

Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex Johansen et al. report the results of a community challenge to predict functional enhancers targeting specific brain cell types. By comparing multi-omics machine learning approaches using in vivo data...

Check out our work on evaluating methods for predicting in vivo cell enhancer activity in the mouse cortex! Combined, scATAC peak specificity and sequence-based CREsted predictions gave the best predictive performance, aiming to advance genetic tool design for cell targeting in the brain.

Intelligence Evolved at Least Twice in Vertebrate Animals | Quanta Magazine Complex neural circuits likely arose independently in birds and mammals, suggesting that vertebrates evolved intelligence multiple times.

Calling someone bird-brained is, in fact, a way of calling someone highly intelligent. @yaseminsaplakoglu.bsky.social reports: www.quantamagazine.org/intelligence...

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at decipher...

Very proud of two new preprints from the lab:
1) CREsted: to train sequence-to-function deep learning models on scATAC-seq atlases, and use them to decipher enhancer logic and design synthetic enhancers. This has been a wonderful lab-wide collaborative effort. www.biorxiv.org/content/10.1...

Also check out Hannah’s thread on our latest preprint on HyDrop v2, an open-source platform for scATAC-sequencing, and a great, cost-efficient way of generating data for S2F models. 🙌

CREsted is available at github.com/aertslab/CRE.... Analysis notebooks can be found at github.com/aertslab/CRE.... All models developed for this preprint and in previous work are available in CREsted through crested.get_model(). We look forward to your feedback!

This was a big collaborative effort, together with @seppedewinter.bsky.social , and with great contributions from @casblaauw.bsky.social , Vasilis and many others. A special shoutout to @lukasmahieu.bsky.social who professionalized the package, and to @steinaerts.bsky.social for supervising.

Finally, we train a model on a full-development zebrafish scATAC-seq atlas, and use it to design and in vivo validate cell type- and timepoint-specific enhancers with a high success rate. We also attempt to modulate reporter strength over two cell types.

In a new functionality to CREsted, we explore Borzoi fine-tuning to mouse motor cortex scATAC-seq data. We show that fine-tuned models and smaller models from scratch have a near-identical performance.

We also study enhancer code inside human cancer cell lines and glioma biopsies and find that enhancer codes between Mesenchymal-like glioblastoma and melanoma states are more similar compared to glioblastoma biopsy data.

Next, we validated CREsted-identified motif instances from a human PBMC model with ChIP-seq data. We further show that gene locus predictions can be used to simulate the effect of TF degradation on chromatin accessibility.

We use the mouse cortex model to highlight CREsted’s gene locus prediction capabilities, both in unseen chromosomes and across species. This presents a powerful tool for potentially annotating genomes across species at high resolution.

We first demonstrate CREsted’s functionality by providing a complete data-driven analysis of mouse motor cortex enhancer codes across cell types. Through matched scRNA-seq data, we link motifs to likely TF candidates.

CREsted starts from the outputs of established scATAC preprocessing pipelines, and trains sequence-to-function models on chromatin accessibility per cell type. It provides complete motif analysis tools to infer cell type-specific enhancer codes and holds a comprehensive
enhancer design toolbox.

We released our preprint on the CREsted package. CREsted allows for complete modeling of cell type-specific enhancer codes from scATAC-seq data. We demonstrate CREsted’s robust functionality in various species and tissues, and in vivo validate our findings: www.biorxiv.org/content/10.1...

Very excited to share our new preprint together with @daniedaaboul.bsky.social, where we studied the gene regulatory code that hippocampal granule cells (GCs) use during synapse formation (1/n)

How does gene regulation shape brain evolution? Our new preprint dives into this question in the context of mammalian cerebellum development! rb.gy/dbcxjz
Led by @ioansarr.bsky.social, @marisepp.bsky.social and @tyamadat.bsky.social, in collaboration with @steinaerts.bsky.social

The latest Discover ASAP episode dives into "Cell Type Directed Design of Synthetic Enhancers," a study published in Nature by CRN Team Voet. They discuss how machine learning enables precise enhancer design for targeted gene expression 🧬

Watch: www.youtube.com/watch?v=Qcms...

Modelling and design of transcriptional enhancers - Nature Reviews Bioengineering Enhancers are genomic elements critical for regulating gene expression. In this Review, the authors discuss how sequence-to-function models can be used to unravel the rules underlying enhancer activit...

We wrote a review article on modelling and design of transcriptional enhancers using sequence-to-function models.

From conventional machine learning methods to CNNs and using models as oracles/generative AI for synthetic enhancer design!

@natrevbioeng.bsky.social

www.nature.com/articles/s44...

This has been a fantastic adventure - to capture the genomic regulatory code underlying brain cell types (using deep learning models trained on chromatin accessibility), and then use these models to compare cell types between the bird and mammalian brain

Constrained roads to complex brains Neural development and brain circuit evolution converged in birds and mammals

Also, check out the two related articles from the @kaessmannlab.bsky.social and García-Moreno groups, and the expert perspective by @giacomogattoni.bsky.social and Maria Antonietta Tosches www.science.org/doi/10.1126/...!

Just very happy to have our paper out today! A big thanks to all our co-authors, and to Nikolai and @steinaerts.bsky.social for the teamwork over the past years. If you are interested in using our models for cross-species enhancer studies, check out crested.readthedocs.io/en/stable/mo... 🙂