Bivariate microarray analysis: statistical interpretation of two-channel functional genomics data

Conventional statistical methods for interpreting microarray data require large numbers of replicates in order to provide sufficient levels of sensitivity. We recently described a method for identifying differentially-expressed genes in one-channel microarray data 1. Based on the idea that the variance structure of microarray data can itself be a reliable measure of noise, this method allows statistically sound interpretation of as few as two replicates per treatment condition. Unlike the one-channel array, the two-channel platform simultaneously compares gene expression in two RNA samples. This leads to covariation of the measured signals. Hence, by accounting for covariation in the variance model, we can significantly increase the power of the statistical test. We believe that this approach has the potential to overcome limitations of existing methods. We present here a novel approach for the analysis of microarray data that involves modeling the variance structure of paired expression data in the context of a Bayesian framework. We also describe a novel statistical test that can be used to identify differentially-expressed genes. This method, bivariate microarray analysis (BMA), demonstrates dramatically improved sensitivity over existing approaches. We show that with only two array replicates, it is possible to detect gene expression changes that are at best detected with six array replicates by other methods. Further, we show that combining results from BMA with Gene Ontology annotation yields biologically significant results in a ligand-treated macrophage cell system.     

Tilescope: online analysis pipeline for high-density tiling microarray data

We developed Tilescope, a fully integrated data processing pipeline for analyzing high-density tiling-array data . In a completely automated fashion, Tilescope will normalize signals between channels and across arrays, combine replicate experiments, score each array element, and identify genomic features. The program is designed with a modular, three-tiered architecture, facilitating parallelism, and a graphic user-friendly interface, presenting results in an organized web page, downloadable for further analysis.     

A recombineering pipeline for functional genomics applied to Caenorhabditis elegans

We present a new concept in DNA engineering based on a pipeline of serial recombineering steps in liquid culture. This approach is fast, straightforward and facilitates simultaneous processing of multiple samples in parallel. We validated the approach by generating green fluorescent protein (GFP)-tagged transgenes from Caenorhabditis briggsae genomic clones in a multistep pipeline that takes only 4 d. The transgenes were engineered with minimal disturbance to the natural genomic context so that the correct level and pattern of expression will be secured after transgenesis. An example transgene for the C. briggsae ortholog of lin-59 was used for ballistic transformation in Caenorhabditis elegans. We show that the cross-species transgene is correctly expressed and rescues RNA interference (RNAi)-mediated knockdown of the endogenous C. elegans gene. The strategy that we describe adapts the power of recombineering in Escherichia coli for fluent DNA engineering to a format that can be directly scaled up for genomic projects.     

NMPP: a user-customized NimbleGen microarray data processing pipeline

NMPP package is a bundle of user-customized tools based on established algorithms and methods to process self-designed NimbleGen microarray data. It features a command-line-based integrative processing procedure that comprises five major functional components, namely the raw microarray data parsing and integrating module, the array spatial effect smoothing and visualization module, the probe-level multi-array normalization module, the gene expression intensity summarization module and the gene expression status inference module. AVAILABILITY: http://plantgenomics.biology.yale.edu/nmpp     

Challenges in data management for functional genomics

Biological databases face challenges in four main areas: (1). integration, interoperation and federation; (2). ontologies and definitions of semantics; (3). community annotation; and (4). integration of data analysis tools with databases. Each of these areas provides interesting targets for research and development.     

Unequal group variances in microarray data analyses

Motivation: In searching for differentially expressed (DE) genesin microarray data, we often observe a fraction of the genesto have unequal variability between groups. This is not an issuein large samples, where a valid test exists that uses individualvariances separately. The problem arises in the small-samplesetting, where the approximately valid Welch test lacks sensitivity,while the more sensitive moderated t-test assumes equal variance. Methods: We introduce a moderated Welch test (MWT) that allowsunequal variance between groups. It is based on (i) weightingof pooled and unpooled standard errors and (ii) improved estimationof the gene-level variance that exploits the information fromacross the genes. Results: When a non-trivial proportion of genes has unequalvariability, false discovery rate (FDR) estimates based on thestandard t and moderated t-tests are often too optimistic, whilethe standard Welch test has low sensitivity. The MWT is shownto (i) perform better than the standard t, the standard Welchand the moderated t-tests when the variances are unequal betweengroups and (ii) perform similarly to the moderated t, and betterthan the standard t and Welch tests when the group variancesare equal. These results mean that MWT is more reliable thanother existing tests over wider range of data conditions. Availability: R package to perform MWT is available at http://www.meb.ki.se/~yudpaw Contact: yudi.pawitan{at}ki.se Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Martin Bishop     

Regulation of bacterial respiration: Comparison of microarray and comparative genomics data

A comparison was made of the structures of the Fnr and ArcA modulons and regulons. The data on modulon composition were taken from published microarray assays, whereas regulons were characterized using comparative genomic approaches. The regulatory cascade involving Fnr and ArcA contributes greatly to the extension of the Fnr modulon over the Fnr regulon by adding operons of the ArcA modulon. The Fnr and ArcA regulons were shown to contain 26 and 16 operons, respectively. Ten operons had high-score and highly conserved sites for both Fnr and ArcA and were isolated as a so-called core of regulons.     

Non-linear mapping for exploratory data analysis in functional genomics

Background  

Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated     

An analysis of extensible modelling for functional genomics data

Background  

Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time.     

Global functional analyses of rice promoters by genomics approaches

Promoters play key roles in conferring temporal, spatial, chemical, developmental, or environmental regulation of gene expression. Promoters that are subject to specific regulations are useful for manipulating foreign gene expression in plant cells, tissues, or organs with desirable patterns and under controlled conditions, and have been important for both basic research and applications in agriculture biotechnology. Recent advances in genomics technologies have greatly facilitated identification and study of promoters in a genome scale with high efficiency. Previously we have generated a large T-DNA tagged rice mutant library (TRIM), in which the T-DNA was designed with a gene/promoter trap system, by placing a promoter-less GUS gene next to the right border of T-DNA. GUS activity screens of this library offer in situ and in planta identifications and analyses of promoter activities in their native configurations in the rice genome. In the present study, we systematically performed GUS activity screens of the rice mutant library for genes/promoters constitutively, differentially, or specifically active in vegetative and reproductive tissues. More than 8,200 lines have been screened, and 11% and 22% of them displayed GUS staining in vegetative tissues and in flowers, respectively. Among the vegetative tissue active promoters, the ratio of leaf active versus root active is about 1.6. Interestingly, all the flower active promoters are anther active, but with varied activities in different flower tissues. To identify tissue specific ABA/stress up-regulated promoters, we compared microarray data of ABA/stress induced genes with those of tissue-specific expression determined by promoter trap GUS staining. Following this approach, we showed that the peroxidase 1 gene promoter was ABA up-regulated by 4 fold within 1 day of exposure to ABA and its expression is lateral root specific. We suggest that this be an easy bioinformatics approach in identifying tissue/cell type specific promoters that are up-regulated by hormones or other factors. Su-May Yu and Swee-Suak Ko equally contributed to this work.     

PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs

The analysis of genetic data often requires a combination of several approaches using different and sometimes incompatible programs. In order to facilitate data exchange and file conversions between population genetics programs, we introduce PGDSpider, a Java program that can read 27 different file formats and export data into 29, partially overlapping, other file formats. The PGDSpider package includes both an intuitive graphical user interface and a command-line version allowing its integration in complex data analysis pipelines. AVAILABILITY: PGDSpider is freely available under the BSD 3-Clause license on http://cmpg.unibe.ch/software/PGDSpider/.