This paper explores the capabilities of multi-objective genetic algorithms to cluster genomic data. We used multiple objective functions not only to further expand the clustering abilities of the algorithm, but also to give more biological significance to the results. Particularly, we grouped a large set of proteins described by a set collection of genomic attributes to infer functional interactions among them. We conducted various computational experiments that demonstrated the proficiency of the proposed method when compared to algorithms that rely on a single biological parameter.

BACKGROUND: Non-homology based methods such as phylogenetic profiles are effective for predicting functional relationships between proteins with no considerable sequence or structure similarity. Those methods rely heavily on traditional similarity metrics defined on pairs of phylogenetic patterns. Proteins do not exclusively interact in pairs as the final biological function of a protein in the cellular context is often hold by a group of proteins. In order to accurately infer modules of functionally interacting proteins, the consideration of not only direct but also indirect relationships is required. In this paper, we used the Bond Energy Algorithm (BEA) to predict functionally related groups of proteins. With BEA we create clusters of phylogenetic profiles based on the associations of the surrounding elements of the analyzed data using a metric that considers linked relationships among elements in the data set. RESULTS: Using phylogenetic profiles obtained from the Cluster of Orthologous Groups of Proteins (COG) database, we conducted a series of clustering experiments using BEA to predict (upper level) relationships between profiles. We evaluated our results by comparing with COG's functional categories, And even more, with the experimentally determined functional relationships between proteins provided by the DIP and ECOCYC databases. Our results demonstrate that BEA is capable of predicting meaningful modules of functionally related proteins. BEA outperforms traditionally used clustering methods, such as k-means and hierarchical clustering by predicting functional relationships between proteins with higher accuracy. CONCLUSION: This study shows that the linked relationships of phylogenetic profiles obtained by BEA is useful for detecting functional associations between profiles and extending functional modules not found by traditional methods. BEA is capable of detecting relationship among phylogenetic patterns by linking them through a common element shared in a group. Additionally, we discuss how the proposed method may become more powerful if other criteria to classify different levels of protein functional interactions, as gene neighborhood or protein fusion information, is provided.

This paper explores the use of the fuzzy C-means clustering algorithm for inferring functional relationships among proteins from their phylogenetic profiles. We conducted a series of experiments using phylogenetic profiles provided by the Cluster of Orthologous Groups of Proteins (COG) database. Experimental results demonstrate that the increasing expressiveness of fuzzy C-means enables the discovery of functional relationships on E.coli operons which traditional methods fail to detect

In this paper, we propose a new method for inferring functional relationships between proteins from their phylogenetic profiles. The evolutionary bond energy algorithm (EBEA) enhances the greedy BEA clustering algorithm with genetic search. We conducted a series of experiments using phylogenetic profiles provided by the Cluster of Orthologous Groups of Proteins (COG) database. Experimental results demonstrate that the proposed method is capable of inferring meaningful clusters of functionally related proteins

SUMMARY: The fact that adjacent genes in bacteria are often functionally related is widely known. GeConT (Gene Context Tool) is a web interface designed to visualize genome context of a gene or a group of genes and their orthologs in all the completely sequenced genomes. The graphical information of GeConT can be used to analyze genome annotation, functional ortholog identification or to verify the genomic context congruence of any set of genes that share a common property. AVAILABILITY: http://www.ibt.unam.mx/biocomputo/gecont.html