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

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 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

BiologyComputational biologyDeep sequencingGeneticsDNA sequencingGeneTranscription (linguistics)Normalization (sociology)GenomicsGenomeHuman geneticsHuman genome

MeSH Terms

AlgorithmsAnimalsBase CompositionCell LineCluster AnalysisComputational BiologyCpG IslandsGene Expression ProfilingGenome-Wide Association StudyHumansMiceOligonucleotide Array Sequence AnalysisPromoter RegionsGeneticReproducibility of ResultsSequence AnalysisDNATranscription Initiation Site

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Publication Info

Year: 2009
Type: article
Volume: 10
Issue: 7
Citations: 142
Access: Closed

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APA Style

                            
                                
                                    Piotr J. Balwierz, 
                                
                                    Piero Carninci, 
                                
                                    Carsten O. Daub
                                
                                et al.
                            
                            (2009). 
                            Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data. 
                            Genome biology
                            , 10
                            (7)
                            .
                            https://doi.org/10.1186/gb-2009-10-7-r79
                        

Identifiers

DOI: 10.1186/gb-2009-10-7-r79
PMID: 19624849
PMCID: PMC2728533

Data Quality

Data completeness: 86%