UNLABELLED: Genome sequence alignments provide valuable information on many aspects of molecular biological processes. In this study, we developed a web server, GenomeCons, for manipulating multiple genome sequence alignments and their consensus sequences for high-throughput genome sequence analyses. This server facilitates the visual inspection of multiple genome sequence alignments for a set of genomic intervals at a time. This allows the user to examine how these sites are evolutionarily conserved over time for their functional importance. The server also reports consensus sequences for the input genomic intervals, which can be applied to downstream analyses such as the identification of common motifs in the regions determined by ChIP-seq experiments. AVAILABILITY AND IMPLEMENTATION: GenomeCons is freely accessible at http://bioinfo.sls.kyushu-u.ac.jp/genomecons/ CONTACT: [email protected].

MOTIVATION: The computational prediction of protein-protein interactions is currently a major issue in bioinformatics. Recently, a variety of co-evolution-based methods have been investigated toward this goal. In this study, we introduced a partial correlation coefficient as a new measure for the degree of co-evolution between proteins, and proposed its use to predict protein-protein interactions. RESULTS: The accuracy of the prediction by the proposed method was compared with those of the original mirror tree method and the projection method previously developed by our group. We found that the partial correlation coefficient effectively reduces the number of false positives, as compared with other methods, although the number of false negatives increased in the prediction by the partial correlation coefficient. AVAILABILITY: The R script for the prediction of protein-protein interactions reported in this manuscript is available at http://timpani.genome.ad.jp/~parco/

MOTIVATION: The prediction of protein-protein interactions is currently an important issue in bioinformatics. The mirror tree method uses evolutionary information to predict protein-protein interactions. However, it has been recognized that predictions by the mirror tree method lead to many false positives. The incentive of our study was to solve this problem by improving the method of extracting the co-evolutionary information regarding the protein pairs. RESULTS: We developed a novel method to predict protein-protein interactions from co-evolutionary information in the framework of the mirror tree method. The originality is the use of the projection operator to exclude the information about the phylogenetic relationships among the source organisms from the distance matrix. Each distance matrix was transformed into a vector for the operation. The vector is referred to as a 'phylogenetic vector'. We have proposed three ways to extract the phylogenetic information: (1) using the 16S rRNA from the same source organisms as the proteins under consideration, (2) averaging the phylogenetic vectors and (3) analyzing the principal components of the phylogenetic vectors. We examined the performance of the proposed methods to predict interacting protein pairs from Escherichia coli, using experimentally verified data. Our method was successful, and it drastically reduced the number of false positives in the prediction. AVAILABILITY: The R script for the prediction of protein-protein interactions reported in this manuscript is available at http://timpani.genome.ad.jp/~proj/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: The information is also available at the same site as the R script.