BACKGROUND: Transcriptomic approaches (microarray and RNA-seq) have been a tremendous advance for molecular science in all disciplines, but they have made interpretation of hypothesis testing more difficult because of the large number of comparisons that are done within an experiment. The result has been a proliferation of techniques aimed at solving the multiple comparisons problem, techniques that have focused primarily on minimizing Type I error with little or no concern about concomitant increases in Type II errors. We have previously proposed a novel approach for setting statistical thresholds with applications for high throughput omics-data, optimal α, which minimizes the probability of making either error (i.e. Type I or II) and eliminates the need for post-hoc adjustments. RESULTS: A meta-analysis of 242 microarray studies extracted from the peer-reviewed literature found that current practices for setting statistical thresholds led to very high Type II error rates. Further, we demonstrate that applying the optimal α approach results in error rates as low or lower than error rates obtained when using (i) no post-hoc adjustment, (ii) a Bonferroni adjustment and (iii) a false discovery rate (FDR) adjustment which is widely used in transcriptome studies. CONCLUSIONS: We conclude that optimal α can reduce error rates associated with transcripts in both microarray and RNA-seq experiments, but point out that improved statistical techniques alone cannot solve the problems associated with high throughput datasets - these approaches need to be coupled with improved experimental design that considers larger sample sizes and/or greater study replication.

BACKGROUND: Normalization is essential in dual-labelled microarray data analysis to remove non-biological variations and systematic biases. Many normalization methods have been used to remove such biases within slides (Global, Lowess) and across slides (Scale, Quantile and VSN). However, all these popular approaches have critical assumptions about data distribution, which is often not valid in practice. RESULTS: In this study, we propose a novel assumption-free normalization method based on the Generalized Procrustes Analysis (GPA) algorithm. Using experimental and simulated normal microarray data and boutique array data, we systemically evaluate the ability of the GPA method in normalization compared with six other popular normalization methods including Global, Lowess, Scale, Quantile, VSN, and one boutique array-specific housekeeping gene method. The assessment of these methods is based on three different empirical criteria: across-slide variability, the Kolmogorov-Smirnov (K-S) statistic and the mean square error (MSE). Compared with other methods, the GPA method performs effectively and consistently better in reducing across-slide variability and removing systematic bias. CONCLUSION: The GPA method is an effective normalization approach for microarray data analysis. In particular, it is free from the statistical and biological assumptions inherent in other normalization methods that are often difficult to validate. Therefore, the GPA method has a major advantage in that it can be applied to diverse types of array sets, especially to the boutique array where the majority of genes may be differentially expressed.

Since packaging of DNA in the chromatin structure restricts the accessibility for regulatory factors, chromatin remodeling is required to facilitate nuclear processes such as gene transcription, replication, and genome recombination. Many conserved non-enzymatic protein domains have been identified that contribute to the activities of multiprotein remodeling complexes. Here we identified a novel conserved protein domain in Eukaryota whose putative function may be in regulating chromatin remodeling. Since this domain is associated with a known SANT domain in several vertebrate proteins, we named it the SANTA (SANT Associated) domain. Sequence analysis showed that the SANTA domain is approximately a 90 amino acid module and likely composed of four central beta-sheets and three flanking alpha-helices. Many hydrophobic residues exhibited high conservation along the domain, implying a possible function in protein-protein interactions. The SANTA domain was identified in mammals, chicken, frog, fish, sea squirt, sea urchin, worms and plants. Furthermore, a phylogenetic tree of SANTA domains showed that one plant-specific duplication event happened in the Viridiplantae lineage.