TileMap

TileMap: create chromosomal map of tiling array hybridizations. Motivation: Tiling array is a new type of microarray that can be used to survey genomic transcriptional activities and transcription factor binding sites at high resolution. The goal of this paper is to develop effective statistical tools to identify genomic loci that show transcriptional or protein binding patterns of interest. Results: A two-step approach is proposed and is implemented in TileMap. In the first step, a test-statistic is computed for each probe based on a hierarchical empirical Bayes model. In the second step, the test-statistics of probes within a genomic region are used to infer whether the region is of interest or not. Hierarchical empirical Bayes model shrinks variance estimates and increases sensitivity of the analysis. It allows complex multiple sample comparisons that are essential for the study of temporal and spatial patterns of hybridization across different experimental conditions. Neighboring probes are combined through a moving average method (MA) or a hidden Markov model (HMM). Unbalanced mixture subtraction is proposed to provide approximate estimates of false discovery rate for MA and model parameters for HMM. Availability: TileMap is freely available at http://biogibbs.stanford.edu/ jihk/TileMap/index.htm


References in zbMATH (referenced in 13 articles )

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  1. Dazard, Jean-Eudes; Rao, J. Sunil: Joint adaptive mean-variance regularization and variance stabilization of high dimensional data (2012)
  2. Mo, Qianxing; Liang, Faming: Bayesian modeling of ChIP-chip data through a high-order Ising model (2010)
  3. Wang, Dong: Modeling epigenetic modifications under multiple treatment conditions (2010)
  4. Datta, Debayan; Zhao, Hongyu: Effect of false positive and false negative rates on inference of binding target conservation across different conditions and species from chip-chip data (2009) ioport
  5. Gelfond, Jonathan A. L.; Gupta, Mayetri; Ibrahim, Joseph G.: A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data (2009)
  6. Spyrou, Christiana; Stark, Rory; Lynch, Andy G.; Tavaré, Simon: Bayespeak: Bayesian analysis of chip-seq data (2009) ioport
  7. Sun, Wei; Buck, Michael J.; Patel, Mukund; Davis, Ian J.: Improved chip-chip analysis by a mixture model approach (2009) ioport
  8. Gottardo, Raphael; Li, Wei; Johnson, W. Evan; Liu, X. Shirley: A flexible and powerful Bayesian hierarchical model for ChIP-chip experiments (2008)
  9. Morris, Carl N.: Comment: “Microarrays, empirical Bayes and the two-groups model” (2008)
  10. Keleş, Sündüz: Mixture modeling for genome-wide localization of transcription factors (2007)
  11. Toedling, Joern; Sklyar, Oleg; Huber, Wolfgang: Ringo - an R/Bioconductor package for analyzing chip-chip readouts (2007) ioport
  12. Zheng, Ming; Barrera, Leah O.; Ren, Bing; Wu, Ying Nian: ChIP-chip: data, model, and analysis (2007)
  13. Ji, Hongkai; Wong, Wing Hung: Computational biology: toward deciphering gene regulatory information in mammalian genomes (2006)