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V V Solovyev

Publications and source records attributed to V V Solovyev.

At least 19 recordsLinked to original sources
PubMed2005

Plant promoter prediction with confidence estimation.

Accurate prediction of promoters is fundamental to understanding gene expression patterns, where confidence estimation is one of the main requirements. Using recently developed transductive confidence machine (TCM) techniques, we developed a new program TSSP-TCM for the prediction of plant promoters that also provides confidence of the prediction. The program was trained on 132 and 104 sequences and tested on 40 and 25 sequences (containing TATA and TATA-less promoters, respectively) with known transcription start sites (TSSs). As negative training samples for TCM learning we used coding and intron sequences of plant genes annotated in the GenBank. In the test set of TATA promoters, the program correctly predicted TSS for 35 out of 40 (87.5%) genes with a median deviation of several base pairs from the true site location. For 25 TATA-less promoters, TSSs were predicted for 21 out of 25 (84%) genes, including 14 cases of 5 bp distance between annotated and predicted TSSs. Using TSSP-TCM program we annotated promoters in the whole Arabidopsis genome. The predicted promoters were in good agreement with the start position of known Arabidopsis mRNAs. Thus, TCM technique has produced a plant-oriented promoter prediction tool of high accuracy. TSSP-TCM program and annotated promoters are available at http://mendel.cs.rhul.ac.uk/mendel.php?topic=fgen.

Arabidopsis↗
PubMed2003

PromH: Promoters identification using orthologous genomic sequences.

Accurate prediction of promoters is fundamental for understanding gene expression patterns, cell specificity and development. In the studies of conserved features of regulatory regions of orthologous genes, it was observed that major promoter functional components such as transcription start points, TATA-boxes and regulatory motifs, are significantly more conservative than the sequences around them (70-100% compared with 30-50%). To improve promoter identification accuracy, we employed these findings in a new program, PromH, created by extending the TSSW program feature set. PromH uses linear discriminant functions that take into account conservation features and nucleotide sequences of promoter regions in pairs of orthologous genes. The program was tested on two sets of pairs of orthologous, mostly human and rodent, sequences with known transcription start sites (TSS), annotated to have TATA (21 genes, 11 orthologous pairs) and TATA-less (38 genes, 19 pairs) promoters, respectively. The program correctly predicted TSS for all 21 genes of the first set with a median deviation of 2 bp from true site location. Only for two genes, was there significant (46 and 105 bp) discrepancy between predicted and annotated TSS positions. For 38 TATA-less promoters from the second set, TSS was predicted for 27 genes, in 14 cases within 10 bp distance from annotated TSS, and in 21 cases--within 100 bp distance. Despite more discrepancies between predicted and annotated TSS for genes from the second set, these results are consistent with observations of much higher occurrence of multiple TSS in TATA-less promoters. In any case, our results show that PromH identifies TSS positions significantly more accurately than any other published promoter prediction method. The PromH program is available at http://www.softberry.com/berry.phtml?topic=promh.

Animals↗
PubMed2001

SpliceDB: database of canonical and non-canonical mammalian splice sites.

A database (SpliceDB) of known mammalian splice site sequences has been developed. We extracted 43 337 splice pairs from mammalian divisions of the gene-centered Infogene database, including sites from incomplete or alternatively spliced genes. Known EST sequences supported 22 815 of them. After discarding sequences with putative errors and ambiguous location of splice junctions the verified dataset includes 22 489 entries. Of these, 98.71% contain canonical GT-AG junctions (22 199 entries) and 0.56% have non-canonical GC-AG splice site pairs. The remainder (0.73%) occurs in a lot of small groups (with a maximum size of 0.05%). We especially studied non-canonical splice sites, which comprise 3.73% of GenBank annotated splice pairs. EST alignments allowed us to verify only the exonic part of splice sites. To check the conservative dinucleotides we compared sequences of human non-canonical splice sites with sequences from the high throughput genome sequencing project (HTG). Out of 171 human non-canonical and EST-supported splice pairs, 156 (91.23%) had a clear match in the human HTG. They can be classified after sequence analysis as: 79 GC-AG pairs (of which one was an error that corrected to GC-AG), 61 errors corrected to GT-AG canonical pairs, six AT-AC pairs (of which two were errors corrected to AT-AC), one case was produced from a non-existent intron, seven cases were found in HTG that were deposited to GenBank and finally there were only two other cases left of supported non-canonical splice pairs. The information about verified splice site sequences for canonical and non-canonical sites is presented in SpliceDB with the supporting evidence. We also built weight matrices for the major splice groups, which can be incorporated into gene prediction programs. SpliceDB is available at the computational genomic Web server of the Sanger Centre: http://genomic.sanger.ac. uk/spldb/SpliceDB.html and at http://www.softberry. com/spldb/SpliceDB.html.

Animals↗
PubMed2000

Analysis of canonical and non-canonical splice sites in mammalian genomes.

A set of 43 337 splice junction pairs was extracted from mammalian GenBank annotated genes. Expressed sequence tag (EST) sequences support 22 489 of them. Of these, 98.71% contain canonical dinucleotides GT and AG for donor and acceptor sites, respectively; 0.56% hold non-canonical GC-AG splice site pairs; and the remaining 0.73% occurs in a lot of small groups (with a maximum size of 0.05%). Studying these groups we observe that many of them contain splicing dinucleotides shifted from the annotated splice junction by one position. After close examination of such cases we present a new classification consisting of only eight observed types of splice site pairs (out of 256 a priori possible combinations). EST alignments allow us to verify the exonic part of the splice sites, but many non-canonical cases may be due to intron sequencing errors. This idea is given substantial support when we compare the sequences of human genes having non-canonical splice sites deposited in GenBank by high throughput genome sequencing projects (HTG). A high proportion (156 out of 171) of the human non-canonical and EST-supported splice site sequences had a clear match in the human HTG. They can be classified after corrections as: 79 GC-AG pairs (of which one was an error that corrected to GC-AG), 61 errors that were corrected to GT-AG canonical pairs, six AT-AC pairs (of which two were errors that corrected to AT-AC), one case was produced from non-existent intron, seven cases were found in HTG that were deposited to GenBank and finally there were only two cases left of supported non-canonical splice sites. If we assume that approximately the same situation is true for the whole set of annotated mammalian non-canonical splice sites, then the 99.24% of splice site pairs should be GT-AG, 0.69% GC-AG, 0.05% AT-AC and finally only 0.02% could consist of other types of non-canonical splice sites. We analyze several characteristics of EST-verified splice sites and build weight matrices for the major groups, which can be incorporated into gene prediction programs. We also present a set of EST-verified canonical splice sites larger by two orders of magnitude than the current one (22 199 entries versus approximately 600) and finally, a set of 290 EST-supported non-canonical splice sites. Both sets should be significant for future investigations of the splicing mechanism.

Animals↗
PubMed2000

Ab initio gene finding in Drosophila genomic DNA.

Ab initio gene identification in the genomic sequence of Drosophila melanogaster was obtained using (human gene predictor) and Fgenesh programs that have organism-specific parameters for human, Drosophila, plants, yeast, and nematode. We did not use information about cDNA/EST in most predictions to model a real situation for finding new genes because information about complete cDNA is often absent or based on very small partial fragments. We investigated the accuracy of gene prediction on different levels and designed several schemes to predict an unambiguous set of genes (annotation CGG1), a set of reliable exons (annotation CGG2), and the most complete set of exons (annotation CGG3). For 49 genes, protein products of which have clear homologs in protein databases, predictions were recomputed by Fgenesh+ program. The first annotation serves as the optimal computational description of new sequence to be presented in a database. Reliable exons from the second annotation serve as good candidates for selecting the PCR primers for experimental work for gene structure verification. Our results shows that we can identify approximately 90% of coding nucleotides with 20% false positives. At the exon level we accurately predicted 65% of exons and 89% including overlapping exons with 49% false positives. Optimizing accuracy of prediction, we designed a gene identification scheme using Fgenesh, which provided sensitivity (Sn) = 98% and specificity (Sp) = 86% at the base level, Sn = 81% (97% including overlapping exons) and Sp = 58% at the exon level and Sn = 72% and Sp = 39% at the gene level (estimating sensitivity on std1 set and specificity on std3 set). In general, these results showed that computational gene prediction can be a reliable tool for annotating new genomic sequences, giving accurate information on 90% of coding sequences with 14% false positives. However, exact gene prediction (especially at the gene level) needs additional improvement using gene prediction algorithms. The program was also tested for predicting genes of human Chromosome 22 (the last variant of Fgenesh can analyze the whole chromosome sequence). This analysis has demonstrated that the 88% of manually annotated exons in Chromosome 22 were among the ab initio predicted exons. The suite of gene identification programs is available through the WWW server of Computational Genomics Group at http://genomic.sanger.ac.uk/gf. html.

Algorithms↗
PubMed1999

INFOGENE: a database of known gene structures and predicted genes and proteins in sequences of genome sequencing projects.

INFOGENE is a database of known and predicted gene structures with descriptions of basic functional signals and gene components. It provides a possibility to create compilations of sequences with a given gene feature as well as to accumulate and analyze predicted genes in finished and unfinished sequences from genome sequencing projects. Protein sequence similarity searches in the database of predicted proteins is offered through the BLASTP program. INFOGENE is realized under the Sequence Retrieval System that provides useful links with the other informational databases. The database is available through the WWW server of the Computational Genomics Group at http://genomic.sanger.ac.uk/db.html

Animals↗
PubMed1998

Statistical significance of ungapped sequence alignments.

Statistical significance of a local sequence alignment depends not only on the similarity score and on the sequence lengths, but also on a length of the alignment. Dependence of the alignment significance on the length of the sequences has been analyzed earlier, and is based on the idea that the longer sequences have more chances to share a local similarity with a bigger score. To the best of our knowledge, a dependence of the statistical significance on the length of an alignment has not been used in selecting the best alignments. We have applied to real proteins formulas for assessing the statistical significance of ungapped local alignments. Let L be a length of the alignment, then the expected value of a similarity score is Sexp = * L, where is the expected similarity between two randomly chosen residues. Value of can be calculated from a similarity (substitution) matrix M and amino acid frequencies P. = sigma ij pi*pj*mij. The probability of observing a score S greater than or equal to x for an alignment of length L is given by the normal distribution: Prob(S > or = x) = 1-integral of N ((S-Sexp)/sigma) = 1-integral of N((S- *L)/sigma m square root of L), where sigma m is a standard deviation of m. From these formula, we conclude, that we should select the best alignment using a normalized value of the similarity score as follows: S' = max ¿(S- *L)/ sigma m square root of L¿. The proposed normalization of the similarity score has been tested on the representative benchmark. To evaluate a performance of the normalization, we have calculated several measures of the recognition quality. Our normalization has improved all these measures. This procedure is important for choosing the correct alignment for homology modelling as well as for selecting distantly related sequences in databases.

Amino Acid Sequence↗
PubMed1998

GeneExpress: a computer system for description, analysis, and recognition of regulatory sequences in eukaryotic genome.

GeneExpress system has been designed to integrate description, analysis, and recognition of eukaryotic regulatory sequences. The system includes 5 basic units: (1) GeneNet contains an object-oriented database for accumulation of data on gene networks and signal transduction pathways and a Java-based viewer that allows an exploration and visualization of the GeneNet information; (2) Transcription Regulation combines the database on transcription regulatory regions of eukaryotic genes (TRRD) and TRRD Viewer; (3) Transcription Factor Binding Site Recognition contains a compilation of transcription factor binding sites (TFBSC) and programs for their analysis and recognition; (4) mRNA Translation is designed for analysis of structural and contextual features of mRNA 5'UTRs and prediction of their translation efficiency; and (5) ACTIVITY is the module for analysis and site activity prediction of a given nucleotide sequence. Integration of the databases in the GeneExpress is based on the Sequence Retrieval System (SRS) created in the European Bioinformatics Institute.

Artificial Intelligence↗
PubMed1997

Protein secondary structure prediction using local alignments.

The accuracy of secondary structure prediction methods has been improved significantly by the use of aligned protein sequences. The PHD method and the NNSSP method reach 71 to 72% of sustained overall three-state accuracy when multiple sequence alignments are with neural networks and nearest-neighbor algorithms, respectively. We introduce a variant of the nearest-neighbor approach that can achieve similar accuracy using a single sequence as the query input. We compute the 50 best non-intersecting local alignments of the query sequence with each sequence from a set of proteins with known 3D structures. Each position of the query sequence is aligned with the database amino acids in alpha-helical, beta-strand or coil states. The prediction type of secondary structure is selected as the type of aligned position with the maximal total score. On the dataset of 124 non-membrane non-homologous proteins, used earlier as a benchmark for secondary structure predictions, our method reaches an overall three-state accuracy of 71.2%. The performance accuracy is verified by an additional test on 461 non-homologous proteins giving an accuracy of 71.0%. The main strength of the method is the high level of prediction accuracy for proteins without any known homolog. Using multiple sequence alignments as input the method has a prediction accuracy of 73.5%. Prediction of secondary structure by the SSPAL method is available via Baylor College of Medicine World Wide Web server.

Algorithms↗
PubMed1997

Analysis of mutation rates in the SMCY/SMCX genes shows that mammalian evolution is male driven.

Mammalian evolution is believed to be male driven because the greater number of germ cell divisions per generation in males increases the opportunity for errors in DNA replication. Since the Y Chromosome (Chr) replicates exclusively in males, its genes should also evolve faster than X or autosomal genes. In addition, estimating the overall male-to-female mutation ratio (alpha m) is of great importance as a large alpha m implies that replication-independent mutagenic events play a relatively small role in evolution. A small alpha m suggests that the impact of these factors may, in fact, be significant. In order to address this problem, we have analyzed the rates of evolution in the homologous X-Y common SMCX/SMCY genes from three different species--mouse, human, and horse. The SMC genes were chosen because the X and Y copies are highly homologous, well conserved in evolution, and in all probability functionally interchangeable. Sequence comparisons and analysis of synonymous substitutions in approximately 1kb of the 5' coding region of the SMC genes reveal that the Y-linked copies are evolving approximately 1.8 times faster than their X homologs. The male-to-female mutation ratio alpha m was estimated to be 3. These data support the hypothesis that mammalian evolution is male driven. However, the ratio value is far smaller than suggested in earlier works, implying significance of replication-independent mutagenic events in evolution.

Amino Acid Sequence↗
PubMed1997

Recognition of 3'-processing sites of human mRNA precursors.

We have developed a computer program POLYAH and an algorithm for the identification of 3'-processing sites of human mRNA precursors. The algorithm is based on a linear discriminant function (LDF) trained to discriminate real poly(A) signal regions from the other regions of human genes possessing the AATAAA sequence which is most likely non-functional. As the parameters of LDF, various significant contextual characteristics of sequences surrounding AATAAA signals were used. An accuracy of method has been estimated on a set of 131 poly(A) regions and 1466 regions of human genes having the AATAAA sequence. When the threshold was set to predict 86% of poly(A) regions correctly, specificity of 51% and correlation coefficient of 0.62 had been achieved. The precision of this approach is better than for the other methods and has been tested on a larger data set. POLYAH can be used through World Wide Web (at Gene-Finder Home page: URL http:@dot.imgen.bcm.tmc.edu:9331/gene-finder/ gf.html) or by sending files with uncharacterized human sequences to the University of Houston or Weizmann Institute of Science e-mail servers.

Algorithms↗
PubMed1995

Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments.

Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.

Algorithms↗
PubMed1995

Identification of human gene structure using linear discriminant functions and dynamic programming.

Development of advanced technique to identify gene structure is one of the main challenges of the Human Genome Project. Discriminant analysis was applied to the construction of recognition functions for various components of gene structure. Linear discriminant functions for splice sites, 5'-coding, internal exon, and 3'-coding region recognition have been developed. A gene structure prediction system FGENE has been developed based on the exon recognition functions. We compute a graph of mutual compatibility of different exons and present a gene structure models as paths of this directed acyclic graph. For an optimal model selection we apply a variant of dynamic programming algorithm to search for the path in the graph with the maximal value of the corresponding discriminant functions. Prediction by FGENE for 185 complete human gene sequences has 81% exact exon recognition accuracy and 91% accuracy at the level of individual exon nucleotides with the correlation coefficient (C) equals 0.90. Testing FGENE on 35 genes not used in the development of discriminant functions shows 71% accuracy of exact exon prediction and 89% at the nucleotide level (C = 0.86). FGENE compares very favorably with the other programs currently used to predict protein-coding regions. Analysis of uncharacterized human sequences based on our methods for splice site (HSPL, RNASPL), internal exons (HEXON), all type of exons (FEXH) and human (FGENEH) and bacterial (CDSB) gene structure prediction and recognition of human and bacterial sequences (HBR) (to test a library for E. coli contamination) is available through the University of Houston, Weizmann Institute of Science network server and a WWW page of the Human Genome Center at Baylor College of Medicine.

Algorithms↗
PubMed1994

Predicting internal exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames.

A new method which predicts internal exon sequences in human DNA has been developed. The method is based on a splice site prediction algorithm that uses the linear discriminant function to combine information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotides in protein coding and intron regions. The accuracy of our splice site recognition function is 97% for donor splice sites and 96% for acceptor splice sites. For exon prediction, we combine in a discriminant function the characteristics describing the 5'-intron region, donor splice site, coding region, acceptor splice site and 3'-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79%. The recognition quality computed at the level of individual nucleotides is 89% for exon sequences and 98% for intron sequences. This corresponds to a correlation coefficient for exon prediction of 0.87. The precision of this approach is better than other methods and has been tested on a larger data set. We have also developed a means for predicting exon-exon junctions in cDNA sequences, which can be useful for selecting optimal PCR primers.

Algorithms↗
PubMed1994

Assignment of position-specific error probability to primary DNA sequence data.

DNA sequence predicted from polyacrylamide gel-based technologies is inaccurate because of variations in the quality of the primary data due to limitations of the technology, and to sequence-specific variations due to nucleotide interactions within the DNA molecule and with the gel. The ability to recognize the probability of error in the primary data will be useful in reconstructing the target sequence of a DNA sequencing project, and in estimating the accuracy of the final sequence. This paper describes the use of linear discriminant analysis to assign position-specific probabilities of incorrect, over- and under-prediction of nucleotides for each predicted nucleotide position in primary sequence data generated by a gel-based DNA sequencing technology. Using this method, most of the error potential in primary sequence data can be assigned to a limited number of discrete positions. The use of probability values in the sequence reconstruction process, and in estimating the accuracy of consensus sequence determination is described.

Base Sequence↗
PubMed1994

Predicting alpha-helix and beta-strand segments of globular proteins.

All current methods of protein secondary structure prediction are based on evaluation of a single residue state. Although the accuracy of the best of them is approximately 60-70%, for reliable prediction of tertiary structure it is more useful to predict an approximate location of alpha-helix and beta-strand segments, especially prolonged ones. We have developed a simple method for protein secondary structure prediction which is oriented on the location of secondary structure segments. The method uses linear discriminant analysis to assign segments of a given amino acid sequence a particular type of secondary structure, by taking into account the amino acid composition of internal parts of segments as well as their terminal and adjacent regions. Four linear discriminant functions were constructed for recognition of short and long alpha-helix and beta-strand segments respectively. These functions combine three characteristics: hydrophobic moment, segment singlet, and pair preferences to an alpha-helix or beta-strand. The last two characteristics are calculated by summing the preference parameters of single residues and pairs of residues located in a segment and its adjacent regions. The final program SSP predicts all possible potential alpha-helices and beta-strands and resolves some possible overlap between them. Overall three-state (alpha, beta, c) prediction gives approximately 65.1% correctly predicted residues on 126 non-homologous proteins using the jackknife test procedure. Analysis of the prediction results shows a high prediction accuracy of long secondary structure segments (approximately 89% of alpha-helices of length > 8 and approximately 71% of beta-strands of length > 6 are correctly located with probability of correct prediction 0.82 and 0.78 respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1994

The prediction of human exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames.

Discriminant analysis is applied to the problem of recognition 5'-, internal and 3'-exons in human DNA sequences. Specific recognition functions were developed for revealing exons of particular types. The method based on a splice site prediction algorithm that uses the linear Fisher discriminant to combine the information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotides in protein coding and intron regions (Solovyev, Lawrence, 1994). The accuracy of our splice site recognition function is about 97%. A discriminant function for 5'-exon prediction includes hexanucleotide composition of upstream region, triplet composition around the ATG codon, ORF coding potential, donor splice site potential and composition of downstream intron region. For internal exon prediction, we combine in a discriminant function the characteristics describing the 5'-intron region, donor splice site, coding region, acceptor splice site and 3'-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79% and a level of pseudoexon ORF prediction of 99.96%. The recognition quality computed at the level of individual nucleotides is 89% for exon sequences and 98% for intron sequences. A discriminant function for 3'-exon prediction includes octanucleotide composition of upstream intron region, triplet composition around the stop codon, ORF coding potential, acceptor splice site potential and hexanucleotide composition of downstream region.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Simulation↗