Egor Guguchkin

DeepVariant has revolutionized the field of genetic variant identification by reframing variant detection as an image classification problem. However, despite its wide adoption in bioinformatics workflows, the tool continues to evolve mainly through the expansion of training datasets, while its core neural network architecture—Inception V3—has remained unchanged. In this study, we revisited the DeepVariant design and presented a prototype of a modernized version that supports alternative neural network backbones. As a proof of concept, we replaced the legacy Inception V3 model with a mid-sized EfficientNet model and evaluated its performance using the benchmark dataset from the Genome in a Bottle (GIAB) project. Alternative architecture demonstrated faster convergence, a twofold reduction in the number of parameters, and improved accuracy in variant identification. On the test dataset, updated workflow achieved consistent improvements of +0.1% in SNP F1-score, enabling the detection of up to several hundred additional true variants per genome. These results show that optimizing the neural architecture alone can enhance the accuracy, robustness, and efficiency of variant calling, thereby improving the overall quality of sequencing data analysis.

Relevance. Modern research in the field of biomedicine leads to the development of therapeutic drugs based on extracellular vesicles, which are defined as sources of production, as well as targeted modification. In the presented work, for the first time, transcriptome profiling of primary culture of cortical neurons under the influence of extracellular vesicles obtained from glial cells during glutamate excitotoxicity was carried out in order to determine differentially expressed genes. Materials and Methods. Extracellular vesicles were obtained from the conditioned medium of human glial progenitor cells using ultracentrifugation. Model of glutamate excitotoxicity, distributed on the first cultured cortical neurons of cells (P0) with the addition of 100 μM glutamate. Sequencing of prepared libraries of electronic technologies on the NextSeq 1000 platform (Illumina, USA) using the NextSeq 1000/2000 P2 (200 cycles) v3 reagent kit supplemented with 2 % Phix (Illumina) as an internal control. The criterion for statistical innovation of gene expression change between officially recognized FDR 0.05. Results and Discussion. Transcriptome analysis showed that the addition of extracellular vesicles during glutamate excitotoxicity leads to increased expression of 190 genes and decreased expression of 309 genes (p value 0.05 and |FC| 1.5). Gene analysis using the Gene Onthology database showed that genes with increased expression are consistently classified by biological processes. The most represented were: regeneration, reorganization of the extracellular matrix and cytoskeleton, maintenance of homeostasis, activation of the PI3K-Akt pathway and response to cellular stress. Genes with reduced expression were consistently classified into groups: calcium transport, regulation of neuronal processes, apoptosis, glutathergic synapse. These data can indicate that. Extracellular vesicles trigger survival processes in nerve cells when exposed to glutamate and inhibit pathways associated with the entry of substances and glutamate into the cell. Conclusions. Extracellular vesicles enhance the expression of genes with survival and inhibit genes, resulting in calcium transport and apoptosis. The results of the study show the promise of using extracellular vesicles of glial origin as a basis for developing new therapeutic approaches to individual neurological diseases.

Neurodegenerative diseases pose a significant challenge to modern medicine. Despite significant advances in neurology, current therapeutic approaches often prove insufficient to treat such disorders. This study investigates the neuroprotective effect of extracellular vesicles derived from glial derivates of human-induced pluripotent stem cells. The extracellular vesicle’s cargo was characterised by proteomic analysis. The neuroprotective effect was assessed using a model of glutamate excitotoxicity performed on a primary culture of cortical neuroglial cells. The viability of cells was estimated using the MTT test and morphometric analyses. A comprehensive methodology was applied to investigate intracellular mechanisms, integrating assessments of intracellular calcium concentrations, mitochondrial membrane potential, and targeted inhibition of the PI3K-Akt pathway. Transcriptomic analysis of neuroglial cultures was used to validate the role of obtained mechanisms of extracellular vesicle’s neuroprotective effect. The obtaining results demonstrated the improvement of neuronal survival by reducing intracellular calcium levels and stabilising mitochondrial membrane potential under glutamate-induced excitotoxicity via PI3K-Akt signalling pathway activation. Moreover, the vesicles contained proteins that contribute to preventing apoptotic processes, activating regeneration of the nervous system, and modulating calcium ion transport and are associated with redox processes. Further transcriptomic analyses of neuroglial cultures treated with EVs showed an up-regulation of genes associated with regeneration, inhibition of calcium ion transport, regulation of membrane depolarisation, and negative regulation of apoptotic pathways.

Neurodegenerative diseases pose a significant challenge to modern medicine. Despite significant advances in neurology, current therapeutic approaches often prove insufficient to treat such disorders. This study investigates the neuroprotective effect of extracellular vesicles derived from glial derivates of human induced pluripotent stem cells. The extra-cellular vesicle’s cargo was characterised by proteomic analysis. They neuroprotective ef-fect was assessed using a model of glutamate excitotoxicity performed on primary culture of cortical neurons. The viability of cortical neurons was estimated using the MTT test and morphometric analyses. A comprehensive methodology was applied to investigate intra-cellular mechanisms, integrating assessments of intracellular calcium concentrations, mitochondrial membrane potential, and targeted inhibition of the PI3K‑Akt pathway. Transcriptomic analysis of cortical neurons was used to validated the role of obtained mechanisms of extracellular vesicle’s neuroprotective effect. The obtaining results demon-strated the improvement of neuronal survival by reducing intracellular calcium levels, and stabilizing mitochondrial membrane potential under glutamate-induced excitotoxi-city via PI3K-Akt signaling pathway activation. Moreover, the vesicles contained proteins that contribute to preventing apoptotic processes, activating regeneration of nervous sys-tem, modulating of calcium ion transport and associated with redox processes. Further transcriptomic analyses of neuroglial cultures treated with EVs showed up-regulation of genes associated with regeneration, inhibition of calcium ion transport, regulation of membrane depolirisation and negative regulation of apoptotic pathways.

Background. Whole-genome sequencing (WGS) enables comprehensive detection of genetic variants but faces limitations in benchmarking due to incomplete reference datasets. Trio-based analysis, leveraging Mendelian inheritance, provides an alternative strategy for validating sequencing results and estimating error rates, particularly in regulatory genomic regions. Methods. We extended the nf-core/sarek WGS pipeline by integrating a module that collects parental and offspring allele information, extracts regulatory genomic regions, and computes Mendelian-consistency scores. The algorithm processes variant calls from parents and children to identify expected versus anomalous inheritance patterns. The module was implemented in C++ and integrated into the Nextflow workflow, supporting automated analysis of trio datasets. Results. The method was validated on two real trio datasets, comparing DeepVariant and HaplotypeCaller as variant callers. For both trios, DeepVariant consistently achieved higher sensitivity and precision, with statistically significant differences confirmed using 95% confidence intervals. These results demonstrate that the proposed approach enables effective benchmarking of variant-calling performance in non-benchmark datasets. Conclusions. The developed method provides a practical and scalable framework for quantifying WGS outcomes from trio data. By incorporating Mendelian-inheritance validation into existing pipelines, researchers can estimate sequencing error rates, compare variant callers, and optimize workflows in regulatory genomic regions. Our findings confirm the superior performance of DeepVariant over HaplotypeCaller for the studied datasets.

Background: In clinical practice, various methods are used to identify ALK gene rearrangements in tumor samples, ranging from “classic” techniques, such as IHC, FISH, and RT-qPCR, to more advanced highly multiplexed approaches, such as NanoString technology and NGS panels. Each of these methods has its own advantages and disadvantages, but they share the drawback of detecting only a restricted (although sometimes quite extensive) set of preselected biomarkers. At the same time, whole transcriptome sequencing (WTS, RNAseq) can, in principle, be used to detect gene fusions while simultaneously analyzing an incomparably wide range of tumor characteristics. However, WTS is not widely used in practice due to purely analytical limitations and the high complexity of bioinformatic analysis, which requires considerable expertise. In particular, methods to detect gene fusions in RNAseq data rely on the identification of chimeric reads. However, the typically low number of true fusion reads in RNAseq limits its sensitivity. In a previous study, we observed asymmetry in the RNAseq exon coverage of the 3′ partners of some fusion transcripts. In this study, we conducted a comprehensive evaluation of the accuracy of ALK fusion detection through an analysis of differences in the coverage of its tyrosine kinase exons. Methods: A total of 906 human cancer biosamples were subjected to analysis using experimental RNAseq data, with the objective of determining the extent of asymmetry in ALK coverage. A total of 50 samples were analyzed, comprising 13 samples with predicted ALK fusions and 37 samples without predicted ALK fusions. These samples were assessed by targeted sequencing with two NGS panels that were specifically designed to detect fusion transcripts (the TruSight RNA Fusion Panel and the OncoFu Elite panel). Results: ALK fusions were confirmed in 11 out of the 13 predicted cases, with an overall accuracy of 96% (sensitivity 100%, specificity 94.9%). Two discordant cases exhibited low ALK coverage depth, which could be addressed algorithmically to enhance the accuracy of the results. It was also important to consider read strand specificity due to the presence of antisense transcripts involving parts of ALK. In a limited patient sample undergoing ALK-targeted therapy, the algorithm successfully predicted treatment efficacy. Conclusions: RNAseq exon coverage analysis can effectively detect ALK rearrangements.

Motivation: Alignment of reads to a reference genome sequence is one of the key steps in the analysis of human whole-genome sequencing data obtained through Next-generation sequencing (NGS) technologies. The quality of the subsequent steps of the analysis, such as the results of clinical interpretation of genetic variants or the results of a genome-wide association study, depends on the correct identification of the position of the read as a result of its alignment. The amount of human NGS whole-genome sequencing data is constantly growing. There are a number of human genome sequencing projects worldwide that have resulted in the creation of large-scale databases of genetic variants of sequenced human genomes. Such information about known genetic variants can be used to improve the quality of alignment at the read alignment stage when analysing sequencing data obtained for a new individual, for example, by creating a genomic graph. While existing methods for aligning reads to a linear reference genome have high alignment speed, methods for aligning reads to a genomic graph have greater accuracy in variable regions of the genome. The development of a read alignment method that takes into account known genetic variants in the linear reference sequence index allows combining the advantages of both sets of methods. Results: In this paper, we present the minimap2_index_modifier tool, which enables the construction of a modified index of a reference genome using known single nucleotide variants and insertions/deletions (indels) specific to a given human population. The use of the modified minimap2 index improves variant calling quality without modifying the bioinformatics pipeline and without significant additional computational overhead. Using the PrecisionFDA Truth Challenge V2 benchmark data (for HG002 short-read data aligned to the GRCh38 linear reference (GCA_000001405.15) with parameters k = 27 and w = 14) it was demonstrated that the number of false negative genetic variants decreased by more than 9500, and the number of false positives decreased by more than 7000 when modifying the index with genetic variants from the Human Pangenome Reference Consortium.

Motivation Alignment of reads to a reference genome sequence is one of the key steps in the analysis of human NGS whole-genome sequencing data. The quality of the subsequent steps of the analysis, such as the results of clinical interpretation of genetic variants or the results of a genome-wide association study (GWAS), depends on the correct identification of the position of the read as a result of its alignment. The amount of human NGS whole-genome sequencing data is constantly growing. There are a number of human genome sequencing projects worldwide that have resulted in the creation of large-scale databases of genetic variants of sequenced human genomes. Such information about known genetic variants can be used to improve the quality of alignment at the read alignment stage when analysing sequencing data obtained for a new individual, for example, by creating a genomic graph. While existing methods for aligning reads to a linear reference genome have high alignment speed, methods for aligning reads to a genomic graph have greater accuracy in variable regions of the genome. The development of a read alignment method that takes into account known genetic variants in the linear reference sequence index allows combining the advantages of both sets of methods. Results In this paper we present the minimap2_index_modifier tool, which allows the construction of a modified index of a reference genome using known SNVs and indels of a given human population. The use of the modified minimap2 index improves variant calling quality without modifying the bioinformatics pipeline and without significant additional computational overhead. Using the FDA Precision Truth Challenge V2 benchmark data (for HG002 short-read data aligned to the GRCh38 linear reference (GCA_000001405.15) with parameters k = 27 and w = 14) it was demonstrated that the number of false negative genetic variants decreased by more than 2000, and the number of false positives decreased by more than 200.

This study emphasizes the importance of aligning short reads in the analysis of human whole-genome sequencing data. The alignment process involves determining the positions of short genetic sequences relative to a known reference genome sequence of the human genome. Traditional alignment methods use a linear reference sequence, but this can lead to incorrect alignment, especially when short reads contain genetic variations. In this work, the index file of the reference sequence was modified using the minimap2 tool. Experimental results showed that adding information about frequently occurring genetic variations to the minimap2 index increases the number of correctly identified genetic variants, which affects the quality of subsequent data analysis.