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G Kolesov

Publications and source records attributed to G Kolesov.

2 recordsLinked to original sources

Snipping polymorphisms from large EST collections in barley (Hordeum vulgare L.).

The public EST (expressed sequence tag) databases represent an enormous but heterogeneous repository of sequences, including many from a broad selection of plant species and a wide range of distinct varieties. The significant redundancy within large EST collections makes them an attractive resource for rapid pre-selection of candidate sequence polymorphisms. Here we present a strategy that allows rapid identification of candidate SNPs in barley (Hordeum vulgare L.) using publicly available EST databases. Analysis of 271,630 EST sequences from different cDNA libraries, representing 23 different barley varieties, resulted in the generation of 56,302 tentative consensus sequences. In all, 8171 of these unigene sequences are members of clusters with six or more ESTs. By applying a novel SNP detection algorithm (SNiPpER) to these sequences, we identified 3069 candidate inter-varietal SNPs. In order to verify these candidate SNPs, we selected a small subset of 63 present in 36 ESTs. Of the 63 SNPs selected, we were able to validate 54 (86%) using a direct sequencing approach. For further verification, 28 ESTs were mapped to distinct loci within the barley genome. The polymorphism information content (PIC) and nucleotide diversity (pi) values of the SNPs identified by the SNiPpER algorithm are significantly higher than those that were obtained by random sequencing. This demonstrates the efficiency of our strategy for SNP identification and the cost-efficient development of EST-based SNP-markers.

Chromatography, High Pressure Liquid↗

SNAPping up functionally related genes based on context information: a colinearity-free approach.

We describe a computational approach for finding genes that are functionally related but do not possess any noticeable sequence similarity. Our method, which we call SNAP (similarity-neighborhood approach), reveals the conservation of gene order on bacterial chromosomes based on both cross-genome comparison and context information. The novel feature of this method is that it does not rely on detection of conserved colinear gene strings. Instead, we introduce the notion of a similarity-neighborhood graph (SN-graph), which is constructed from the chains of similarity and neighborhood relationships between orthologous genes in different genomes and adjacent genes in the same genome, respectively. An SN-cycle is defined as a closed path on the SN-graph and is postulated to preferentially join functionally related gene products that participate in the same biochemical or regulatory process. We demonstrate the substantial non-randomness and functional significance of SN-cycles derived from real genome data and estimate the prediction accuracy of SNAP in assigning broad function to uncharacterized proteins. Examples of practical application of SNAP for improving the quality of genome annotation are described.

Algorithms↗