MOTIVATION: Protein-protein interaction networks provide insights into the relationships between the proteins of an organism thereby contributing to a better understanding of cellular processes. Nevertheless, large-scale interaction networks are available for only a few model organisms. Thus, interologs are useful for a systematic transfer of protein interaction networks between organisms. However, no standard tool is available so far for that purpose. RESULTS: In this study, we present an automated prediction tool developed for all sequenced genomes available in Integr8. We also have developed a second method to predict protein-protein interactions in the widely used cyanobacterium Synechocystis. Using these methods, we have constructed a new network of 8783 inferred interactions for Synechocystis. AVAILABILITY: InteroPORC is open-source, downloadable and usable through a web interface at http://biodev.extra.cea.fr/interoporc/.

MOTIVATION: To identify genetic conservation relative to precise aspects of developmental diversity, an essential question in computational biology, we developed a new comparative method that allows conserved modules for the best balance between protein sequence similarity and gene co-expression to be constructed, in invertebrates. RESULTS: Our method, referred to as the best-balance constraint procedure (BBCP), yielded 719 functionally conserved modules (FCMs) comprising 2-23 gene pairs. These modules were consistent with the developmental roles of orthologues as inferred from Gene Ontology, RNAi knockouts, InterPro and process-specific microarray data. New relationships were defined between genetic conservation and developmental diversity. Novel gene associations were indeed found in 94% of the FCMs, 150 modules being completely new. A significant proportion of the FCMs (18%, 132 modules) described cell type-specific mechanisms, comprising neuronal, muscle and germ cell signaling, new associations being found in 125 modules. Also found were gene associations for cell fate specification activities previously not highlighted by computational means, e.g. in FCMs containing homeogenes. These data indicate that highly discriminative description of genetic conservation can be deduced using BBCP, and reveal new correlations between cellular and developmental diversity and gene essentiality in invertebrates. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: For supplementary information, please refer to Bioinformatics online.

MOTIVATION: Different automatic methods of sequence alignments are routinely used as a starting point for homology searches and function inference. Confidence in an alignment probability is one of the major fundamentals of massive automatic genome-scale pairwise comparisons, for clustering of putative orthologs and paralogs, sequenced genome annotation or multiple-genomic tree constructions. Extreme value distribution based on the Karlin-Altschul model, usually advised for large-scale comparisons are not always valid, particularly in the case of comparisons of non-biased with nucleotide-biased genomes (such that of Plasmodium falciparum). Z-values estimates based on Monte Carlo technics, can be calculated experimentally for any alignment output, whatever the method used. Empirically, a Z-value higher than approximately 8 is supposed reasonable to assess that an alignment score is significant, but this arbitrary figure was never theoretically justified. RESULTS: In this paper, we used the Bienaymé-Chebyshev inequality to demonstrate a theorem of the upper limit of an alignment score probability (or P-value). This theorem implies that a computed Z-value is a statistical test, a single-linkage clustering criterion and that 1/Z-value(2) is an upper limit to the probability of an alignment score whatever the actual probability law is. Therefore, this study provides the missing theoretical link between a Z-value cut-off used for an automatic clustering of putative orthologs and/or paralogs, and the corresponding statistical risk in such genome-scale comparisons (using non-biased or biased genomes).