Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect

The archaeal P-type ATPases.

A phylogenetic analysis was carried out of a total of 58 P-type ATPases encoded within the genomes of 20 archaea species. Members from six subfamilies were identified including: putative metal-, proton-, calcium-, sodium/potassium-, potassium-, and magnesium/nickel-transporting ATPases. Six novel putative proton-ATPases from archaea species growing under different temperature and pH conditions were shown to have shorter N- and C-termini than those of orthologous yeast or plant proton-ATPases. Moreover recent biochemical data are reviewed that report functional expression of putative archaea metal- or proton-ATPases in bacteria or yeast

Emergence of Species-Specific Transporters During Evolution of the Hemiascomycete Phylum

We have traced the evolution patterns of 2480 transmembrane transporters from five complete genome sequences spanning the entire Hemiascomycete phylum: Saccharomyces cerevisiae, Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii, and Yarrowia lipolytica. The use of nonambiguous functional and phylogenetic criteria derived from the TCDB classification system has allowed the identification within the Hemiascomycete phylum of 97 small phylogenetic transporter subfamilies comprising a total of 355 transporters submitted to four distinct evolution patterns named “ubiquitous,” “species specific,” “phylum gains and losses,” or “homoplasic.” This analysis identifies the transporters that contribute to the emergence of species during the evolution of the Hemiascomycete phylum and may aid in establishing novel phylogenetic criteria for species classification

Phylogenetic classification of transporters and other membrane proteins from Saccharomyces cerevisiae.

On the basis of functional and phylogenetic criteria, we have identified a total of 229 subfamilies and 111 singletons predicted to carry out transport or other membrane functions in Saccharomyces cerevisiae. We have extended the Transporter Classification (TC) and created a Membrane Classification (MC) for non-transporter membrane proteins. Using the preliminary phylogenetic digits X, Y, Z (for new families, subfamilies, and clusters, respectively), we allocated a five-digit number to 850 proteins predicted to contain more than two transmembrane domains. Compared with a previous TC of the yeast genome, we classified an additional set of 538 membrane proteins (transporters and non-transporters) and identified 111 novel phylogenetic subfamilies

A benchmark for statistical microarray data analysis that preserves actual biological and technical variance

<p>Abstract</p> <p>Background</p> <p>Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.</p> <p>Results</p> <p>Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.</p> <p>Conclusions</p> <p>Performance analysis refined the results from benchmarks published previously.</p> <p>We show that the Shrinkage <it>t </it>test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized <it>t </it>test and the Window <it>t </it>test performed slightly better.</p> <p>Availability</p> <p>The R scripts used for the analysis are available at <url>http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/</url>.</p