Biology subjects

Meshcheryakov, G.

Publications and source records attributed to Meshcheryakov, G..

5 recordsLinked to original sources

bioRxiv2024

Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors

Gene expression is regulated by transcription factors (TFs), which recognize specific DNA sequence motifs. Several hundred putative human TFs, identified mainly by an apparent DNA-binding domain, lack known binding motifs1, and even for well-characterized TFs, it remains controversial to what degree motifs accurately reflect binding sites in living cells2,3. Here, we describe a systematic effort ("Codebook") to determine the sequence specificity of 332 putative and poorly characterized human TFs. Over 4,000 independent experiments, encompassing multiple in vitro and in vivo assays, produced motifs for just over half (177, or 53%), of which most are unique to a single protein, thereby extending the vocabulary of sequence recognition encoded by human TFs by [~]100 distinct motifs. Moreover, binding motifs identified in vitro are strongly enriched within cellular binding sites. Collectively, the data reveal tens of thousands of previously unknown, conserved, and direct TF binding sites across the human genome. These sites are concentrated in promoter regions, and are predictive of gene expression, illustrating that this new data atlas provides an important step forward in decoding the human genome.

genomics↗

bioRxiv2023

Statistical framework for calling allelic imbalance in high-throughput sequencing data

High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene expression. Compared to classic association studies, allelic imbalance at heterozygous variants captures the functional effects of the regulatory genome variation with smaller sample sizes and higher sensitivity. Yet, the identification of allele-specific events from allelic read counts remains non-trivial due to multiple sources of technical and biological variability, which induce data-dependent biases and overdispersion. Here we present MIXALIME, a novel computational framework for calling allele-specific events in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy-number variation. We benchmark MIXALIME against existing tools and demonstrate its practical usage by constructing an atlas of allele-specific chromatin accessibility, UDACHA, from thousands of available datasets obtained from diverse cell types. Availabilityhttps://github.com/autosome-ru/MixALime, https://udacha.autosome.org

bioinformatics↗

bioRxiv2023

Evaluation and optimization of sequence-based gene regulatory deep learning models

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

genomics↗

bioRxiv2022

LegNet: resetting the bar in deep learning for accurate prediction of promoter activity and variant effects from massive parallel reporter assays

MotivationThe increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep learning approaches for DNA regulatory grammar. ResultsHere we introduce LegNet, an EfficientNetV2-inspired convolutional network for modeling short gene regulatory regions. By approaching the sequence-to-expression regression problem as a soft classification task, LegNet secured first place for the autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. Using published data, here we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of single-nucleotide variants. Furthermore, we show how LegNet can be used in a diffusion network manner for the rational design of promoter sequences yielding the desired expression level. Availability and Implementationhttps://github.com/autosome-ru/LegNet. The GitHub repository includes the Python code under the MIT license to reproduce the results presented in the study and a Jupyter Notebook tutorial. Supplementary InformationOnline-only supplementary data are available at Bioinformatics online. Contactdmitrypenzar1996@gmail.com, ivan.kulakovskiy@gmail.com

bioinformatics↗

bioRxiv2021

The role of AUX1 during lateral root development in the domestication of the model C4 grass Setaria italica

C4 photosynthesis increases the efficiency of carbon fixation by spatially separating high concentrations of molecular oxygen from rubisco. The specialized leaf anatomy required for this separation evolved independently many times. C4 root systems are highly branched, an adaptation thought to support high rates of photosynthesis; however, little is known about the molecular mechanisms that have driven the evolution of C4 root system architecture (RSA). Using a mutant screen in the C4 model plant Setaria italica, we identify siaux1-1 and siaux1-2 as RSA mutants, and use CRISPR/cas9-mediated genome editing and overexpression to confirm the importance of the locus. As AUX1 is not necessary for lateral root emergence in S. viridis, the species from which S. italica was domesticated, we conducted an analysis of auxin responsive elements in the promoters of auxin-responsive gene families in S. italica, and explore the molecular basis of SiAUX1s role in seedling development using an RNAseq analysis of wild type and siaux1-1 plants. Finally, we use a root coordinate system to compare cell-by-cell meristem structures in siaux1-1 and wild type Setaria plants, observing changes in the distribution of cell volumes in all cell layers and a dependence in the frequency of protophloem and protoxylem strands on siAUX1.

plant biology↗