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

Balwierz, P. J. and Carninci, P. and Daub, C. O. and Kawai, J. and Hayashizaki, Y. and Van Belle, W. and Beisel, C. and van Nimwegen, E. J.. (2009) Methods for analyzing deep sequencing expression data : constructing the human and mouse promoterome with deepCAGE data. Genome biology, Vol. 10, H. 7 , R79.

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Official URL: http://edoc.unibas.ch/dok/A5259617

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Abstract

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 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.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (van Nimwegen)
UniBasel Contributors:van Nimwegen, Erik
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:BioMed Central
ISSN:1465-6906
Note:Publication type according to Uni Basel Research Database: Journal article
Last Modified:22 Mar 2012 14:21
Deposited On:22 Mar 2012 13:23

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