Computers & chemistry最新文献

Recursive segmentation is a procedure that partitions a DNA sequence into domains with a homogeneous composition of the four nucleotides A, C, G and T. This procedure can also be applied to any sequence converted from a DNA sequence, such as to a binary strong(G+C)/weak(A+T) sequence, to a binary sequence indicating the presence or absence of the dinucleotide CpG, or to a sequence indicating both the base and the codon position information. We apply various conversion schemes in order to address the following five DNA sequence analysis problems: isochore mapping, CpG island detection, locating the origin and terminus of replication in bacterial genomes, finding complex repeats in telomere sequences, and delineating coding and noncoding regions. We find that the recursive segmentation procedure can successfully detect isochore borders, CpG islands, and the origin and terminus of replication, but it needs improvement for detecting complex repeats as well as borders between coding and noncoding regions.

In the framework of genome annotation, scientific literature is obviously the major source of biological knowledge. The aim of the work described in this paper is to exploit this source of data for the model plant Arabidopsis thaliana. The first step has consisted in constituting a relevant bibliographic references dataset for plant genomic research. Genes co-citations have then been systematically annotated in this reference dataset, starting from the simple idea that if genes are cited in the same publication, they must probably share some related functional properties. In order to deal with the synonymous gene name problem; a gene name reference list has been constituted starting from A. thaliana SwissProt entries. This list was used to build clusters of co-cited genes by a single linkage procedure such that any gene in a given cluster possesses at least one co-cited partner in the same cluster. Analysis of the clusters demonstrate the biological consistency of this approach, with only very few fortuitous links. As an example, a cluster including genes related to flowering time is more deeply described in the paper. Finally, a graphical representation of each cluster was performed, which provides a convenient way to retrieve the genes (the nodes of the graphs) and the references in which they were co-cited (the edges of the graphs). All the results can be accessed at the URL http://chlora.Igi.infobiogen.fr:1234/bib_arath/.

This paper describes three weighting schemes for improving the accuracy of progressive multiple sequence alignment methods: (1) global profile pre-processing, to capture for each sequence information about other sequences in a profile before the actual multiple alignment takes place; (2) local pre-processing; which incorporates a new protocol to only use non-overlapping local sequence regions to construct the pre-processed profiles; and (3) local–global alignment, a weighting scheme based on the double dynamic programming (DDP) technique to softly bias global alignment to local sequence motifs. The first two schemes allow the compilation of residue-specific multiple alignment reliability indices, which can be used in an iterative fashion. The schemes have been implemented with associated iterative modes in the PRALINE multiple sequence alignment method, and have been evaluated using the BAliBASE benchmark alignment database. These tests indicate that PRALINE is a toolbox able to build alignments with very high quality. We found that local profile pre-processing raises the alignment quality by 5.5% compared to PRALINE alignments generated under default conditions. Iteration enhances the quality by a further percentage point. The implications of multiple alignment scoring functions and iteration in relation to alignment quality and benchmarking are discussed.

The proteins involved in a single biological process may form a stable supra-molecular assembly or be transiently in interaction. Although, the first annotation steps of a complete genome may allow the identification of the different partners, their assembly in a functional system, referred to as an integrated system, is a domain where methodological effort has to be done. Indeed, the knowledge required to assemble partners of such systems should be explicitly included in annotation software. The availability of a complete genome, and therefore of all the proteins encoded by that genome, motivated the development of automated approaches through the coordinated combination of different bio-informatic methods allowing the identification of the different partners, their assembly and the classification of the reconstructed systems in functional categories. In this data flux, the identification of the sequence partners represents the principal bottleneck. Here, we describe and compare the results obtained with different classes of methods (blastp2, psi-blast, mast and Meta-meme) applied to the identification in complete genomes of a given family of integrated systems: the ABC transporters. psi-blast appears to significantly outperform motif-based methods, and the results are discussed according to the nature of the proteins and the structure of the sub-families.

The Z-value (Comput. Chem. 23 (1999) 333) is an extension of the Z-score that is classically used to compare sets of biological sequences. The Z-value has been successfully used to handle complete genome studies as well as analyze large sets of proteins. The Z-value computation is based on a Monte Carlo approach to estimate the statistical significance of a Smith & Waterman alignment score. Comet et al. (Comput. Chem. 23 (1999) 333) have shown that, in contrast to the alignment score, the Z-value largely reduces the bias due to the lengths and compositions of the sequences. They also described an estimator of the deviation of Z-values, that we extend in this paper in order to optimize Z-values computation. The incremental algorithm described here provides two characteristics which are usually incompatible: (i) it improves the accuracy of Z-values calculation; (ii) it reduces the time complexity (this algorithm has been named incremental because it iteratively adds random sequences to the Monte-Carlo process when needed). Results are presented, originating from the all-by-all comparison of the proteins from Saccharomyces cerevisiae and Escherichia coli.

This paper presents an algorithm, DCFold, that automatically predicts the common secondary structure of a set of aligned homologous RNA sequences. It is based on the comparative approach. Helices are searched in one of the sequences, called the ‘target sequence’, and compared to the helices in the other sequences, called the ‘test sequences’. Our algorithm searches in the target sequence for palindromes that have a high probability to define helices that are conserved in the test sequences. This selection of significant palindromes is based on criteria that take into account their length and their mutation rate. A recursive search of helices, starting from these likely ones, is implemented using the ‘divide and conquer’ approach. Indeed, as pseudo-knots are not searched by DCFold, a selected palindrome (p, p′) makes possible to divide the initial sequence into two sequences, the internal one and the one resulting from the concatenation of the two external ones. New palindromes can be searched independently in these subsequences. This algorithm was run on ribosomal RNA sequences and recovered very efficiently their common secondary structures.