Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

MEDVIR: 3D visual interface applied to gene profile analisys.

The origins for this work arise in response to the increasing need for biologists and doctors to obtain tools for visual analysis of data. When dealing with multidimensional data, such as medical data, the traditional data mining techniques can be a tedious and complex task, even to some medical experts. Therefore, it is necessary to develop useful visualization techniques that can complement the expert’s criterion, and at the same time visually stimulate and make easier the process of obtaining knowledge from a dataset. Thus, the process of interpretation and understanding of the data can be greatly enriched. Multidimensionality is inherent to any medical data, requiring a time-consuming effort to get a clinical useful outcome. Unfortunately, both clinicians and biologists are not trained in managing more than four dimensions. Specifically, we were aimed to design a 3D visual interface for gene profile analysis easy in order to be used both by medical and biologist experts. In this way, a new analysis method is proposed: MedVir. This is a simple and intuitive analysis mechanism based on the visualization of any multidimensional medical data in a three dimensional space that allows interaction with experts in order to collaborate and enrich this representation. In other words, MedVir makes a powerful reduction in data dimensionality in order to represent the original information into a three dimensional environment. The experts can interact with the data and draw conclusions in a visual and quickly way.

The AtCathB3 gene, encoding a cathepsin B-like protease, is expressed during germination of Arabidopsis thaliana and transcriptionally repressed by the basic leucine zipper protein GBF1.

Protein hydrolysis plays an important role during seed germination and post-germination seedling establishment. In Arabidopsis thaliana, cathepsin B-like proteases are encoded by a gene family of three members, but only the AtCathB3 gene is highly induced upon seed germination and at the early post-germination stage. Seeds of a homozygous T-DNA insertion mutant in the AtCathB3 gene have, besides a reduced cathepsin B activity, a slower germination than the wild type. To explore the transcriptional regulation of this gene, we used a combined phylogenetic shadowing approach together with a yeast one-hybrid screening of an arrayed library of approximately 1200 transcription factor open reading frames from Arabidopsis thaliana. We identified a conserved CathB3-element in the promoters of orthologous CathB3 genes within the Brassicaceae species analysed, and, as its DNA-interacting protein, the G-Box Binding Factor1 (GBF1). Transient overexpression of GBF1 together with a PAtCathB3::uidA (β-glucuronidase) construct in tobacco plants revealed a negative effect of GBF1 on expression driven by the AtCathB3 promoter. In stable P35S::GBF1 lines, not only was the expression of the AtCathB3 gene drastically reduced, but a significant slower germination was also observed. In the homozygous knockout mutant for the GBF1 gene, the opposite effect was found. These data indicate that GBF1 is a transcriptional repressor of the AtCathB3 gene and affects the germination kinetics of Arabidopsis thaliana seeds. As AtCathB3 is also expressed during post-germination in the cotyledons, a role for the AtCathB3-like protease in reserve mobilization is also inferred.