Transcript normalization and segmentation of tiling array data.
For the analysis of transcriptional tiling arrays we have developed two methods based on state-of-the-art machine learning algorithms. First, we present a novel transcript normalization technique to alleviate the effect of oligonucleotide probe sequences on hybridization intensity. It is specifically designed to decrease the variability observed for individual probes complementary to the same transcript. Applying this normalization technique to Arabidopsis tiling arrays, we are able to reduce sequence biases and also significantly improve separation in signal intensity between exonic and intronic/intergenic probes. Our second contribution is a method for transcript mapping. It extends an algorithm proposed for yeast tiling arrays to the more challenging task of spliced transcript identification. When evaluated on raw versus normalized intensities our method achieves highest prediction accuracy when segmentation is performed on transcript-normalized tiling array data.
Zeller
Georg
G
Friedrich Miescher Laboratory of the Max Planck Society & Max Planck Institute for Developmental Biology, Dept. for Molecular Biology, Spemannstr 35 & 39, 72076 Tiibingen, Germany. Georg.Zeller@tuebingen.mpg.de
Henz
Stefan R
SR
Laubinger
Sascha
S
Weigel
Detlef
D
Rätsch
Gunnar
G
D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D001185:Artificial Intelligence; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D005091:Exons; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D018745:Genome, Plant; D008390:Markov Chains; D046228:Microarray Analysis / Q000706:statistics & numerical data*; D015335:Molecular Probes; D012044:Regression Analysis; D014158:Transcription, Genetic
D015335:Molecular Probes