CLUM: A cluster program for analyzing microarray data

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems. Cluster analysis has proven to be a very useful tool for investigating the structure of microarray data. This paper presents a program for clustering microarray data, which is based on the so-called path-distance. The algorithm gives in each step a partition in two clusters and no prior assumptions on the structure of clusters are required. It assigns each object (gene or sample) to only one cluster and gives the global optimum for the function that quantifies the adequacy of a given partition of the sample into k clusters. The program was tested on experimental data sets, showing the robustness of the algorithm.     

Strategies for analyzing highly enriched IP-chip datasets

Background  

Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been employed to examine features such as protein binding and histone modifications on a genome-wide scale in a variety of cell types. Array data from the latter studies typically have a high proportion of enriched probes whose signals vary considerably (due to heterogeneity in the cell population), and this makes their normalization and downstream analysis difficult.     

CLUM: a cluster program for analyzing microarray data

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems. Cluster analysis has proven to be a very useful tool for investigating the structure of microarray data. This paper presents a program for clustering microarray data, which is based on the so call path-distance. The algorithm gives in each step a partition in two clusters and no prior assumptions on the structure of clusters are required. It assigns each object (gene or sample) to only one cluster and gives the global optimum for the function that quantifies the adequacy of a given partition of the sample into k clusters. The program was tested on experimental data sets, showing the robustness of the algorithm.     

A method for fabricating uni-dsDNA microarray chip for analyzing DNA-binding proteins

This paper describes an approach for preparing unimolecular double-stranded DNA (uni-dsDNA) microarray chip. In this method, the various target oligonucleotides containing a reverse complementary sequence at 5' end were firstly annealed to a same universal oligonucleotide with amino group at 5' end and immobilized on aldehyde-derivatized glass slide. An on-chip DNA polymerization reaction was then performed to elongate the universal oligonucleotides. After a denaturation and a followed intra-strand annealing, a hairpin structure was formed at the free 3' end of the immobilized oligonucleotides. Finally, another on-chip DNA polymerization was done to synthesize the uni-dsDNA microarray. Combining with a PCR amplification of chemically synthesized target oligonucleotides, this method was much cost-effective for production of the uni-dsDNA microarray. The uni-dsDNA microarray was verified applicable for detecting the presence and monitoring the DNA-binding activity of the sequence-specific DNA-binding proteins.     

A scalable method for integration and functional analysis of multiple microarray datasets

MOTIVATION: The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions. RESULTS: To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.     

Tools for managing and analyzing microarray data

The microarray-based analysis of gene expression has become a workhorse for biomedical research. Managing the amount and diversity of data that such experiments produce is a task that must be supported by appropriate software tools, which led to the creation of literally hundreds of systems. In consequence, choosing the right tool for a given project is difficult even for the expert. We report on the results of a survey encompassing 78 of such tools, of which 22 were inspected in detail and seven were tested hands-on. We report on our experiences with a focus on completeness of functionality, ease-of-use, and necessary effort for installation and maintenance. Thereby, our survey provides a valuable guideline for any project considering the use of a microarray data management system. It reveals which tasks are covered by mature tools and also shows that important requirements, especially in the area of integrated analysis of different experimental data, are not yet met satisfyingly by existing systems.     

WebArray: an online platform for microarray data analysis

Background  

Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments.     

A web-based platform for rice microarray annotation and data analysis

Rice (Oryza sativa) feeds over half of the global population. A web-based integrated platform for rice microarray annotation and data analysis in various biological contexts is presented, which provides a convenient query for comprehensive annotation compared with similar databases. Coupled with existing rice microarray data, it provides online analysis methods from the perspective of bioinformatics. This comprehensive bioinformatics analysis platform is composed of five modules, including data retrieval, microarray annotation, sequence analysis, results visualization and data analysis. The BioChip module facilitates the retrieval of microarray data information via identifiers of “Probe Set ID”, “Locus ID” and “Analysis Name”. The BioAnno module is used to annotate the gene or probe set based on the gene function, the domain information, the KEGG biochemical and regulatory pathways and the potential microRNA which regulates the genes. The BioSeq module lists all of the related sequence information by a microarray probe set. The BioView module provides various visual results for the microarray data. The BioAnaly module is used to analyze the rice microarray’s data set.     

A secreted protein microarray platform for extracellular protein interaction discovery

Characterization of the extracellular protein interactome has lagged far behind that of intracellular proteins, where mass spectrometry and yeast two-hybrid technologies have excelled. Improved methods for identifying receptor-ligand and extracellular matrix protein interactions will greatly accelerate biological discovery in cell signaling and cellular communication. These technologies must be able to identify low-affinity binding events that are often observed between membrane-bound coreceptor molecules during cell-cell or cell-extracellular matrix contact. Here we demonstrate that functional protein microarrays are particularly well-suited for high-throughput screening of extracellular protein interactions. To evaluate the performance of the platform, we screened a set of 89 immunoglobulin (Ig)-type receptors against a highly diverse extracellular protein microarray with 686 genes represented. To enhance detection of low-affinity interactions, we developed a rapid method to assemble bait Fc fusion proteins into multivalent complexes using protein A microbeads. Based on these screens, we developed a statistical methodology for hit calling and identification of nonspecific interactions on protein microarrays. We found that the Ig receptor interactions identified using our methodology are highly specific and display minimal off-target binding, resulting in a 70% true-positive to false-positive hit ratio. We anticipate that these methods will be useful for a wide variety of functional protein microarray users.     

A general framework for analyzing data from two short time-series microarray experiments

We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two homogenous short time-course data. The framework generalizes the recently proposed Hilbert-Schmidt Independence Criterion (HSIC)-based framework adapting it to the time-series scenario by utilizing tensor analysis for data transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed genes and time-course patterns of interest between two time-series experiments without requiring to explicitly cluster the data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various data sets are found to be both biologically meaningful and consistent with published studies.     

Development of a microarray assay that measures hybridization stoichiometry in moles

Microarray data is most useful when it can be compared with other genetic detection technologies. In this report, we designed a microarray assay format that transforms raw data into a defined scientific unit (i.e., moles) by measuring the amount of array feature present and the cDNA sequence hybridized. This study profiles a mouse reference universal RNA sample on a microarray consisting of PCR products. In measuring array features, a labeled DNA sequence was designed that hybridizes to a conserved sequence that is present in every array feature. To measure the amount of cDNA sample hybridized, the RNA sample was processed to ensure consistent dye to DNA ratio for every labeled target cDNA molecule, using labeled branched dendrimers rather than by incorporation. A dye printing assay was then performed in order to correlate molecules of cyanine dye to signal intensity. We demonstrate that by using this microarray assay design, raw data can be transformed into defined scientific units, which will facilitate interpretation of other experiments, such as data deposited at the Gene Expression Omnibus and ArrayExpress.     

Use of within-array replicate spots for assessing differential expression in microarray experiments

MOTIVATION: Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. The usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This results in the loss of valuable information about genewise variability. RESULTS: A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. AVAILABILITY: The methodology is implemented in the limma software package for R, available from the CRAN repository http://www.r-project.org