Background: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection.

We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.

Genomic mapping of DNA replication origins (ORIs) in mammals provides a powerful means for understanding the regulatory complexity of our genome. Here we combine a genome-wide approach to identify preferential sites of DNA replication initiation at 0.4% of the mouse genome with detailed molecular analysis at distinct classes of ORIs according to their location relative to the genes. Our study reveals that 85% of the replication initiation sites in mouse embryonic stem (ES) cells are associated with transcriptional units. Nearly half of the identified ORIs map at promoter regions and, interestingly, ORI density strongly correlates with promoter density, reflecting the coordinated organisation of replication and transcription in the mouse genome. Detailed analysis of ORI activity showed that CpG island promoter-ORIs are the most efficient ORIs in ES cells and both ORI specification and firing efficiency are maintained across cell types. Remarkably, the distribution of replication initiation sites at promoter-ORIs exactly parallels that of transcription start sites (TSS), suggesting a co-evolution of the regulatory regions driving replication and transcription. Moreover, we found that promoter-ORIs are significantly enriched in CAGE tags derived from early embryos relative to all promoters. This association implies that transcription initiation early in development sets the probability of ORI activation, unveiling a new hallmark in ORI efficiency regulation in mammalian cells.

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We examined Type I error rates of Felsenstein's (1985; Am. Nat. 125:1-15) comparative method of phylogenetically independent contrasts when branch lengths are in error and the model of evolution is not Brownian motion. We used seven evolutionary models, six of which depart strongly from Brownian motion, to simulate the evolution of two continuously valued characters along two different phylogenies (15 and 49 species). First, we examined the performance of independent contrasts when branch lengths are distorted systematically, for example, by taking the square root of each branch segment. These distortions often caused inflated Type I error rates, but performance was almost always restored when branch length transformations were used. Next, we investigated effects of random errors in branch lengths. After the data were simulated, we added errors to the branch lengths and then used the altered phylogenies to estimate character correlations. Errors in the branches could be of two types: fixed, where branch lengths are either shortened or lengthened by a fixed fraction; or variable, where the error is a normal variate with mean zero and the variance is scaled to the length of the branch (so that expected error relative to branch length is constant for the whole tree). Thus, the error added is unrelated to the microevolutionary model. Without branch length checks and transformations, independent contrasts tended to yield extremely inflated and highly variable Type I error rates. Type I error rates were reduced, however, when branch lengths were checked and transformed as proposed by Garland et al. (1992; Syst. Biol. 41:18-32), and almost never exceeded twice the nominal P-value at alpha = 0.05. Our results also indicate that, if branch length transformations are applied, then the appropriate degrees of freedom for testing the significance of a correlation coefficient should, in general, be reduced to account for estimation of the best branch length transformation. These results extend those reported in Díaz-Uriarte and Garland (1996; Syst. Biol. 45:27-47), and show that, even with errors in branch lengths and evolutionary models different from Brownian motion, independent contrasts are a robust method for testing hypotheses of correlated evolution.