MOTIVATION: Increases in microarray feature density allow the construction of so-called tiling microarrays. These arrays, or sets of arrays, contain probes targeting regions of sequenced genomes at regular genomic intervals. The unbiased nature of this approach allows for the identification of novel transcribed sequences, the localization of transcription factor binding sites (ChIP-chip), and high resolution comparative genomic hybridization, among other uses. These applications are quickly growing in popularity as tiling microarrays become more affordable. To reach maximum utility, the tiling microarray platform needs be developed to the point that 1 nt resolutions are achieved and that we have confidence in individual measurements taken at this fine of resolution. Any biases in tiling array signals must be systematically removed to achieve this goal. RESULTS: Towards this end, we investigated the importance of probe sequence composition on the efficacy of tiling microarrays for identifying novel transcription and transcription factor binding sites. We found that intensities are highly sequence dependent and can greatly influence results. We developed three metrics for assessing this sequence dependence and use them in evaluating existing sequence-based normalizations from the tiling microarray literature. In addition, we applied three new techniques for addressing this problem; one method, adapted from similar work on GeneChip brand microarrays, is based on modeling array signal as a linear function of probe sequence, the second method extends this approach by iterative weighting and re-fitting of the model, and the third technique extrapolates the popular quantile normalization algorithm for between-array normalization to probe sequence space. These three methods perform favorably to existing strategies, based on the metrics defined here. AVAILABILITY: http://tiling.gersteinlab.org/sequence_effects/

BACKGROUND: Tiling microarrays are becoming an essential technology in the functional genomics toolbox. They have been applied to the tasks of novel transcript identification, elucidation of transcription factor binding sites, detection of methylated DNA and several other applications in several model organisms. These experiments are being conducted at increasingly finer resolutions as the microarray technology enjoys increasingly greater feature densities. The increased densities naturally lead to increased data analysis requirements. Specifically, the most widely employed algorithm for tiling array analysis involves smoothing observed signals by computing pseudomedians within sliding windows, a O(n2logn) calculation in each window. This poor time complexity is an issue for tiling array analysis and could prove to be a real bottleneck as tiling microarray experiments become grander in scope and finer in resolution. RESULTS: We therefore implemented Monahan's HLQEST algorithm that reduces the runtime complexity for computing the pseudomedian of n numbers to O(nlogn) from O(n2logn). For a representative tiling microarray dataset, this modification reduced the smoothing procedure's runtime by nearly 90%. We then leveraged the fact that elements within sliding windows remain largely unchanged in overlapping windows (as one slides across genomic space) to further reduce computation by an additional 43%. This was achieved by the application of skip lists to maintaining a sorted list of values from window to window. This sorted list could be maintained with simple O(log n) inserts and deletes. We illustrate the favorable scaling properties of our algorithms with both time complexity analysis and benchmarking on synthetic datasets. CONCLUSION: Tiling microarray analyses that rely upon a sliding window pseudomedian calculation can require many hours of computation. We have eased this requirement significantly by implementing efficient algorithms that scale well with genomic feature density. This result not only speeds the current standard analyses, but also makes possible ones where many iterations of the filter may be required, such as might be required in a bootstrap or parameter estimation setting. Source code and executables are available at http://tiling.gersteinlab.org/pseudomedian/.

MOTIVATION: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. METHODOLOGY: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). RESULTS: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments.