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.