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

Piotr J Balwierz; Piero Carninci; Carsten O Daub; Jun Kawai; Yoshihide Hayashizaki; Werner Van Belle; Christian Beisel; Erik van Nimwegen

doi:10.1186/gb-2009-10-7-r79

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

Genome Biol. 2009;10(7):R79. doi: 10.1186/gb-2009-10-7-r79. Epub 2009 Jul 22.

Authors

Piotr J Balwierz¹, Piero Carninci, Carsten O Daub, Jun Kawai, Yoshihide Hayashizaki, Werner Van Belle, Christian Beisel, Erik van Nimwegen

Affiliation

¹ Biozentrum, University of Basel, and Swiss Institute of Bioinformatics, Klingelbergstrasse 50/70, 4056-CH, Basel, Switzerland.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Base Composition
Cell Line
Cluster Analysis
Computational Biology / methods
CpG Islands / genetics
Gene Expression Profiling / methods
Gene Expression Profiling / statistics & numerical data*
Genome-Wide Association Study / methods
Humans
Mice
Oligonucleotide Array Sequence Analysis / methods
Oligonucleotide Array Sequence Analysis / statistics & numerical data*
Promoter Regions, Genetic / genetics*
Reproducibility of Results
Sequence Analysis, DNA / methods
Sequence Analysis, DNA / statistics & numerical data*
Transcription Initiation Site*