291 research outputs found

    A hidden Markov model approach for determining expression from genomic tiling micro arrays

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    BACKGROUND: Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. RESULTS: We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes [1]. Results can be downloaded and viewed from our web site [2]. CONCLUSION: The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity

    Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome

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    Tiling arrays make possible a large scale exploration of the genome thanks to probes which cover the whole genome with very high density until 2 000 000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work we propose to consider simultaneously both questions as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge like annotation and spatial dependence between probes. Since probes are not biologically relevant units we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and the region classification

    Tilescope: online analysis pipeline for high-density tiling microarray data

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    Tilescope is a fully integrated and automated new data-processing pipeline for analyzing high-density tiling-array data

    An efficient pseudomedian filter for tiling microrrays

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    <p>Abstract</p> <p>Background</p> <p>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 <it>O</it>(<it>n</it><sup>2</sup>log<it>n</it>) 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.</p> <p>Results</p> <p>We therefore implemented Monahan's HLQEST algorithm that reduces the runtime complexity for computing the pseudomedian of <it>n </it>numbers to <it>O</it>(<it>n</it>log<it>n</it>) from <it>O</it>(<it>n</it><sup>2</sup>log<it>n</it>). 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 <it>O</it>(log <it>n</it>) inserts and deletes. We illustrate the favorable scaling properties of our algorithms with both time complexity analysis and benchmarking on synthetic datasets.</p> <p>Conclusion</p> <p>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 <url>http://tiling.gersteinlab.org/pseudomedian/</url>.</p

    Transcriptional landscape estimation from tiling array data using a model of signal shift and drift

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    Motivation: High-density oligonucleotide tiling array technology holds the promise of a better description of the complexity and the dynamics of transcriptional landscapes. In organisms such as bacteria and yeasts, transcription can be measured on a genome-wide scale with a resolution >25 bp. The statistical models currently used to handle these data remain however very simple, the most popular being the piecewise constant Gaussian model with a fixed number of breakpoints

    Parameter estimation for robust HMM analysis of ChIP-chip data

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    Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s

    Genome-wide Copy Number Profiling on High-density Bacterial Artificial Chromosomes, Single-nucleotide Polymorphisms, and Oligonucleotide Microarrays: A Platform Comparison based on Statistical Power Analysis

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    Recently, comparative genomic hybridization onto bacterial artificial chromosome (BAC) arrays (array-based comparative genomic hybridization) has proved to be successful for the detection of submicroscopic DNA copy-number variations in health and disease. Technological improvements to achieve a higher resolution have resulted in the generation of additional microarray platforms encompassing larger numbers of shorter DNA targets (oligonucleotides). Here, we present a novel method to estimate the ability of a microarray to detect genomic copy-number variations of different sizes and types (i.e. deletions or duplications). We applied our method, which is based on statistical power analysis, to four widely used high-density genomic microarray platforms. By doing so, we found that the high-density oligonucleotide platforms are superior to the BAC platform for the genome-wide detection of copy-number variations smaller than 1 Mb. The capacity to reliably detect single copy-number variations below 100 kb, however, appeared to be limited for all platforms tested. In addition, our analysis revealed an unexpected platform-dependent difference in sensitivity to detect a single copy-number loss and a single copy-number gain. These analyses provide a first objective insight into the true capacities and limitations of different genomic microarrays to detect and define DNA copy-number variations

    Use of tiling array data and RNA secondary structure predictions to identify noncoding RNA genes

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    <p>Abstract</p> <p>Background</p> <p>Within the last decade a large number of noncoding RNA genes have been identified, but this may only be the tip of the iceberg. Using comparative genomics a large number of sequences that have signals concordant with conserved RNA secondary structures have been discovered in the human genome. Moreover, genome wide transcription profiling with tiling arrays indicate that the majority of the genome is transcribed.</p> <p>Results</p> <p>We have combined tiling array data with genome wide structural RNA predictions to search for novel noncoding and structural RNA genes that are expressed in the human neuroblastoma cell line SK-N-AS. Using this strategy, we identify thousands of human candidate RNA genes. To further verify the expression of these genes, we focused on candidate genes that had a stable hairpin structures or a high level of covariance. Using northern blotting, we verify the expression of 2 out of 3 of the hairpin structures and 3 out of 9 high covariance structures in SK-N-AS cells.</p> <p>Conclusion</p> <p>Our results demonstrate that many human noncoding, structured and conserved RNA genes remain to be discovered and that tissue specific tiling array data can be used in combination with computational predictions of sequences encoding structural RNAs to improve the search for such genes.</p

    Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

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    Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)

    Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling)

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    Background: Existing statistical methods for tiling array transcriptome data either focus on transcript discovery in one biological or experimental condition or on the detection of differential expression between two conditions. Increasingly often, however, biologists are interested in time-course studies, studies with more than two conditions or even multiple-factor studies. As these studies are currently analyzed with the traditional microarray analysis techniques, they do not exploit the genome-wide nature of tiling array data to its full potential. Results: We present an R Bioconductor package, waveTiling, which implements a wavelet-based model for analyzing transcriptome data and extends it towards more complex experimental designs. With waveTiling the user is able to discover (1) group-wise expressed regions, (2) differentially expressed regions between any two groups in single-factor studies and in (3) multifactorial designs. Moreover, for time-course experiments it is also possible to detect (4) linear time effects and (5) a circadian rhythm of transcripts. By considering the expression values of the individual tiling probes as a function of genomic position, effect regions can be detected regardless of existing annotation. Three case studies with different experimental set-ups illustrate the use and the flexibility of the model-based transcriptome analysis. Conclusions: The waveTiling package provides the user with a convenient tool for the analysis of tiling array trancriptome data for a multitude of experimental set-ups. Regardless of the study design, the probe-wise analysis allows for the detection of transcriptional effects in both exonic, intronic and intergenic regions, without prior consultation of existing annotation
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