Biology subjects

Petrov, M.

Publications and source records attributed to Petrov, M..

3 recordsLinked to original sources

bioRxiv2024

GENA-Web - GENomic Annotations Web Inference using DNA language models

The advent of advanced sequencing technologies has significantly reduced the cost and increased the feasibility of assembling high-quality genomes. Yet, the annotation of genomic elements remains a complex challenge. Even for species with comprehensively annotated reference genomes, the functional assessment of individual genetic variants is not straightforward. In response to these challenges, recent breakthroughs in machine learning have led to the development of DNA language models. These transformer-based architectures are designed to tackle a wide array of genomic tasks with enhanced efficiency and accuracy. In this context, we introduce GENA-Web, a web-based platform that consolidates a suite of genome annotation tools powered by DNA language models. The version of GENA-Web presented here encompasses a diverse set of models trained on human data, including the prediction of promoter activity, annotation of splice sites, determination of various chromatin features, and a model for scoring of enhancer activity in Drosophila. GENA-Web is accessible online at https://dnalm.airi.net/

bioinformatics↗

bioRxiv2023

GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences

Recent advancements in genomics, propelled by artificial intelligence, have unlocked unprecedented capabilities in interpreting genomic sequences, mitigating the need for exhaustive experimental analysis of complex, intertwined molecular processes inherent in DNA function. A significant challenge, however, resides in accurately decoding genomic sequences, which inherently involves comprehending rich contextual information dispersed across thousands of nucleotides. To address this need, we introduce GENA-LM, a suite of transformer-based foundational DNA language models capable of handling input lengths up to 36,000 base pairs. Notably, integrating the newly-developed Recurrent Memory mechanism allows these models to process even larger DNA segments. We provide pre-trained versions of GENA-LM, including multispecies and taxon-specific models, demonstrating their capability for fine-tuning and addressing a spectrum of complex biological tasks with modest computational demands. While language models have already achieved significant breakthroughs in protein biology, GENA-LM showcases a similarly promising potential for reshaping the landscape of genomics and multi-omics data analysis. All models are publicly available on GitHub https://github.com/AIRI-Institute/GENA_LM and HuggingFace https://huggingface.co/AIRI-Institute. In addition, we provide a web-service https://dnalm.airi.net/ allowing user-friendly DNA annotation with GENA-LM models.

bioinformatics↗

bioRxiv2020

Single cell reconstruction of human basal cell diversity in normal and IPF lung.

RationaleDeclining lung function in patients with interstitial lung disease is accompanied by epithelial remodeling and progressive scarring of the gas-exchange region. There is a need to better understand the contribution of basal cell hyperplasia and associated mucosecretory dysfunction to the development of idiopathic pulmonary fibrosis (IPF). ObjectivesWe sought to decipher the transcriptome of freshly isolated epithelial cells from normal and IPF lung to discern disease-dependent changes within basal stem cells. MethodsSingle cell RNA sequencing was used to map epithelial cell types of the normal and IPF human airway. Organoid and ALI cultures were used to investigate functional properties of basal cell subtypes. Measurements and Main ResultsWe found that basal cells included multipotent and secretory primed subsets in control adult lung tissue. Secretory primed basal cells include an overlapping molecular signature with basal cells obtained from distal lung tissue of IPF lungs. We confirmed that NOTCH2 maintains undifferentiated basal cells and restrict basal-to-ciliated differentiation, and present evidence that NOTCH3 functions to restrain secretory differentiation. ConclusionsBasal cells are dynamically regulated in disease and are specifically biased towards expansion of the secretory primed basal cell subset in idiopathic pulmonary fibrosis. Modulation of basal cell plasticity may represent a relevant target for therapeutic intervention in IPF.

cell biology↗