Oligonucleotide microarray data mining: search for age-dependent gene expression

Information on gene expression in colon tumors versus normal human colon was recently generated by an oligonucleotide microarray study. We used the associated database to search for genes that display age-dependent variations in expression. Statistically significant evidence was obtained that such genes are present in both the tumor and normal tissue databases. Besides the analysis of all genes included in the database, three subsets of genes were analyzed separately: genes controlled by p53, and genes coding for ribosomal proteins and for nuclear-encoded mitochondrial proteins. Among the genes controlled by p53 some show an age-dependent change in expression in tumor tissues, in the sense compatible with an activation of p53 at higher age. A decreased expression of some ribosomal genes at advanced age was detected both in tumor and normal tissues. No significant age-dependent expression could be detected for genes encoding mitochondrial proteins.     

DANCER: a program for digital anatomical reconstruction of gene expression data

A digital anatomy construction (DANCER) program was developed for gene expression data. DANCER can be used to reconstruct anatomical images from in situ hybridization images, microarray or other gene expression data. The program fills regions of a drawn figure with the corresponding values from a gene expression data set. The output of the program presents the expression levels of a particular gene in a particular region relative to other regions. The program was tested with values from experimental in situ hybridization autoradiographs and from a microarray experiment. Reconstruction of in situ hybridization data from adult rat brain made by DANCER corresponded well with the original autoradiograph. Reconstruction of microarray data from adult mouse brains provided images that reflect actual expression levels. This program should help to provide visualization and interpretation of data derived from gene expression experiments. DANCER may be freely downloaded.     

A computer program for modeling the kinetics of gene expression

Enzyme induction may be modeled on the basis of four, quantifiable processes that control the rates at which specific gene products accumulate and decay. These processes include synthesis of functional mRNA, translation and degradation of mRNA, and degradation of the protein product. We present a simple computer program that permits mathematical simulation of gene expression on the basis of experimentally determined rates of synthesis and degradation. The program was implemented as a spreadsheet using Microsoft Excel for Macintosh and MS-DOS operating systems and also was adapted for HyperCard on the Macintosh. It contains a formula to account for growth of tissue or cell populations. The program predicts amounts of individual mRNAs and proteins (or enzyme activities) in cells as a function of time after a stimulus alters their rates of synthesis or degradation.     

gimlet: a computer program for analysing genetic individual identification data

Growing interest in microsatellite genotyping, combined with noninvasive genetic sampling has led to the increased production of data. New tools to analyse these data are required. gimlet is a user‐friendly software package designed to perform several simple tasks: (i) construction of consensus genotypes from repeated genotyping; (ii) estimation of genotyping error rates; (iii) identification of identical genotypes; (iv) comparison of new genotypes to a set of reference genotypes; (v) determination of the kinship; and (vi) estimation of several population parameters such as allele frequencies, heterozygosity, probability of identity, and population size.     

Large scale data mining approach for gene-specific standardization of microarray gene expression data

MOTIVATION: The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of gene's behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples. RESULTS: The availability of a large collection of array samples sharing the same platform in public databases, such as NCBI GEO, enabled us to re-standardize the expression intensity of a gene using its mean and variation in the wide variety of experimental conditions. This approach was evaluated via the re-identification of breast cancer-specific gene expression. It successfully prioritized several genes associated with breast tumor, for which the expression difference between normal and breast cancer cells was marginal and thus would have been difficult to recognize using conventional analysis methods. Maximizing the utility of microarray data in the public database, it provides a valuable tool particularly for the identification of previously unrecognized disease-related genes. AVAILABILITY: A user friendly web-interface (http://compbio.sookmyung.ac.kr/~lage/) was constructed to provide the present large-scale approach for the analysis of GEO microarray data (GS-LAGE server).     

Efficiently mining gene expression data via a novel parameterless clustering method

Clustering analysis has been an important research topic in the machine learning field due to the wide applications. In recent years, it has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high quality, and high efficiency at the same time. In this paper, we propose a novel, parameterless and efficient clustering algorithm, namely, correlation search technique (CST), which fits for analysis of gene expression data. The unique feature of CST is it incorporates the validation techniques into the clustering process so that high quality clustering results can be produced on the fly. Through experimental evaluation, CST is shown to outperform other clustering methods greatly in terms of clustering quality, efficiency, and automation on both of synthetic and real data sets.     

An effective data mining technique for reconstructing gene regulatory networks from time series expression data

Recent development in DNA microarray technologies has made the reconstruction of gene regulatory networks (GRNs) feasible. To infer the overall structure of a GRN, there is a need to find out how the expression of each gene can be affected by the others. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene in an unseen sample has any interactions with other genes, statistical verification of the reliability of the discovered interactions can be difficult. Furthermore, since the temporal ordering of the data is not taken into consideration, the directionality of regulation cannot be established using these existing techniques. To tackle these problems, we propose a data mining technique here. This technique makes use of a probabilistic inference approach to uncover interesting dependency relationships in noisy, high-dimensional time series expression data. It is not only able to determine if a gene is dependent on another but also whether or not it is activated or inhibited. In addition, it can predict how a gene would be affected by other genes even in unseen samples. For performance evaluation, the proposed technique has been tested with real expression data. Experimental results show that it can be very effective. The discovered dependency relationships can reveal gene regulatory relationships that could be used to infer the structures of GRNs.     

CressExpress: a tool for large-scale mining of expression data from Arabidopsis

CressExpress is a user-friendly, online, coexpression analysis tool for Arabidopsis (Arabidopsis thaliana) microarray expression data that computes patterns of correlated expression between user-entered query genes and the rest of the genes in the genome. Unlike other coexpression tools, CressExpress allows characterization of tissue-specific coexpression networks through user-driven filtering of input data based on sample tissue type. CressExpress also performs pathway-level coexpression analysis on each set of query genes, identifying and ranking genes based on their common connections with two or more query genes. This allows identification of novel candidates for involvement in common processes and functions represented by the query group. Users launch experiments using an easy-to-use Web-based interface and then receive the full complement of results, along with a record of tool settings and parameters, via an e-mail link to the CressExpress Web site. Data sets featured in CressExpress are strictly versioned and include expression data from MAS5, GCRMA, and RMA array processing algorithms. To demonstrate applications for CressExpress, we present coexpression analyses of cellulose synthase genes, indolic glucosinolate biosynthesis, and flowering. We show that subselecting sample types produces a richer network for genes involved in flowering in Arabidopsis. CressExpress provides direct access to expression values via an easy-to-use URL-based Web service, allowing users to determine quickly if their query genes are coexpressed with each other and likely to yield informative pathway-level coexpression results. The tool is available at http://www.cressexpress.org.     

PCP: a program for supervised classification of gene expression profiles

PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms. AVAILABILITY: Free download at http://pcp.sourceforge.net     

A computer program for displaying two-dimensional gel electrophoresis data

A computer program is described which facilitates comparison between the pattern of spots seen on different two-dimensional polyacrylamide electrophoresis gels. Essentially, the position of each spot is replotted on a graph by the computer using its molecular weight and isoelectric point as coordinates. An intensity factor is also assigned to each point by the operator which will determine the size and shape of the final plotted spot on the computer drawn figure. The resulting plot makes it more feasible to compare patterns of spots between independently run two-dimensional electrophoresis gels.     

GenMiner: mining non-redundant association rules from integrated gene expression data and annotations

GenMiner is an implementation of association rule discovery dedicated to the analysis of genomic data. It allows the analysis of datasets integrating multiple sources of biological data represented as both discrete values, such as gene annotations, and continuous values, such as gene expression measures. GenMiner implements the new NorDi (normal discretization) algorithm for normalizing and discretizing continuous values and takes advantage of the Close algorithm to efficiently generate minimal non-redundant association rules. Experiments show that execution time and memory usage of GenMiner are significantly smaller than those of the standard Apriori-based approach, as well as the number of extracted association rules. AVAILABILITY: The GenMiner software and supplementary materials are available at http://bioinfo.unice.fr/publications/genminer_article/ and http://keia.i3s.unice.fr/?Implementations:GenMiner SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

MicroMeasure: a new computer program for the collection and analysis of cytogenetic data.

The ability to identify individual chromosomes in cytological preparations is an essential component of many investigations. While several computer software applications have been used to facilitate such quantitative karyotype analysis, most of these programs are limited by design for specific types of analyses, or can be used only with specific hardware configurations. MicroMeasure is a new image analysis application that may be used to collect data for a wide variety of chromosomal parameters from electronically captured or scanned images. Unlike similar applications, MicroMeasure may be individually configured by the end user to suit a wide variety of research needs. This program can be used with most common personal computers, and requires no unusual or specific hardware. MicroMeasure is made available to the research community without cost by the Department of Biology at Colorado State University via the World Wide Web at http://www.biology.colostate.edu/MicroMeasure.     

SplicerAV: a tool for mining microarray expression data for changes in RNA processing

Background  

Over the past two decades more than fifty thousand unique clinical and biological samples have been assayed using the Affymetrix HG-U133 and HG-U95 GeneChip microarray platforms. This substantial repository has been used extensively to characterize changes in gene expression between biological samples, but has not been previously mined en masse for changes in mRNA processing. We explored the possibility of using HG-U133 microarray data to identify changes in alternative mRNA processing in several available archival datasets.