Data augmentation with generative models improves detection of Non-B DNA structures.

Non-B DNA structures, or flipons, are important functional elements that regulate a large spectrum of cellular programs. Experimental technologies for flipon detection are limited to the subsets that are active at the time of an experiment and cannot capture whole-genome functional set. Thus, the task of generating reliable whole-genome annotations of non-B DNA structures is put on deep learning models, however their quality depends on the available experimental data for training.

Z-DNA formation in promoters conserved between human and mouse are associated with increased transcription reinitiation rates.

A long-standing question concerns the role of Z-DNA in transcription. Here we use a deep learning approach DeepZ that predicts Z-flipons based on DNA sequence, structural properties of nucleotides and omics data. We examined Z-flipons that are conserved between human and mouse genomes after generating whole-genome Z-flipon maps and then validated them by orthogonal approaches based on high resolution chemical mapping of Z-DNA and the transformer algorithm Z-DNABERT.

Research of Russian physicians' opinions on tuberculosis pharmacogenetics.

Background: There is currently no widespread implementation of pharmacogenetic testing (PGx) methods in the practice of phthisiology service.

Objective: The aim of this study is to determine how informed and prepared phthisiologists, residents, and postgraduate students of the Russian Medical Academy of Continuing Professional Education (RMACPE, Moscow) use PGx techniques in their work to improve treatment safety, predict the occurrence of adverse reactions (ADRs), and personalize therapy.

Methods: A survey was conducted among phthisiologists (n = 314) living in different regions of the Russian Federation and studying at RMACPE, such as residents and post-graduate students (n = 185).

Z-flipon variants reveal the many roles of Z-DNA and Z-RNA in health and disease.

Identifying roles for Z-DNA remains challenging given their dynamic nature. Here, we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA forming sequences (Z-flipons). The algorithm yields large performance enhancements (F1 = 0.