Demo Data from VLDB 2026 / reviewers in the wild / expert
Vladimir N. Babenko
dblp:84/3373
· DBLP profile ↗
6ranked-venue papers
1as first author
0since 2021 · last 2005
0000-0002-3077-9559ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 6 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
4 papers |
Bioinformatics and computational biology · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › sequence analysis
genomic sequence analysis |
0.0 | 2 | 1998 | GenExpress: A Computer System for Description, Analysis and Recognition of Regulatory Sequences in Eukaryotic Genome · ISMB 1998 Identification of CDNA Sequences by Specific Oligonucleotide Sets - A Computer Tool and Application · ISMB 1995 |
Bioinformatics and computational biology › gene expression analysis
oligonucleotide probe design |
0.0 | 1 | 1998 | A genetic algorithm for designing gene family-specific oligonucleotide sets used for hybridization: the G protein-coupled receptor protein superfamily · Bioinform. 1998 |
Bioinformatics and computational biology › gene regulation
regulatory sequence analysis |
0.0 | 1 | 1998 | GenExpress: A Computer System for Description, Analysis and Recognition of Regulatory Sequences in Eukaryotic Genome · ISMB 1998 |
Bioinformatics and computational biology
genome annotation |
0.0 | 1 | 1998 | GenExpress: A Computer System for Description, Analysis and Recognition of Regulatory Sequences in Eukaryotic Genome · ISMB 1998 |
Methods — techniques the papers use, named apart from their topics
sequence complexity profile · 0.0nucleotide frequency profile · 0.0degenerate oligonucleotide motif · 0.0genetic algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Analysis of evolution of exon-intron structure of eukaryotic genesabstractThe availability of multiple, complete eukaryotic genome sequences allows one to address many fundamental evolutionary questions on genome scale. One such important, long-standing problem is evolution of exon-intron structure of eukaryotic genes. Analysis of orthologous genes from completely sequenced genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists. The data on shared and lineage-specific intron positions were used as the starting point for evolutionary reconstruction with parsimony and maximum-likelihood approaches. Parsimony methods produce reconstructions with intron-rich ancestors but also infer lineage-specific, in many cases, high levels of intron loss and gain. Different probabilistic models gave opposite results, apparently depending on model parameters and assumptions, from domination of intron loss, with extremely intron-rich ancestors, to dramatic excess of gains, to the point of denying any true conservation of intron positions among deep eukaryotic lineages. Development of models with adequate, realistic parameters and assumptions seems to be crucial for obtaining more definitive estimates of intron gain and loss in different eukaryotic lineages. Many shared intron positions were detected in ancestral eukaryotic paralogues which evolved by duplication prior to the divergence of extant eukaryotic lineages. These findings indicate that numerous introns were present in eukaryotic genes already at the earliest stages of evolution of eukaryotes and are compatible with the hypothesis that the original, catastrophic intron invasion accompanied the emergence of the eukaryotic cells. Comparison of various features of old and younger introns starts shedding light on probable mechanisms of intron insertion, indicating that propagation of old introns is unlikely to be a major mechanism for origin of new ones. The existence and structure of ancestral protosplice sites were addressed by examining the context of introns inserted within codons that encode amino acids conserved in all eukaryotes and, accordingly, are not subject to selection for splicing efficiency. It was shown that introns indeed predominantly insert into or are fixed in specific protosplice sites which have the consensus sequence (A/C)AG|Gt. Igor B. Rogozin, Alexander V. Sverdlov, Vladimir N. Babenko, Eugene V. Koonin |
Briefings Bioinform. | 3 |
| 2003 | Computational analysis of mutation spectraabstractMutation frequencies vary along a nucleotide sequence, and nucleotide positions with an exceptionally high mutation frequency are called hotspots. Mutation hotspots in DNA often reflect intrinsic properties of the mutation process, such as the specificity with which mutagens interact with nucleic acids and the sequence-specificity of DNA repair/replication enzymes. They might also reflect structural and functional features of target protein or RNA sequences in which they occur. The determinants of mutation frequency and specificity are complex and there are many analytical methods for their study. This paper discusses computational approaches to analysing mutation spectra (distribution of mutations along the target genes) that include many detectable (mutable) positions. The following methods are reviewed: mutation hotspot prediction; pairwise and multiple comparisons of mutation spectra; derivation of a consensus sequence; and analysis of correlation between nucleotide sequence features and mutation spectra. Spectra of spontaneous and induced mutations are used for illustration of the complexities and pitfalls of such analyses. In general, the DNA sequence context of mutation hotspots is a fingerprint of interactions between DNA and DNA repair/replication/modification enzymes, and the analysis of hotspot context provides evidence of such interactions. Igor B. Rogozin, Vladimir N. Babenko, Luciano Milanesi, Youri I. Pavlov |
Briefings Bioinform. | 2 |
| 1999 | Investigating extended regulatory regions of genomic DNA sequencesabstractMOTIVATION: Despite the growing volume of data on primary nucleotide sequences, the regulatory regions remain a major puzzle with regard to their function. Numerous recognising programs considering a diversity of properties of regulatory regions have been developed. The system proposed here allows the specific contextual, conformational and physico-chemical properties to be revealed based on analysis of extended DNA regions. RESULTS: The Internet-accessible computer system RegScan, designed to analyse the extended regulatory regions of eukaryotic genes, has been developed. The computer system comprises the following software: (i) programs for classification dividing a set of promoters into TATA-containing and TATA-less promoters and promoters with and without CpG islands; (ii) programs for constructing (a) nucleotide frequency profiles, (b) sequence complexity profiles and (c) profiles of conformational and physico-chemical properties; (iii) the program for constructing the sets of degenerate oligonucleotide motifs of a specified length; and (iv) the program searching for and visualising repeats in nucleotide sequences. The system has allowed us to demonstrate the following characteristic patterns of vertebrate promoter regions: the TATA box region is flanked by regions with an increased G+C content and increased bending stiffness, the TATA box content is asymmetric and promoter regions are saturated with both direct and inverted repeats. AVAILABILITY: The computer system RegScan is available via the Internet at http://www.mgs.bionet.nsc. ru/Systems/RegScan, http://www.cbil.upenn.edu/mgs/systems/r egscan/. Vladimir N. Babenko, P. S. Kosarev, Oleg V. Vishnevsky, Victor G. Levitsky, V. V. Basin, Anatoly S. Frolov |
Bioinform. | 1 |
| 1998 | GenExpress: A Computer System for Description, Analysis and Recognition of Regulatory Sequences in Eukaryotic Genome
Nikolay A. Kolchanov, Mikhail P. Ponomarenko, Alexander E. Kel, Yury V. Kondrakhin, Anatoly S. Frolov, Fedor A. Kolpakov, T. N. Goryachkovskaya, O. V. Kel, Elena A. Ananko, Elena V. Ignatieva, Olga A. Podkolodnaya, Vladimir N. Babenko, Irina L. Stepanenko, Aida G. Romashchenko, Tatyana I. Merkulova, Denis G. Vorobiev, Sergey V. Lavryushev, Y. V. Ponomarenko, Alexey V. Kochetov, G. B. Kolesov, Victor V. Solovyev, Luciano Milanesi, Nikolay L. Podkolodny, Edgar Wingender, T. Heinemeyer |
ISMB | 12 |
| 1998 | A genetic algorithm for designing gene family-specific oligonucleotide sets used for hybridization: the G protein-coupled receptor protein superfamilyabstractMOTIVATION: Massive oligonucleotide hybridization is one of the most promising technologies of functional genome analysis. The critical point is to design appropriate sets of oligonucleotides that can be used effectively in identification by hybridization. RESULTS: Using a genetic algorithm approach, we have attempted to design sets of oligo probes capable of identifying new genes belonging to a defined gene family within a cDNA or genomic library. It is not limited by oligonucleotide length and admits the letter 'N' in the structure of the oligonucleotides selected. One of the major advantages of this approach is the low homology required to identify functional families of sequences with little homology. We have designed the oligonucleotide sets that are most selective for the cDNA clones of transmembrane G protein-coupled receptors (GPCRs), a large family of proteins that form part of a modular system of extracellular signal transduction to the intracellular second messenger pathways. The accuracy of identification has been checked on the EST library containing 713 870 cDNA sequences. A set of 15 oligos between 7 and 14 bases in length has correctly identified 70% of the GPCR cDNA collection sequences with 0.02% false positives. AVAILABILITY: The developed software is available by ftp://ftp.bionet.nsc. ru/pub/biology/ and on the Web page http://www.bionet.nsc. ru/SRCG/Oligoselector/. CONTACT: [email protected]; sebastian. [email protected] Alexander E. Kel, Andrey A. Ptitsyn, Vladimir N. Babenko, Sebastian Meier-Ewert, Hans Lehrach |
Bioinform. | 3 |
| 1995 | Identification of CDNA Sequences by Specific Oligonucleotide Sets - A Computer Tool and Application
Nikolay A. Kolchanov, Oleg V. Vishnevsky, Vladimir N. Babenko, Alexander E. Kel, Ilya N. Shindyalov |
ISMB | 3 |