UNLABELLED: Biological insights can be obtained through computational integration of genomics data sets consisting of diverse types of information. The integration is often hampered by a large variety of existing file formats, often containing similar information, and the necessity to use complicated tools to achieve the desired results. We have built an R package, genomation, to expedite the extraction of biological information from high throughput data. The package works with a variety of genomic interval file types and enables easy summarization and annotation of high throughput data sets with given genomic annotations. AVAILABILITY AND IMPLEMENTATION: The software is currently distributed under MIT artistic license and freely available at http://bioinformatics.mdc-berlin.de/genomation, and through the Bioconductor framework.

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

UNLABELLED: An improved version of the PRIDE (PRobaility of IDEntity) fold prediction algorithm has been developed, based on more solid statistical basis, fast search capabilities and efficient input structure processing. The new algorithm is effective in identifying protein structures at the 'H' level of the CATH hierarchy. AVAILABILITY: The new algorithm is integrated into the PRIDE2 web servers at http://pride.szbk.u-szeged.hu and http://www.icgeb.org/pride. SUPPLEMENTARY INFORMATION: Detailed documentation and performance evaluation is available in the description section of the PRIDE2 web server.

BACKGROUND: There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity. RESULTS: We compared the performance of several commonly used measures and a novel method we introduce in this paper--Measure Independent of Length and Composition (MILC). Large, randomly generated sequence sets were used to test for dependence on (i) sequence length, (ii) overall amount of codon bias and (iii) codon bias discrepancy in the sequences. A derivative of the method, named MELP (MILC-based Expression Level Predictor) can be used to quantitatively predict gene expression levels from genomic data. It was compared to other similar predictors by examining their correlation with actual, experimentally obtained mRNA or protein abundances. CONCLUSION: We have established that MILC is a generally applicable measure, being resistant to changes in gene length and overall nucleotide composition, and introducing little noise into measurements. Other methods, however, may also be appropriate in certain applications. Our efforts to quantitatively predict gene expression levels in several prokaryotes and unicellular eukaryotes met with varying levels of success, depending on the experimental dataset and predictor used. Out of all methods, MELP and Rainer Merkl's GCB method had the most consistent behaviour. A 'reference set' containing known ribosomal protein genes appears to be a valid starting point for a codon usage-based expressivity prediction.

UNLABELLED: INteractive Codon usage Analysis (INCA) provides an array of features useful in analysis of synonymous codon usage in whole genomes. In addition to computing codon frequencies and several usage indices, such as 'codon bias', effective Nc and CAI, the primary strength of INCA has numerous options for the interactive graphical display of calculated values, thus allowing visual detection of various trends in codon usage. Finally, INCA includes a specific unsupervised neural network algorithm, the self-organizing map, used for gene clustering according to the preferred utilization of codons. AVAILABILITY: INCA is available for the Win32 platform and is free of charge for academic use. For details, visit the web page http://www.bioinfo-hr.org/inca or contact the author directly. SUPPLEMENTARY INFORMATION: Software is accompanied with a user manual and a short tutorial.

Abstract Summary: A simple heuristic scoring method is described for assigning sequences to known domain types based on BLAST search outputs. The scoring is based on the score distribution of the known domain groups determined from a database versus database comparison and is directly applicable to BLAST output processing. Availability: The scoring system will be incorporated into the server www.icgeb.trieste.it/sbase/ Contact: [email protected]; [email protected]

UNLABELLED: A WWW server is described for creating 3D models of canonical or bent DNA starting from sequence data. Predicted DNA trajectory is first computed based on a choice of di- and tri-nucleotide models (M.G. Munteanu et al., Trends Biochem. Sci. 23, 341-347, 1998); an atomic model is then constructed and optionally energy-minimized with constrained molecular dynamics. The data are presented as a standard PDB file, directly viewable on the user's PC using any molecule manipulation program. AVAILABILITY: The model.it server is freely available at http://www.icgeb.trieste.it/dna/ CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: a series of help files is available at the above address.

RESULTS: A WWW server for protein domain homology prediction, based on BLAST search and a simple data-mining algorithm (Hegyi,H. and Pongor,S. (1993) Comput. Appl. Biosci., 9, 371-372), was constructed providing a tabulated list and a graphic plot of similarities. AVAILABILITY: http://www.icgeb.trieste.it/domain. Mirror site is available at http://sbase.abc.hu/domain. A standalone programme will be available on request. SUPPLEMENTARY INFORMATION: A series of help files is available at the above addresses.