Biology subjects

Ilina, E.

Publications and source records attributed to Ilina, E..

4 recordsLinked to original sources

bioRxiv2026

Direct empirical in-house assessment of peptide proteotypicity for targeted proteomics

In bottom-up proteomics peptide it was early shown that despite a certain protein is present in a sample, only a subset of its proteolytic peptide products will be detected with LC-MS analysis. Property of peptide being frequently detected given its source proteins identification was called proteotypicity. Much effort has been since applied to predict proteotypic peptides and summarize evidence on peptide detection. Nevertheless, when targeted proteomics method is being developed, prediction or inference from communal experience might be inaccurate and prior knowledge of true peptide proteotypicity in a selected setup for a selected population is necessary. In this work we test fully in-house approach for proteotypicity assessment including comprehensive peptide synthesis and detection verification. Proteotypicity and contribution of sample processing and biology-related factors are estimated in a model experiment for three plasma proteins, albumin, ceruloplasmin and C-reactive protein.

genomics↗

bioRxiv2024

AliMarko: A Novel Tool for Eukaryotic Virus Identification Using Expert-Guided Approach

Metagenomic sequencing is a valuable tool for studying viral diversity in biological samples. Analyzing this data is complex due to the high variability of viral genomes and their low representation in databases. We present the Alimarko pipeline, designed to streamline virus identification in metagenomic data. A key feature of our tool is the focus on the interpretability of findings: results are provided with tabular and visual information to help determine the confidence level in the identified viral sequences. The pipeline employs two approaches for identifying viral sequences: mapping to reference genomes and de novo assembly followed by the application of Hidden Markov Models (HMM). Additionally, it includes a step for phylogenetic analysis, which constructs a phylogenetic tree to determine the evolutionary relationships with reference sequences. We also emphasize reducing false-positive results. Reads related to cellular organisms are computationally depleted, and the identified viral sequences are checked against a list of potential contaminants. The output is an HTML document containing visualizations and tabular information designed to assist researchers in making informed decisions about the presence of viruses. Using our pipeline for total RNA sequencing of bat feces, we identified a range of viruses and rapidly determined the validity and phylogenetic relationships of the findings to known sequences with the aid of reports generated by AliMarko.

bioinformatics↗

bioRxiv2019

Genome Complexity Browser: estimation and visualization of prokaryote genome variability

MotivationComparative genomics studies may be used to acquire new knowledge about chromosomal architecture - the rules to combine a set of genes in a genome of a living organism. Hundreds of thousands of prokaryote genomes were sequenced and assembled. Still, there is a lack of computational tools able to compare hundreds of genomes simultaneously, i.e. to find hotspots of genome rearrangements and horizontal gene transfer or to analyze which part of an operon is conservative and which is variable.\n\nResultsWe developed Genomic Complexity Browser (GCB), a tool that allows to visualize gene contexts in a graph form and evaluate genome variability of different parts of a prokaryotic chromosome. We introduce a measure called complexity, which is an indirect measure of genome variability. Intraspecies and interspecies comparisons reveal that regions with high complexity tend to be located in a similar context in different strains and species. While many of such hot spots are associated with prophages and pathogenicity islands, some of them lack these determinants and mechanisms that govern their dynamics are to be elucidated.\n\nAvailabilityGCB is freely available as a web server at http://gcb.rcpcm.org and as a stand-alone application at https://github.com/DNKonanov/GCB\n\nContactparaslonic@gmail.com

bioinformatics↗

bioRxiv2018

VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota

The resistance of bacterial pathogens to antibiotics is one of the most important issues of the modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microflora by means of horizontal gene transfer (HGT). Thus, at the moment it is important to develop methods of prediction and monitoring of antibiotics resistance in human population. We represent the agent-based VERA model, which allows to simulate the spread of pathogen with account of possible horizontal transfer of resistance determinants from commensal microbiota community. The model considers the opportunity of residents to stay in the town or in medical institution, have wrong self-treatment, reception of several antibiotics types, transfer and accumulation of resistance determinants from a microbiota to a pathogen. In this model we have also created assessment of optimum intensity of observation of infection spread among the population. Investigating model behavior we show a number of nonlinear dependencies, including the exponential nature of dependence of total of the diseased on average resistance of a pathogen. As the model infection we considered infection with Shigella spp, though it could be applied to a wide range of other pathogens.\n\nAvailability and implementationSource code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform.

bioinformatics↗