MOTIVATION: Validation and reproducibility of results is a central and pressing issue in genomics. Several recent embarrassing incidents involving the irreproducibility of high-profile studies have illustrated the importance of this issue and the need for rigorous methods for the assessment of reproducibility. RESULTS: Here, we describe an existing statistical model that is very well suited to this problem. We explain its utility for assessing the reproducibility of validation experiments, and apply it to a genome-scale study of adenosine deaminase acting on RNA (ADAR)-mediated RNA editing in Drosophila. We also introduce a statistical method for planning validation experiments that will obtain the tightest reproducibility confidence limits, which, for a fixed total number of experiments, returns the optimal number of replicates for the study. AVAILABILITY: Downloadable software and a web service for both the analysis of data from a reproducibility study and for the optimal design of these studies is provided at http://ccmbweb.ccv.brown.edu/reproducibility.html .

MOTIVATION: A recently developed DNaseI assay has given us our first genome-wide view of chromatin structure. In addition to cataloging DNaseI hypersensitive sites, these data allows us to more completely characterize overall features of chromatin accessibility. We employed a Bayesian hierarchical change-point model (CPM), a generalization of a hidden Markov Model (HMM), to characterize tiled microarray DNaseI sensitivity data available from the ENCODE project. RESULTS: Our analysis shows that the accessibility of chromatin to cleavage by DNaseI is well described by a four state model of local segments with each state described by a continuous mixture of Gaussian variables. The CPM produces a better fit to the observed data than the HMM. The large posterior probability for the four-state CPM suggests that the data falls naturally into four classes of regions, which we call major and minor DNaseI hypersensitive sites (DHSs), regions of intermediate sensitivity, and insensitive regions. These classes agree well with a model of chromatin in which local disruptions (DHSs) are concentrated within larger domains of intermediate sensitivity, the accessibility islands. The CPM assigns 92% of the bases within the ENCODE regions to the insensitive regions. The 5.8% of the bases that are in regions of intermediate sensitivity are clearly enriched in functional elements, including genes and activating histone modifications, while the remaining 2.2% of the bases in hypersensitive regions are very strongly enriched in these elements. AVAILABILITY: The CPM software is available upon request from the authors.

MOTIVATION: Identification of functionally conserved regulatory elements in sequence data from closely related organisms is becoming feasible, due to the rapid growth of public sequence databases. Closely related organisms are most likely to have common regulatory motifs; however, the recent speciation of such organisms results in the high degree of correlation in their genome sequences, confounding the detection of functional elements. Additionally, alignment algorithms that use optimization techniques are limited to the detection of a single alignment that may not be representative. Comparative-genomics studies must be able to address the phylogenetic correlation in the data and efficiently explore the alignment space, in order to make specific and biologically relevant predictions. RESULTS: We describe here a Gibbs sampler that employs a full phylogenetic model and reports an ensemble centroid solution. We describe regulatory motif detection using both simulated and real data, and demonstrate that this approach achieves improved specificity, sensitivity, and positive predictive value over non-phylogenetic algorithms, and over phylogenetic algorithms that report a maximum likelihood solution. AVAILABILITY: The software is freely available at http://bayesweb.wadsworth.org/gibbs/gibbs.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.