Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data.

Piotr J. Balwierz^1,2
Piero Carninci³
Carsten Daub³
Jun Kawai³
Werner Van Belle⁴ - werner@yellowcouch.org, werner.van.belle@gmail.com
Christian Beisel⁴ - christian.beisel@bsse.ethz.ch
Erik Van Nimwegen^1,2* - erik.vannimwegen@unibas.ch

1- Biozentrum; University of Basel; Klingelbergstrasse 50/70, 4056-CH, Basel; Switzerland
2- Swiss Institute of Bioinformatics;
3- RIKEN Omics Science Center, RIKEN Yokohama Institute; 1-7-22 Suehiro-cho Tsurumi-ku Yokohama, Kanagawa, 230-0045 Japan
4- Deep Sequencing Unit Department of Biosystems Science and Engineering ETH Zurich; Mattenstrasse 24, Building 1058, Basel; Switzerland
* Corresponding author

Abstract :  With the advent of ultra high-throughput sequencing technologies, increasingly researchers are turning to deep sequencing for gene expression studies. Here we present a set of rigorous methods for normalization, quantification of noise, and co-expression analysis of deep sequencing data. Using these methods on 122 cap analysis of gene expression (CAGE) samples of transcription start sites, we construct genome-wide 'promoteromes' in human and mouse consisting of a three-tiered hierarchy of transcription start sites, transcription start clusters, and transcription start regions.

Keywords:  deep sequencinging promotoreome sequencing by synthesis analytical methods
Reference:  Piotr J. Balwierz, Piero Carninci, Carsten Daub, Jun Kawai, Werner Van Belle, Christian Beisel, Erik Van Nimwegen; Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data.; Genome Biology; Biomed Central; volume 10; nr 7; pages R79; July 2009
Files: gb-2009-10-7-r79.pdf
See also:
Article at Genome Biology