BackgroundDifferential expression analysis based on “next-generation” sequencing technologies is a fundamental means of studying RNA expression. We recently developed a multi-step normalization method (called TbT) for two-group RNA-seq data with replicates and demonstrated that the statistical methods available in four R packages (edgeR, DESeq, baySeq, and NBPSeq) together with TbT can produce a well-ranked gene list in which true differentially expressed genes (DEGs) are top-ranked and non-DEGs are bottom ranked. However, the advantages of the current TbT method come at the cost of a huge computation time. Moreover, the R packages did not have normalization methods based on such a multi-step strategy.ResultsTCC (an acronym for Tag Count Comparison) is an R package that provides a series of functions for differential expression analysis of tag count data. The package incorporates multi-step normalization methods, whose strategy is to remove potential DEGs before performing the data normalization. The normalization function based on this DEG elimination strategy (DEGES) includes (i) the original TbT method based on DEGES for two-group data with or without replicates, (ii) much faster methods for two-group data with or without replicates, and (iii) methods for multi-group comparison. TCC provides a simple unified interface to perform such analyses with combinations of functions provided by edgeR, DESeq, and baySeq. Additionally, a function for generating simulation data under various conditions and alternative DEGES procedures consisting of functions in the existing packages are provided. Bioinformatics scientists can use TCC to evaluate their methods, and biologists familiar with other R packages can easily learn what is done in TCC.ConclusionDEGES in TCC is essential for accurate normalization of tag count data, especially when up- and down-regulated DEGs in one of the samples are extremely biased in their number. TCC is useful for analyzing tag count data in various scenarios ranging from unbiased to extremely biased differential expression. TCC is available at http://www.iu.a.u-tokyo.ac.jp/~kadota/TCC/ and will appear in Bioconductor (http://bioconductor.org/) from ver. 2.13.

CLOCK is a positive component of a transcription/ translation-based negative feedback loop of the central circadian oscillator in the suprachiasmatic nucleus in mammals. To examine CLOCK-regulated circadian transcription in peripheral tissues, we performed microarray analyses using liver RNA isolated from Clock mutant mice. We also compared expression profiles with those of Cryptochromes (Cry1 and Cry2) double knockout mice. We identified more than 100 genes that fluctuated from day to night and of which expression levels were decreased in Clock mutant mice. In Cry-deficient mice, the expression levels of most CLOCK-regulated genes were elevated to the upper range of normal oscillation. Most of the screened genes had a CLOCK/BMAL1 binding site (E box) in the 5-flanking region. We found that CLOCK was absolutely concerned with the circadian transcription of one type of liver genes (such as DBP, TEF, and Usp2) and partially with another (such as mPer1, mPer2, mDec1, Nocturnin, P450 oxidoreductase, and FKBP51) because the latter were damped but remained rhythmic in the mutant mice. Our results showed that CLOCK and CRY proteins are involved in the transcriptional regulation of many circadian output genes in the mouse liver. In addition to being a core component of the negative feedback loop that drives the circadian oscillator, CLOCK also appears to be involved in various physiological functions such as cell cycle, lipid metabolism, immune functions, and proteolysis in peripheral tissues.

Background: Identification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric, the dataset, and so on. In addition, when using the Affymetrix GeneChip ® system, researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS, RMA, and DFW, for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs, a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset.

We have systematically characterized gene expression patterns in 49 adult and embryonic mouse tissues by using cDNA microarrays with 18,816 mouse cDNAs. Cluster analysis defined sets of genes that were expressed ubiquitously or in similar groups of tissues such as digestive organs and muscle. Clustering of expression profiles was observed in embryonic brain, postnatal cerebellum, and adult olfactory bulb, reflecting similarities in neurogenesis and remodeling. Finally, clustering genes coding for known enzymes into 78 metabolic pathways revealed a surprising coordination of expression within each pathway among different tissues. On the other hand, a more detailed examination of glycolysis revealed tissue-specific differences in profiles of key regulatory enzymes. Thus, by surveying global gene expression by using microarrays with a large number of elements, we provide insights into the commonality and diversity of pathways responsible for the development and maintenance of the mammalian body plan.