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: LegNet: parameter-efficient modeling of gene regulatory regions using modern convolutional neural network

4 December 2024 5:30 p.m. - 6:30 p.m. Central European Time

Speaker: Dmitry Penzar, autosome.org team

Abstract:

State-of-the-art genome-scale deep learning (DL) models still struggle to reliably make cell type-specific predictions of gene regulatory regions or reveal the fine-grained effects of individual genome variants, even having been trained with thousands of bulk epigenetic profiles. This is, in particular, due to the comparably low number and finite diversity of native genomic sequences. Overcoming these limitations is expected by tapping from the increasing flow of uniformly processed data from massively parallel reporter assays (MPRA). Inspired by up-to-date developments in image analysis, we developed a new fully-convolutional architecture LegNet, which can efficiently utilize the volume and diversity of the MPRA data to learn the underlying grammar of short gene regulatory regions. LegNet won 1st place in the Random Promoter Dream Challenge 2022, significantly outperforming other approaches in predicting yeast promoter activity. After that, we adapted LetNet for lentiMPRA, where we compared its performance against the SOTA human genome-trained models. Surprisingly, LegNet performed on par or better than fine-tuned Enformer and Sei, despite having 250-fold fewer parameters, thus significantly reducing the computational costs for training and running DL models of regulatory regions. Another adaptation of LegNet (SELEX-LegNet) allows its applications to even shorter sequences, such as results of the high-throughput SELEX assays of the transcription factor binding specificity. Further, we developed RNA-LegNet for MPRAs evaluating the 5' and 3' UTRs effects on the RNA stability and translation efficiency. Last but not least, we have implemented a cold diffusion generative model that produces sequences with desired properties, starting with yeast promoters of desired activity. Next, we repurposed the model to generate UTRs with cell-specific activity, highlighting LegNet's potential in the development of improved RNA therapeutics. All in all, we consider LegNet to be among the SOTA models for short regulatory sequences, and to provide a solid baseline for further application of deep neural networks to decipher the logic of eukaryotic gene regulation on both transcriptional and post-transcriptional levels.

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