Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data.
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.
Balwierz
Piotr J
PJ
Biozentrum, University of Basel, and Swiss Institute of Bioinformatics, Klingelbergstrasse 50/70, 4056-CH, Basel, Switzerland.
Carninci
Piero
P
Daub
Carsten O
CO
Kawai
Jun
J
Hayashizaki
Yoshihide
Y
Van Belle
Werner
W
Beisel
Christian
C
van Nimwegen
Erik
E
D000465:Algorithms; D000818:Animals; D001482:Base Composition; D002460:Cell Line; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D018899:CpG Islands / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data*; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data*; D011401:Promoter Regions, Genetic / Q000235:genetics*; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA / Q000379:methods / Q000706:statistics & numerical data*; D024363:Transcription Initiation Site