ABSTRACTWe present our preliminary results for a full automatic strategy to define new relationships between biological sequences based on theidentification of low-level similarity signals combined with the intrinsic features of the sequences sharing these signals. We start with theautomatic extraction of signals, using an exhaustive but simple database searching strategy, and a set of tools for automatic patternrecognition and analysis of the high volume of data generated, then we apply association rules discovery techniques to identify previouslyunknown knowledge. This strategy has shown suitable to identify well known motifs and functional domains along several tests and it hasalso shown suitable to predict new relationships between sequences based on this low-level similarities. Our work includes a parallelimplementation of all the computationally demand step for shared-distributed memory multiprocessors, both using message-passing andthread-based programming models obtaining very good levels of efficiency.Keywords:

SUMMARY AnaGram is a web service for protein function assignment based on identity detection of small significant fragments (protomotifs) that can act as modular pieces in peptide construction. The system is able to assign function by finding correlations between protomotifs and functional annotations contained in SWISS-PROT and Medline databases. In addition, function ontologies are used for hierarchical organization of the predicted functions. Extensive tests have been carried out to evaluate the accuracy and performance of the system. AVAILABILITY http://jaguar.genetica.uma.es/anagram.htm

Motivation: The description of genes in databases by keywords helps the non-specialist to quickly grasp the properties of a gene and increases the efficiency of computational tools that are applied to gene data (e.g. searching a gene database for sequences related to a particular biological process). However, the association of keywords to genes or protein sequences is a difficult process that ultimately implies examination of the literature related to a gene. Results: To support this task, we present a procedure to derive keywords from the set of scientific abstracts related to a gene. Our system is based on the automated extraction of mappings between related terms from different databases using a model of fuzzy associations that can be applied with all generality to any pair of linked databases. We tested the system by annotating genes of the SWISS-PROT database with keywords derived from the abstracts linked to their entries (stored in the MEDLINE database of scientific references). The performance of the annotation procedure was much better for SWISS-PROT keywords (recall of 47%, precision of 68%) than for Gene Ontology terms (recall of 8%, precision of 67%). Availability: The algorithm can be publicly accessed and used for the annotation of sequences through a web server at http://www.bork.embl.de/kat

A method for assigning functions to unknown sequences based on finding correlations between short signals and functional annotations in a protein database is presented. This approach is based on keyword (KW) and feature (FT) information stored in the SWISS-PROT database. The former refers to particular protein characteristics and the latter locates these characteristics at a specific sequence position. In this way, a certain keyword is only assigned to a sequence if sequence similarity is found in the position described by the FT field. Exhaustive tests performed over sequences with homologues (cluster set) and without homologues (singleton set) in the database show that assigning functions is much 'cleaner' when information about domains (FT field) is used, than when only the keywords are used.