Integrative analyses for omics data: a bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements

Journal of Toxicology and Environmental Health - Part A: Current Issues

Volumn 75, Issue 8-10, 2012, Pages 461-470

  Schäfer, Martin ^a   Lkhagvasuren, Otgonzul ^a   Klein, Hans Ulrich ^b   Elling, Christian ^c   Wüstefeld, Torsten ^d,e   Müller Tidow, Carsten ^c   Zender, Lars ^d,e   Koschmieder, Steffen ^f   Dugas, Martin ^b   Ickstadt, Katja ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

^b UNIVERSITY OF MÜNSTER   (Germany)

^c UNIVERSITY HOSPITAL MÜNSTER   (Germany)

^d HELMHOLTZ CENTRE FOR INFECTION RESEARCH   (Germany)

^e HANNOVER MEDICAL SCHOOL   (Germany)

^f UNIVERSITY HOSPITAL RWTH AACHEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BCR ABL PROTEIN; DNA FRAGMENT;

ANIMAL TISSUE; ARTICLE; BAYES THEOREM; CHROMATIN IMMUNOPRECIPITATION; CHRONIC MYELOID LEUKEMIA; CONTROLLED STUDY; CORRELATION COEFFICIENT; EPIGENETICS; GENE ACTIVATION; GENE SEQUENCE; GENERAL DEVICE; GENOMICS; HISTONE ACETYLATION; HISTONE MODIFICATION; MICROARRAY ANALYSIS; MOUSE; NONHUMAN; ONCOGENE; PRIORITY JOURNAL; PROTEOMICS; TRANSCRIPTOMICS;

ALGORITHMS; ANIMALS; BAYES THEOREM; CAPILLARY ELECTROCHROMATOGRAPHY; CHROMATIN IMMUNOPRECIPITATION; DATA INTERPRETATION, STATISTICAL; DNA; EPIGENOMICS; FUSION PROTEINS, BCR-ABL; GENOMICS; HEMATOPOIETIC STEM CELLS; HISTONES; LEUKEMIA, MYELOGENOUS, CHRONIC, BCR-ABL POSITIVE; MARKOV CHAINS; MICE; MICROARRAY ANALYSIS; MODELS, STATISTICAL; MONTE CARLO METHOD; ONCOGENES; PROTEOMICS; SAMPLE SIZE; SEQUENCE ANALYSIS, DNA; TRANSDUCTION, GENETIC;

EID: 84862508196     PISSN: 15287394     EISSN: 10872620     Source Type: Journal    
DOI: 10.1080/15287394.2012.674914     Document Type: Article

References (18)

1. Bolstad, B. M., Irizarry, R. A., Åstrand, M., and Speed, T. P. 2003. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19: 185-93.
2. Bröet, P., and Richardson, S. 2006. Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model. Bioinformatics 22: 911-18.
3. Elling, C., Erben, P., Walz, C., Frickenhaus, M., Schemionek, M., Stehling, M., Serve, H., Cross, N. C. P., Hochhaus, A., Hofmann, W.-K., Berdel, W. E., Müller-Tidow, C., Reiter, A., and Koschmieder, S. 2011. Novel imatinib-sensitive PDGFRA-activating point mutations in hypereosinophilic syndrome induce growth factor independence and leukemia-like disease. Blood 117: 2935-43.
4. Gallegos R., Mariëlle I., Floor, K., Roepman, P., Rodriguez, J. A., Meijer, G. A., Mooi, W. J., Jassem, E., Niklinski, J., Muley, T., van Zandwijk, N., Smit, E. F., Beebe, K., Neckers, L., Ylstra, B., and Giaccone, G. 2008. Integration of gene dosage and gene expression in non-small cell lung cancer, identification of HSP90 as potential target. PLoS ONE 3: e0001722.
5. Ho, W. K. J., Bishop, E., Karchenko, P. V., Negré, N., White, K. P., and Park, P. J. 2011. ChIP-chip versus ChIP-seq. Lessons for experimental design and data analysis. BMC Genomics 12: 134.
6. Kuan, P. F., Chung, D., Pan, G., Thomson, J. A., Stewart, R., and Keleş, S. 2011. A statistical framework for the analysis of ChIP-seq data. J. Am. Stat. Assoc. 106: 891-903.
7. Li, H., and Durbin, R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25: 1754-60.
8. Mo, Q., and Liang, F. 2010. Bayesian modeling of ChIP-chip data through a high-order Ising model. Biometrics 66: 1284-94.
9. Mockler, T. C., and Ecker, J. R. 2005. Applications of DNA tiling arrays for whole-genome analysis. Genomics 85: 1-15.
10. Ortiz-Estevez, M., De las Rivas, J., Fontanillo, C., and Rubio, A. 2011. Segmentation of genomic and transcriptomic microarrays data reveals major correlation between DNA copy number aberrations and gene-loci expression. Genomics 97: 86-93.
11. Park, P. J. 2009. ChIP-seq: Advantages and challenges of a maturing technology. Nat. Rev. Genet. 10: 669-80.
12. R Developent Core Team. 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
13. Schäfer, M., Schwender, H., Merk, S., Haferlach, C., Ickstadt, K., and Dugas, M. 2009. Integrated analysis of copy number alterations gene expression: a bivariate assessment of equally directed abnormalities. Bioinformatics 25: 3228-35.
14. Spiegelhalter, D., Thomas, A., Best, N., Lunn, D. 2003. WinBUGS User Manual, Version 1.4.Available at http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/ manual14.pdf (as accessed on August 23rd, 2011).
15. van Wieringen W. N., and van de Wiel M. A. 2009. Nonparametric testing for DNA copy number induced differential mRNA gene expression. Biometrics 65: 19-29.
16. Vega, V. B., Cheung, E., Palanisamy, N., and Sung, W.-K. 2009. Inherent signals in sequencing-based chromatin-immunoprecipitation control libraries. PLoS One 4: e5241.
17. Wei, P., and Pan, W. 2008. Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model. Bioinformatics 24: 404-11.
18. Zacher, B., Kuan, P. F., and Tresch, A. 2010. Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data. BMC Bioinformatics 11: 194. doi:10.1186/1471-2105-11-194.