Normalization procedures attempt to remove non- biological variance within microarray datasets. The choice of normalization procedure is important, as it has a dramatic effect on downstream data analysis. Although many normalization procedures have been developed, comparison and evaluation of their performance is difficult. We present a method to evaluate normalization procedures by utilizing gene- gene associations derived from the biomedical literature via Latent Semantic Indexing. The functional coherence of co- regulated genes obtained from different normalized data sets is calculated in order to evaluate the effectiveness of each normalization procedure. The method was tested on three popular normalization procedures (MAS5, PDNN and RMA) applied to gene expression data across 71 recombinant inbred mouse brain samples. Results show that, on average, MAS5 outperforms both PDNN and RMA by producing a higher number of functionally cohesive gene sets. These results demonstrate that our literature-based cohesion analysis can provide an objective method for evaluation of normalization procedures.

MOTIVATION: The analysis of gene coexpression is at the core of many types of genetic analysis. The coexpression between two genes can be calculated by using a traditional Pearson's correlation coefficient. However, unobserved confounding effects may cause inflation of the Pearson's correlation so that uncorrelated genes appear correlated. Many general methods have been suggested, which aim to remove the effects of confounding from gene expression data. However, the residual confounding which is not accounted for by these generic correction procedures has the potential to induce correlation between genes. Therefore, a method that specifically aims to calculate gene coexpression between gene expression arrays, while accounting for confounding effects, is desirable. RESULTS: In this article, we present a statistical model for calculating gene coexpression called mixed model coexpression (MMC), which models coexpression within a mixed model framework. Confounding effects are expected to be encoded in the matrix representing the correlation between arrays, the inter-sample correlation matrix. By conditioning on the information in the inter-sample correlation matrix, MMC is able to produce gene coexpressions that are not influenced by global confounding effects and thus significantly reduce the number of spurious coexpressions observed. We applied MMC to both human and yeast datasets and show it is better able to effectively prioritize strong coexpressions when compared to a traditional Pearson's correlation and a Pearson's correlation applied to data corrected with surrogate variable analysis (SVA). AVAILABILITY: The method is implemented in the R programming language and may be found at http://genetics.cs.ucla.edu/mmc. CONTACT: [email protected]; [email protected].

MOTIVATION: Structural variations and in particular copy number variations (CNVs) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads. RESULTS: In this study, we propose an efficient algorithm to handle these multi-mapping reads such that the CNVs can be reconstructed with high accuracy even for repeat-rich regions. To our knowledge, this is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Our experiments show that our method is not only computationally efficient but also accurate.

BACKGROUND: The characterization of structural variations (SV) such as insertions, deletions and copy number variations is a critical step in the process of understanding the full genetic architecture of organisms. Copy number variations (CNV) have attracted much recent attention due to their effects on gene expression and disease status. RESULTS: In this paper, we present a method that utilizes next-generation sequencing technologies (NGS), in order to both detect and reconstruct CNVs. We focus on a special type of CNV, namely tandemly organized de novo CNVs, which have been shown to occur with high frequency in the mouse genome. CONCLUSIONS: We apply our method to CNV regions randomly inserted into the reference mouse genome and show that our method achieves good performance for both detection and reconstruction of tandemly organized de novo CNVs.

MOTIVATION: Motif Tool Manager is a web-based framework for comparing and combining different approaches to discover novel DNA motifs. It comes with a set of five well-known approaches to motif discovery. It provides an easy mechanism for adding new motif finding tools to the framework through a web-interface and a minimal setup of the tools on the server. Users can execute the tools through the web-based framework and compare results from such executions. The framework provides a basic mechanism for identifying the most similar motif candidates found by a majority of themotif finding tools. AVAILABILITY: http://cetus.cs.memphis.edu/motif