MOTIVATION: Finding association between genetic variants and phenotypes related to disease has become an important vehicle for the study of complex disorders. In this context, multi-loci genetic association might unravel additional information when compared with single loci search. The main goal of this work is to propose a non-linear methodology based on information theory for finding combinatorial association between multi-SNPs and a given phenotype. RESULTS: The proposed methodology, called MISS (mutual information statistical significance), has been integrated jointly with a feature selection algorithm and has been tested on a synthetic dataset with a controlled phenotype and in the particular case of the F7 gene. The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions. Several sets of SNPs within the F7 gene region have been found to show a significant correlation with the FVII levels in blood. The proposed multi-site approach unveils combinations of SNPs that explain more significant information of the phenotype than their individual polymorphisms. MISS is able to find more correlations between SNPs and the phenotype than MLR and MECPM. Most of the marked SNPs appear in the literature as functional variants with real effect on the protein FVII levels in blood. AVAILABILITY: The code is available at http://sisbio.recerca.upc.edu/R/MISS_0.2.tar.gz

Regulatory sequence detection is a critical facet for understanding the cell mechanisms in order to coordinate the response to stimuli. Protein synthesis involves the binding of a transcription factor to specific sequences in a process related to the gene expression initiation. A characteristic of this binding process is that the same factor binds with different sequences placed along all genome. Thus, any computational approach shows many difficulties related with this variability observed from the binding sequences. This paper proposes the detection of transcription factor binding sites based on a parametric uncertainty measurement (Rényi entropy). This detection algorithm evaluates the variation on the total Rényi entropy of a set of sequences when a candidate sequence is assumed to be a true binding site belonging to the set. The efficiency of the method is measured in form of receiver operating characteristic (ROC) curves on different transcription factors fromSaccharomyces cerevisiaeorganism. The results are compared with other known motif detection algorithms such as Motif Discovery scan (MDscan) and multiple expectation–maximization (EM) for motif elicitation (MEME).

An effective data representation methodology on high-dimension feature spaces is presented, which allows a better interpretation of subjacent physiological phenomena (namely, cardiac behavior related to cardiovascular diseases), and is based on search criteria over a feature set resulting in an increase in the detection capability of ischemic pathologies, but also connecting these features with the physiologic representation of the ECG. The proposed dimension reduction scheme consists of three levels: projection, interpretation, and visualization. First, a hybrid algorithm is described that projects the multidimensional data to a lower dimension space, gathering the features that contribute similarly in the meaning of the covariance reconstruction in order to find information of clinical relevance over the initial training space. Next, an algorithm of variable selection is provided that further reduces the dimension, taking into account only the variables that offer greater class separability, and finally, the selected feature set is projected to a 2-D space in order to verify the performance of the suggested dimension reduction algorithm in terms of the discrimination capability for ischemia detection. The ECG recordings used in this study are from the European ST-T database and from the Universidad Nacional de Colombia database. In both cases, over 99% feature reduction was obtained, and classification precision was over 99% using a five-nearest-neighbor classifier (5-NN).

Finding association between genotypic differences and disease traits has become one of the main objectives in current genetic research. It has been published that some of the underlying factors in the dynamics of the coagulation process have a genetic compound, showing significant hereditability. This is the case of the Factor VII. In this work, we propose a method for selecting sets of Single Nucleotide Polymorphisms (SNPs) of the F7 gene that are significantly related with the phenotype (Factor VII levels). The methodology is applied to the sib pairs from the GAIT project sample. The method consists of an adapted Sequential Floating Feature Selection (SFFS) algorithm. This algorithm is applied with two relevance criteria, one linear and one non linear. The SNPs sets found with linear models are included in the sets found with non linear techniques. The results fit in with previous results in clinical area.

The Gene Ontology project is an effort to structure knowledge on biological products and processes by adding semantic information to them. This is done in a systematic way so that this additional information can be automatically processed. In this contribution a protein-protein interaction visualization algorithm is proposed, which combines protein interaction data with Gene Ontology semantic information. The information is integrated using a semantic distance measure defined in ontologies or taxonomies. Multidimensional scaling is applied to this measure and the output complements protein interaction data in building an interaction visualization map.

During the process of protein synthesis, transcription of DNA to messenger RNA starts with the binding of the transcription factors to the promoter. One of the issues on the prediction of transcription factor binding is that sequences corresponding to the binding present variability. In this manuscript a method for the detection of binding site is proposed, based on a parametric uncertainty measurement (Renyi entropy). This measurement is done through an estimation of the probability for each nucleotide avoiding any numerical representation of the nucleotides. We obtain values of the efficiency of the method as receiver operating characteristic curves found on ABF1 and ROX1 binding sites in chromosome I and XVI of the organism Saccharomyces cerevisiae.