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

Kristof De Beuf (UGent) , Peter Pipelers (UGent) , Megan Andriankaja (UGent) , Olivier Thas (UGent) , Dirk Inzé (UGent) , Ciprian Crainiceanu and Lieven Clement (UGent)
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Biotechnology for a sustainable economy (Bio-Economy)
Project
Bioinformatics: from nucleotids to networks (N2N)
Abstract
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.
Keywords
TIME, MODELS, MICROARRAYS, GENES, RESOURCE, IDENTIFICATION, GENOME, CELL-CYCLE, TRANSCRIPTIONAL LANDSCAPE, ARABIDOPSIS-THALIANA

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Chicago
De Beuf, Kristof, Peter Pipelers, Megan Andriankaja, Olivier Thas, Dirk Inzé, Ciprian Crainiceanu, and Lieven Clement. 2012. “Analysis of Tiling Array Expression Studies with Flexible Designs in Bioconductor (waveTiling).” Bmc Bioinformatics 13.
APA
De Beuf, K., Pipelers, P., Andriankaja, M., Thas, O., Inzé, D., Crainiceanu, C., & Clement, L. (2012). Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling). BMC BIOINFORMATICS, 13.
Vancouver
1.
De Beuf K, Pipelers P, Andriankaja M, Thas O, Inzé D, Crainiceanu C, et al. Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling). BMC BIOINFORMATICS. 2012;13.
MLA
De Beuf, Kristof et al. “Analysis of Tiling Array Expression Studies with Flexible Designs in Bioconductor (waveTiling).” BMC BIOINFORMATICS 13 (2012): n. pag. Print.
@article{3136190,
  abstract     = {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.},
  articleno    = {234},
  author       = {De Beuf, Kristof and Pipelers, Peter and Andriankaja, Megan and Thas, Olivier and Inzé, Dirk and Crainiceanu, Ciprian and Clement, Lieven},
  issn         = {1471-2105},
  journal      = {BMC BIOINFORMATICS},
  keywords     = {TIME,MODELS,MICROARRAYS,GENES,RESOURCE,IDENTIFICATION,GENOME,CELL-CYCLE,TRANSCRIPTIONAL LANDSCAPE,ARABIDOPSIS-THALIANA},
  language     = {eng},
  pages        = {14},
  title        = {Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling)},
  url          = {http://dx.doi.org/10.1186/1471-2105-13-234},
  volume       = {13},
  year         = {2012},
}

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