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Motivation: Neighbor-dependent substitution processes generated specific pattern of dinucleotide frequencies in the genomes of most organisms. The CpG-methylation--deamination process is, e.g. a prominent process in vertebrates (CpG effect). Such processes, often with unknown mechanistic origins, need to be incorporated into realistic models of nucleotide substitutions. Results: Based on a general framework of nucleotide substitutions we developed a method that is able to identify the most relevant neighbor-dependent substitution processes, estimate their relative frequencies and judge their importance in order to be included into the modeling. Starting from a model for neighbor independent nucleotide substitution we successively added neighbor-dependent substitution processes in the order of their ability to increase the likelihood of the model describing given data. The analysis of neighbor-dependent nucleotide substitutions based on repetitive elements found in the genomes of human, zebrafish and fruit fly is presented. Availability: A web server to perform the presented analysis is freely available at: http://evogen.molgen.mpg.de/server/substitution-analysis Contact: arndt@molgen.mpg.de

Motivation: Substantial regional variations of substitutional processes have recently been reported from human/mouse comparisons. However, several features including the C + G dependence and the CpG-based transition effect remain obscure. Results: Utilizing the vast amount of transposable elements in the human genome, we performed detailed analysis of the substitutional and insertion/deletion patterns along the human lineage in a regional and time-resolved fashion. We observed a drastic increase in the CpG-based transition frequency at about the time of the mammalian radiation. We also observed clear regional biases of substitution patterns, most notably a bias to enrich the C+G content toward the telomeres. Availability: The programs used are available upon request from the authors.

The score statistics of a recently introduced `hybrid alignment' algorithm is studied in detail numerically. An extensive survey across the 2216 models of protein domains contained in the Pfam v5.4 database (Bateman et al., Nucleic Acids Res., 28, 263–266, 2000) verifies the theoretical predictions: For the position-specific scoring functions used in the Pfam models, the score statistics of hybrid alignment obey the Gumbel distribution, with the key Gumbel parameter λ taking on the asymptotic value 1 universally for all models. Thus, the use of hybrid alignment eliminates the time-consuming computer simulations normally needed to assign p-values to alignment scores, freeing the users to experiment with different scoring parameters and functions. The performance of the hybrid algorithm in detecting sequence homology is also studied. For protein sequences from the SCOP database (Murzin et al., J. Mol. Biol., 247, 536–540, 1995) using uniform scoring functions, the performance is found to be comparable to the best of the existing methods. Preliminary results using the PfamA database suggest that the hybrid algorithm achieves similar performance as existing methods for position-specific scoring systems as well. Hybrid alignment is thereby established as a high performance alignment algorithm with well-characterized, universal statistics. Contact: yyu@fau.edu * Present address: Department of Physics, The Ohio State University, 174 West 18th Avenue, Columbus, OH 43210-1106, USA

Motivation: The DNA microarray technology can generate a large amount of data describing the time-course of gene expression. These data, when properly interpreted, can yield a great deal of information concerning differential gene expression during development. Much current effort in bioinformatics has been devoted to the analysis of gene expression data, usually via some ‘clustering analysis’ on the raw data in some abstract high dimensional space. Here, we describe a method where we first ‘process’ the raw time-course data using a simple biologically based kinetic model of gene expression. This allows us to reduce the vast data to a few vital attributes characterizing each expression profile, e.g. the times of the onset and cessation of the expression of the developmentally regulated genes. These vital attributes can then be trivially clustered by visual inspection to reveal biologically significant effects. Results: We have applied this approach to microarray expression data from samples isolated every 2 h throughout the 24 h developmental program of Dictyostelium discoideum. mRNA accumulation patterns for 50 developmental genes were found to fit the kinetic model with a p-value of 0.05 or better. Transcription of these genes appears to be initiated in bursts at well-defined periods during development, in a manner suggestive of a dependent sequence. This approach can be applied to analyses of other temporal gene expression patterns, including those of the cell cycle. Contact: sasik@physics.ucsd.edu Supplementary information: Intensity ratios for all genes in this study are available at http://www.biology.ucsd.edu/loomis-cgi/microarray/index.html. * To whom correspondence should be addressed.