Bioinformatics Advance Access published online on March 25, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm052
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Assessing the need for sequence-based normalization in tiling microarray experiments
1Interdepartmental Program in Computational Biology and Bioinformatics, 2Department of Molecular Biophysics and Biochemistrym, 3Department of Computer Science. Yale University, New Haven, CT 06520, USA
*To whom correspondence should be addressed. Thomas E. Royce, E-mail: thomas.royce{at}yale.edu
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Abstract |
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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 1nt 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/
Associate Editor: Dr. Trey Ideker
Received on October 3, 2006; revised on December 28, 2006; accepted on February 8, 2007
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