Biomedical subjects

Kristian Vlahovicek

Publications and source records attributed to Kristian Vlahovicek.

4 recordsLinked to original sources

PubMed2003

DNA analysis servers: plot.it, bend.it, model.it and IS.

The WWW servers at http://www.icgeb.trieste.it/dna/ are dedicated to the analysis of user-submitted DNA sequences; plot.it creates parametric plots of 45 physicochemical, as well as statistical, parameters; bend.it calculates DNA curvature according to various methods. Both programs provide 1D as well as 2D plots that allow localisation of peculiar segments within the query. The server model.it creates 3D models of canonical or bent DNA starting from sequence data and presents the results in the form of a standard PDB file, directly viewable on the user's PC using any molecule manipulation program. The recently established introns server allows statistical evaluation of introns in various taxonomic groups and the comparison of taxonomic groups in terms of length, base composition, intron type etc. The options include the analysis of splice sites and a probability test for exon-shuffling.

Computer Graphics↗

PubMed2003

The DNA secondary structure of the Bacillus subtilis genome.

The entire genomic DNA sequence of the Gram-positive bacterium Bacillus subtilis reported in the SubtiList database has been subjected in this work to a complete bioinformatic analysis of the potential formation of secondary DNA structures such as hairpins and bending. The most significant of these structures have been mapped with respect to their genomic location and compared to those structures already known to have a physiological role, such as the rho-independent transcription terminators. The distribution of these structures along the bacterial chromosome shows two major features: (i). the concentration of the most curved DNA in the intergenic regions rather than within the ORFs, and (ii). a decreasing gradient of large hairpins from the origin towards the terC end of chromosomal DNA replication. Given the increasing biological relevance of secondary DNA structures, these findings should facilitate further studies on the evolution, dynamics and expression of the genetic information stored in bacterial genomes.

Bacillus subtilis↗

PubMed2003

The SBASE domain sequence library, release 10: domain architecture prediction.

SBASE (http://www.icgeb.trieste.it/sbase) is an on-line collection of protein domain sequences and related computational tools designed to facilitate detection of domain homologies based on simple database search. The 10th 'jubilee release' of the SBASE library of protein domain sequences contains 1 052 904 protein sequence segments annotated by structure, function, ligand-binding or cellular topology, clustered into over 6000 domain groups. Domain identification and functional prediction are based on a comparison of BLAST search outputs with a knowledge base of biologically significant similarities extracted from known domain groups. The knowledge base is generated automatically for each domain group from the comparison of within-group ('self') and out-of-group ('non-self') similarities. This is a memory-based approach wherein group-specific similarity functions are automatically learned from the database.

Animals↗

PubMed2002

The SBASE protein domain library, release 9.0: an online resource for protein domain identification.

SBASE (http://www.icgeb.trieste.it/sbase) is an online resource of protein domain sequences designed to facilitate detection of domain homologies based on a simple database search. The ninth release of the SBASE library of protein domain sequences contains 320 000 annotated structural, functional, ligand-binding and topogenic segments of proteins clustered into over 3481 domain groups and 483 protein families. Domain identification and functional prediction are based on a comparison of BLAST search outputs with a knowledge base of within-group ('self') and out-of-group ('non-self') similarities of the known domain groups. This is a memory-based approach wherein class-specific similarity functions are automatically learned from the database [Stanfill,C. and Waltz,D. (1986) COMMUN: ACM, 29, 1213-1228].

Animals↗