This article is part of the supplement: Proceedings of the Ninth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics
Detecting genomic regions associated with a disease using variability functions and Adjusted Rand Index
Département d'lnformatique, Université du Quebec a Montreal, C.P. 8888, Succursale Centre-Ville, Montreal (Quebec), H3C 3P8, Canada
BMC Bioinformatics 2011, 12(Suppl 9):S9 doi:10.1186/1471-2105-12-S9-S9Published: 5 October 2011
The identification of functional regions contained in a given multiple sequence alignment constitutes one of the major challenges of comparative genomics. Several studies have focused on the identification of conserved regions and motifs. However, most of existing methods ignore the relationship between the functional genomic regions and the external evidence associated with the considered group of species (e.g., carcinogenicity of Human Papilloma Virus). In the past, we have proposed a method that takes into account the prior knowledge on an external evidence (e.g., carcinogenicity or invasivity of the considered organisms) and identifies genomic regions related to a specific disease.
Results and conclusion
We present a new algorithm for detecting genomic regions that may be associated with a disease. Two new variability functions and a bipartition optimization procedure are described. We validate and weigh our results using the Adjusted Rand Index (ARI), and thus assess to what extent the selected regions are related to carcinogenicity, invasivity, or any other species classification, given as input. The predictive power of different hit region detection functions was assessed on synthetic and real data. Our simulation results suggest that there is no a single function that provides the best results in all practical situations (e.g., monophyletic or polyphyletic evolution, and positive or negative selection), and that at least three different functions might be useful. The proposed hit region identification functions that do not benefit from the prior knowledge (i.e., carcinogenicity or invasivity of the involved organisms) can provide equivalent results than the existing functions that take advantage of such a prior knowledge. Using the new algorithm, we examined the Neisseria meningitidis FrpB gene product for invasivity and immunologic activity, and human papilloma virus (HPV) E6 oncoprotein for carcinogenicity, and confirmed some well-known molecular features, including surface exposed loops for N. meningitidis and PDZ domain for HPV.