PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Transcript Normalization and Segmentation of Tiling Array Data
Georg Zeller, Stefan Henz, Sascha Laubinger, Detlef Weigel and Gunnar Raetsch
In: PSB 2008, 4-8 Jan 2008, Hawaii, USA.

Abstract

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3131
Deposited By:Gunnar Raetsch
Deposited On:21 December 2007