Alexey V. Kochetov
Novosibirsk State University
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Featured researches published by Alexey V. Kochetov.
BMC Bioinformatics | 2007
Alexey V. Kochetov; Andrey Palyanov; Igor I. Titov; D. A. Grigorovich; Akinori Sarai; Nikolay A. Kolchanov
BackgroundThe translation start site plays an important role in the control of translation efficiency of eukaryotic mRNAs. The recognition of the start AUG codon by eukaryotic ribosomes is considered to depend on its nucleotide context. However, the fraction of eukaryotic mRNAs with the start codon in a suboptimal context is relatively large. It may be expected that mRNA should possess some features providing efficient translation, including the proper recognition of a translation start site. It has been experimentally shown that a downstream hairpin located in certain positions with respect to start codon can compensate in part for the suboptimal AUG context and also increases translation from non-AUG initiation codons. Prediction of such a compensatory hairpin may be useful in the evaluation of eukaryotic mRNA translation properties.ResultsWe evaluated interdependency between the start codon context and mRNA secondary structure at the CDS beginning: it was found that a suboptimal start codon context significantly correlated with higher base pairing probabilities at positions 13 – 17 of CDS of human and mouse mRNAs. It is likely that the downstream hairpins are used to enhance translation of some mammalian mRNAs in vivo. Thus, we have developed a tool, AUG_hairpin, to predict local stem-loop structures located within the defined region at the beginning of mRNA coding part. The implemented algorithm is based on the available published experimental data on the CDS-located stem-loop structures influencing the recognition of upstream start codons.ConclusionAn occurrence of a potential secondary structure downstream of start AUG codon in a suboptimal context (or downstream of a potential non-AUG start codon) may provide researchers with a testable assumption on the presence of additional regulatory signal influencing mRNA translation initiation rate and the start codon choice. AUG_hairpin, which has a convenient Web-interface with adjustable parameters, will make such an evaluation easy and efficient.
Bioinformatics | 2005
Alexey V. Kochetov
MOTIVATION The translation start site plays an important role in the control of translation efficiency of eukaryotic mRNAs. However, mRNAs with a suboptimal context of start AUG codon are relatively abundant. It is likely that at least some mRNAs with suboptimal start codon context contain the other signals providing additional information for efficient AUG recognition. RESULTS Frequency of AUG codons at the beginning of the coding part of eukaryotic mRNAs was analyzed in relation to the context of translation start codon. It was found that the observed downstream AUG content in the mRNAs with optimal start codon context was close to the expected value, whereas it was significantly higher in the mRNAs with a suboptimal context. It is likely that downstream AUG codons can often be utilized as additional start sites to increase translation rate of mRNAs with a suboptimal context of the annotated start codon and many eukaryotic proteins can be characterized by some N-end heterogeneity.
Bioinformatics | 2004
Alexey V. Kochetov; Akinori Sarai
MOTIVATION According to scanning model, 40S ribosomal subunits can either initiate translation at start AUG codon in suboptimal context or miss it and initiate translation at downstream AUG(s), thereby producing several proteins. Functional significance of such a protein translational polymorphism is still unknown. RESULTS We compared predicted subcellular localizations of annotated Arabidopsis thaliana proteins and their potential N-terminally truncated forms started from the nearest downstream in-frame AUG codons. It was found that localizations of full and N-truncated proteins differ in many cases: 12.2% of N-truncated proteins acquired sorting signals de novo and 5.7% changed their predicted subcellular locations (mitochodria, chloroplast or secretory pathway). It is likely that the in-frame downstream AUGs may be frequently utilized to synthesize proteins possessing new functional properties and such a translational polymorphism may serve as an important source of cellular and organelle proteomes.
Journal of Integrative Bioinformatics | 2010
Björn Sommer; Evgeny S. Tiys; Benjamin Kormeier; Klaus Hippe; Sebastian Jan Janowski; Timofey V. Ivanisenko; Anatoly O. Bragin; Patrizio Arrigo; Pavel S. Demenkov; Alexey V. Kochetov; Vladimir A. Ivanisenko; N. A. Kolchanov; Ralf Hofestädt
Detailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to create such a network associated with an abstract cell environment is discussed in order to gain insights into the functional as well as spatial interrelationships. It is shown that experimentally gained knowledge enriched with data warehouse content and text mining data can be used for the reconstruction and localization of a cardiovascular disease developing network beginning with MUPP1/MPDZ (multi-PDZ domain protein).
Journal of Bioinformatics and Computational Biology | 2013
Björn Sommer; Benjamin Kormeier; Pavel S. Demenkov; Patrizio Arrigo; Klaus Hippe; Özgür Ates; Alexey V. Kochetov; Vladimir A. Ivanisenko; N. A. Kolchanov; Ralf Hofestädt
The CELLmicrocosmos PathwayIntegration (CmPI) was developed to support and visualize the subcellular localization prediction of protein-related data such as protein-interaction networks. From the start it was possible to manually analyze the localizations by using an interactive table. It was, however, quite complicated to compare and analyze the different localization results derived from data integration as well as text-mining-based databases. The current software release provides a new interactive visual workflow, the Subcellular Localization Charts. As an application case, a MUPP1-related protein-protein interaction network is localized and semi-automatically analyzed. It will be shown that the workflow was dramatically improved and simplified. In addition, it is now possible to use custom protein-related data by using the SBML format and get a view of predicted protein localizations mapped onto a virtual cell model.
Journal of Integrative Bioinformatics | 2016
Canan Has; Sergey A. Lashin; Alexey V. Kochetov; Jens Allmer
Abstract Improvements in genome sequencing technology increased the availability of full genomes and transcriptomes of many organisms. However, the major benefit of massive parallel sequencing is to better understand the organization and function of genes which then lead to understanding of phenotypes. In order to interpret genomic data with automated gene annotation studies, several tools are currently available. Even though the accuracy of computational gene annotation is increasing, a combination of multiple lines of experimental evidences should be gathered. Mass spectrometry allows the identification and sequencing of proteins as major gene products; and it is only these proteins that conclusively show whether a part of a genome is a coding region or not to result in phenotypes. Therefore, in the field of proteogenomics, the validation of computational methods is done by exploiting mass spectrometric data. As a result, identification of novel protein coding regions, validation of current gene models, and determination of upstream and downstream regions of genes can be achieved. In this paper, we present new functionality for our proteogenomic tool, PGMiner which performs all proteogenomic steps like acquisition of mass spectrometric data, peptide identification against preprocessed sequence databases, assignment of statistical confidence to identified peptides, mapping confident peptides to gene models, and result visualization. The extensions cover determining proteotypic peptides and thus unambiguous protein identification. Furthermore, peptides conflicting with gene models can now automatically assessed within the context of predicted alternative open reading frames.
Bioinformatics | 2016
Alexey V. Kochetov; Jens Allmer; Alexandra Igorevna Klimenko; Bulat S. Zuraev; Yury G. Matushkin; Sergey A. Lashin
Motivation: Protein synthesis is not a straight forward process and one gene locus can produce many isoforms, for example, by starting mRNA translation from alternative start sites. altORF evaluator (altORFev) predicts alternative open reading frames within eukaryotic mRNA translated by a linear scanning mechanism and its modifications (leaky scanning and reinitiation). The program reveals the efficiently translated altORFs recognized by the majority of 40S ribosomal subunits landing on the 5′‐end of an mRNA. This information aids to reveal the functions of eukaryotic genes connected to synthesis of either unknown isoforms of annotated proteins or new unrelated polypeptides. Availability and Implementation: altORFev is available at http://www.bionet.nsc.ru/AUGWeb/and has been developed in Java 1.8 using the BioJava library; and the Vaadin framework to produce the web service. Contact: [email protected]
Bioinformatics | 2001
Igor B. Rogozin; Alexey V. Kochetov; Fyodor A. Kondrashov; Eugene V. Koonin; Luciano Milanesi
international conference on bioinformatics | 1999
Alexey V. Kochetov; Mikhail P. Ponomarenko; Anatoly S. Frolov; Lev L. Kisselev; N. A. Kolchanov
intelligent systems in molecular biology | 1998
N. A. Kolchanov; Mikhail P. Ponomarenko; Alexander E. Kel; Y. V. Kondrakhin; Anatoly S. Frolov; Fedor A. Kolpakov; T. N. Goryachkovskaya; O. V. Kel; Elena A. Ananko; E. V. Ignatieva; O. A. Podkolodnaya; V. N. Babenko; Irina L. Stepanenko; Aida G. Romashchenko; T. I. Merkulova; Denis G. Vorobiev; Sergey V. Lavryushev; Y. V. Ponomarenko; Alexey V. Kochetov; Grigory Kolesov; Victor V. Solovyev; Luciano Milanesi; Nikolay L. Podkolodny; Edgar Wingender; T. Heinemeyer