%0 Journal Article
%T A normalization strategy for comparing tag count data
%A Koji Kadota
%A Tomoaki Nishiyama
%A Kentaro Shimizu
%J Algorithms for Molecular Biology
%D 2012
%I BioMed Central
%R 10.1186/1748-7188-7-5
%X We describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.Our results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.Development of next-generation sequencing technologies has enabled biological features such as gene expression and histone modification to be quantified as tag count data by ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses [1,2]. Different from hybridization-based microarray technologies [3,4], sequencing-based technologies do not require prior information about the genome or transcriptome sequences of the samples of interest [5]. Therefore, researchers can profile the expression of not only well-annotated model organisms but also poorly annotated non-model organisms. RNA-seq in such organisms enables the gene structures and expression levels to be determined.One important task for RNA-seq is to identify differential expression (DE) for genes or transcripts. Similar to microarray analysis, we typically start the analysis with a so-ca
%U http://www.almob.org/content/7/1/5