Method
Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data
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* Corresponding author: Erik van Nimwegen erik.vannimwegen@unibas.ch
1 Biozentrum, University of Basel, and Swiss Institute of Bioinformatics, Klingelbergstrasse 50/70, 4056-CH, Basel, Switzerland
2 RIKEN Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho Tsurumi-ku Yokohama, Kanagawa, 230-0045 Japan
3 Laboratory of Quantitative Genomics, Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule Zurich, Mattenstrasse 26, 4058 Basel, Switzerland
Genome Biology 2009, 10:R79 doi:10.1186/gb-2009-10-7-r79
Published: 22 July 2009Abstract
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.