Biology subjects

Bogatyreva, N. S.

Publications and source records attributed to Bogatyreva, N. S..

4 recordsLinked to original sources

bioRxiv2025

Solving the gene classification problem with a novel Gene Space approach

Genomic organization and its comparative analysis throughout all major kingdoms of life are extensively studied across multiple scales, ranging from individual gene-level analyses to system-wide investigations. This work introduces a novel framework for characterizing genetic architecture through a new integral genomic parameter. We propose the concept of a multidimensional Gene Space to enable holistic quantification of genome organization principles. Gene Space -- a multidimensional space based on the frequencies of nucleotide tokens, such as individual nucleotides, codons, or codon pairs. We demonstrate that in this space, genes from each of the studied microorganism species occupy a limited region, and individual genes from different species can be effectively separated. Consequently, a specific Genome Subspace can be defined for each species, which constrains the organisms evolutionary pathways, thereby determining the constraints on gene optimization for these species. Further in-depth analysis is required to test if it is true for other organisms as well. The Gene Space framework offers a novel and powerful approach for genome analysis at the most basic levels, with promising applications in comparative genomics, evolutionary biology, and gene optimization.

bioinformatics↗

bioRxiv2024

Exploring individual codon influence on protein expression: a predictive approach

An important role of a particular synonymous codon composition of a gene on its expression level is well-known. There are a number of algorithms optimising codon usage of recombinant genes to maximise their expression in host cells. Nevertheless, the problem has not been solved yet and remains relevant. In the realm of modern biotechnology, directing protein production to a specific level is crucial for metabolic engineering, genome rewriting, and a growing number of other applications. In this study, we propose two new simple statistical and empirical methods for predicting the protein expression level from the nucleotide sequence of the corresponding gene: Codon Expression Index Score (CEIS) and Codon Productivity Score (CPS). Both of these methods are based on the influence of each individual codon in the gene on the overall expression level of the encoded protein and the frequencies of isoacceptors in the species. Our predictions achieve a correlation with experimentally measured quantitative proteome data of Escherichia coli up to a level of r=0.7, which is superior to any previously proposed methods. Our work helps to understand how codons determine translation rates; based on our methods, it is possible to design proteins optimised for expression in a particular organism.

bioinformatics↗

bioRxiv2023

Where to stop psi-BLAST iterations so that the found sequences remain related to the query sequence?

The BLAST program [Altschul et al., 1990, J. Mol. Biol., 215:403-410] stands as a widely used tool for the search of the most similar sequences, while the iterative {Psi}-BLAST program [Altschul et al., 1997, Nucl. Acids Res., 25:3389-3402] offers a high sensitivity for detecting remote homologs of the query sequence through an iterative usage of the BLAST search. However, the number of iterations that have to be used by the {Psi}-BLAST is rather poorly justified in the literature. Our study shows that, as the number of iterations increases, {Psi}-BLAST rapidly loses the ability to be guided by the query sequence in the search for homologs. When working with the non-redundant (nr) sequence database of 2021, {Psi}-BLAST, already after the second iteration, retains the query sequence at the top of the list of the found homologs to this sequence in only 18% of cases. Moreover, a query sequence is still listed among homologs found by {Psi}-BLAST after the recommended 10 iterations [Altschul et al., 1997, Nucl. Acids Res., 25:3389-3402] in only 42% of cases. Using a considerably smaller nr database-2011 as a reference, we reveal that these effects intensify over time. Our findings underscore the necessity for circumspection when interpreting {Psi}-BLAST outcomes; the degree of vigilance must increase with the database size. A vigilant monitoring of the position of the query sequence in the array of detected homologs is needed. We recommend using the disappearance of the query sequence from the list of homologs produced by {Psi}-BLAST as a criterion to conclude the {Psi}-BLAST iterations.

bioinformatics↗

bioRxiv2019

HypercubeME: two hundred million combinatorially complete datasets from a single experiment

MotivationEpistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a \"combinatorially complete dataset\". So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets.\n\nResultsWe present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data.\n\nAvailabilityhttps://github.com/ivankovlab/HypercubeME.git.

evolutionary biology↗