Kipoi - Seminar

The monthly virtual seminar series is designed as a platform for interested Kipoi users and developers and will host talks on the applications of deep learning on biological data. The seminar is held on every first Wednesday of the month at 5:30 p.m. - 6:30 p.m. CET. We are also happy to share the recordings of the seminar on YouTube.

How to take part

The Virtual Seminar Series takes place via Zoom. To take part in the seminar, you can register for the online Zoom conference. Your personal join link will be valid for all upcoming lectures of the series.

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How to apply as a speaker

The seminar is a great opportunity to present your recent work to a large international audience. If you want to apply as a speaker, please use the contact in the registration confirmation email.

Next seminar

Title: Nucleotide dependency analysis of DNA language models reveals genomic functional elements

5 February 2025 5:30 p.m. - 6:30 p.m. Central European Time

Speaker: Pedro Tomaz da Silva, Gagneur lab, Technical University Munich

Abstract:

Deciphering how nucleotides in genomes encode regulatory instructions and molecular machines is a long-standing goal in biology. DNA language models (LMs) implicitly capture functional elements and their organization from genomic sequences alone by modeling probabilities of each nucleotide given its sequence context. However, using DNA LMs for discovering functional genomic elements has been challenging due to the lack of interpretable methods. Here, we introduce nucleotide dependencies, which quantify how nucleotide substitutions at one genomic position affect the probabilities of nucleotides at other positions. We generated genome-wide maps of pairwise nucleotide dependencies within kilobase ranges for animal, fungal, and bacterial species. Our results demonstrate that nucleotide dependencies indicate deleteriousness of human genetic variants more effectively than alignment-based conservation and DNA LM reconstruction. Regulatory elements appear as dense blocks in dependency maps, enabling the systematic identification of transcription factor binding sites as accurately as models trained on experimental binding data. Additionally, nucleotide dependencies highlight bases in contact within RNA structures, including pseudoknots and tertiary structure contacts, with remarkable accuracy, leading to the discovery of four novel, experimentally validated RNA structures in Escherichia coli. Finally, through benchmarking and visual diagnosis using dependency maps, we reveal critical limitations of several DNA LM architectures and training sequence selection strategies. Altogether, nucleotide dependency analysis opens a new avenue for discovering and studying functional elements and their interactions in genomes.

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