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At least 1,261 records · Page 70Linked to original sources
PubMed2005

Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets.

Coexpression patterns of gene expression across many microarray data sets may reveal networks of genes involved in linked processes. To identify factors involved in cellulose biosynthesis, we used a regression method to analyze 408 publicly available Affymetrix Arabidopsis microarrays. Expression of genes previously implicated in cellulose synthesis, as well as several uncharacterized genes, was highly coregulated with expression of cellulose synthase (CESA) genes. Four candidate genes, which were coexpressed with CESA genes implicated in secondary cell wall synthesis, were investigated by mutant analysis. Two mutants exhibited irregular xylem phenotypes similar to those observed in mutants with defects in secondary cellulose synthesis and were designated irx8 and irx13. Thus, the general approach developed here is useful for identification of elements of multicomponent processes.

Arabidopsis↗
PubMed2002

Mutations in the RING domain of TFB3, a subunit of yeast transcription factor IIH, reveal a role in cell cycle progression.

The RNA polymerase II general transcription factor TFIIH is composed of 9 known subunits and possesses DNA helicase and protein kinase activities. The kinase subunits of TFIIH in animal cells, Cdk7, cyclin H, and MAT1, were independently isolated as an activity termed CAK (Cdk-activating kinase), which phosphorylates and activates cell cycle kinases. However, CAK activity of TFIIH subunits could not be demonstrated in budding yeast. TFB3, the 38-kDa subunit of yeast TFIIH, is the homolog of mammalian MAT1. By random mutagenesis we have isolated a temperature-sensitive mutation in the conserved RING domain. The mutant Tfb3 protein associates less efficiently with the kinase moiety of TFIIH than the wild type protein. In contrast to lethal mutants in other subunits of TFIIH, this mutation does not impair general transcription. Transcription of CLB2, and possibly other genes, is reduced in the mutant. At the restrictive temperature, the cells display a defect in cell cycle progression, which is manifest at more than one phase of the cycle. To conclude, in the present study we bring another demonstration of the multifunctional nature of TFIIH.

Cell Cycle↗
PubMed2004

Gene clustering based on clusterwide mutual information.

Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on minimizing mutual information among gene clusters. Simulated annealing is employed to solve the optimization problem. Bootstrap techniques are employed to get more accurate estimates of mutual information when the data sample size is small. Moreover, we propose to combine the mutual information criterion and traditional distance criteria such as the Euclidean distance and the fuzzy membership metric in designing the clustering algorithm. The performances of the new clustering methods are compared with those of some existing methods, using both synthesized data and experimental data. It is seen that the clustering algorithm based on a combined metric of mutual information and fuzzy membership achieves the best performance. The supplemental material is available at www.gspsnap.tamu.edu/gspweb/zxb/glioma_zxb.

Algorithms↗
PubMed2006

Binary state pattern clustering: a digital paradigm for class and biomarker discovery in gene microarray studies of cancer.

Class and biomarker discovery continue to be among the preeminent goals in gene microarray studies of cancer. We have developed a new data mining technique, which we call Binary State Pattern Clustering (BSPC) that is specifically adapted for these purposes, with cancer and other categorical datasets. BSPC is capable of uncovering statistically significant sample subclasses and associated marker genes in a completely unsupervised manner. This is accomplished through the application of a digital paradigm, where the expression level of each potential marker gene is treated as being representative of its discrete functional state. Multiple genes that divide samples into states along the same boundaries form a kind of gene-cluster that has an associated sample-cluster. BSPC is an extremely fast deterministic algorithm that scales well to large datasets. Here we describe results of its application to three publicly available oligonucleotide microarray datasets. Using an alpha-level of 0.05, clusters reproducing many of the known sample classifications were identified along with associated biomarkers. In addition, a number of simulations were conducted using shuffled versions of each of the original datasets, noise-added datasets, as well as completely artificial datasets. The robustness of BSPC was compared to that of three other publicly available clustering methods: ISIS, CTWC and SAMBA. The simulations demonstrate BSPC's substantially greater noise tolerance and confirm the accuracy of our calculations of statistical significance.

Algorithms↗
PubMed2002

Linking gene expression data with patient survival times using partial least squares.

There is an increasing need to link the large amount of genotypic data, gathered using microarrays for example, with various phenotypic data from patients. The classification problem in which gene expression data serve as predictors and a class label phenotype as the binary outcome variable has been examined extensively, but there has been less emphasis in dealing with other types of phenotypic data. In particular, patient survival times with censoring are often not used directly as a response variable due to the complications that arise from censoring. We show that the issues involving censored data can be circumvented by reformulating the problem as a standard Poisson regression problem. The procedure for solving the transformed problem is a combination of two approaches: partial least squares, a regression technique that is especially effective when there is severe collinearity due to a large number of predictors, and generalized linear regression, which extends standard linear regression to deal with various types of response variables. The linear combinations of the original variables identified by the method are highly correlated with the patient survival times and at the same time account for the variability in the covariates. The algorithm is fast, as it does not involve any matrix decompositions in the iterations. We apply our method to data sets from lung carcinoma and diffuse large B-cell lymphoma studies to verify its effectiveness.

Algorithms↗
PubMed2003

Graphical methods for class prediction using dimension reduction techniques on DNA microarray data.

MOTIVATION: We introduce simple graphical classification and prediction tools for tumor status using gene-expression profiles. They are based on two dimension estimation techniques sliced average variance estimation (SAVE) and sliced inverse regression (SIR). Both SAVE and SIR are used to infer on the dimension of the classification problem and obtain linear combinations of genes that contain sufficient information to predict class membership, such as tumor type. Plots of the estimated directions as well as numerical thresholds estimated from the plots are used to predict tumor classes in cDNA microarrays and the performance of the class predictors is assessed by cross-validation. A microarray simulation study is carried out to compare the power and predictive accuracy of the two methods. RESULTS: The methods are applied to cDNA microarray data on BRCA1 and BRCA2 mutation carriers as well as sporadic tumors from Hedenfalk et al. (2001). All samples are correctly classified.

Algorithms↗
PubMed2003

Kernel hierarchical gene clustering from microarray expression data.

MOTIVATION: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. RESULTS: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality. AVAILABILITY: Supplementary data at www.cs.columbia.edu/compbio/hiclust. Software source code available by request.

Algorithms↗
PubMed2004

Modeling the relationship between LVAD support time and gene expression changes in the human heart by penalized partial least squares.

MOTIVATION: Heart failure affects more than 20 million people in the world. Heart transplantation is the most effective therapy, but the number of eligible patients far outweighs the number of available donor hearts. The left mechanical ventricular assist device (LVAD) has been developed as a successful substitution therapy that aids the failing ventricle while a patient is waiting for the donor heart. We obtained genomics data from paired human heart samples harvested at the time of LVAD implant and explant. The heart failure patients in our study were supported by the LVAD for various periods of time. The goal of this study is to model the relationship between the time of LVAD support and gene expression changes. RESULTS: To serve the purpose, we propose a novel penalized partial least squares (PPLS) method to build a regression model. Compared with partial least squares and Breiman's random forest method, PPLS gives the best prediction results for the LVAD data.

Adaptation, Physiological↗
PubMed2004

GeneCluster 2.0: an advanced toolset for bioarray analysis.

SUMMARY: GeneCluster 2.0 is a software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding classification, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting and k-nearest neighbor algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater. AVAILABILITY: http://www.broad.mit.edu/cancer/software

Algorithms↗
PubMed2005

Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays.

MOTIVATION: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample. RESULTS: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. AVAILABILITY: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website.

Algorithms↗
PubMed2006

Prophet, a web-based tool for class prediction using microarray data.

UNLABELLED: Sample classification and class prediction is the aim of many gene expression studies. We present a web-based application, Prophet, which builds prediction rules and allows using them for further sample classification. Prophet automatically chooses the best classifier, along with the optimal selection of genes, using a strategy that renders unbiased cross-validated errors. Prophet is linked to different microarray data analysis modules, and includes a unique feature: the possibility of performing the functional interpretation of the molecular signature found. AVAILABILITY: Prophet can be found at the URL http://prophet.bioinfo.cipf.es/ or within the GEPAS package at http://www.gepas.org/ SUPPLEMENTARY INFORMATION: http://gepas.bioinfo.cipf.es/tutorial/prophet.html.

Algorithms↗
PubMed1999

Polymorphism analysis and gene detection by minisequencing on an array of gel-immobilized primers.

Two procedures, multibase and multiprimer, have been developed for single nucleotide extension of primers immobilized within polyacrylamide gel pads on a microchip. In the multibase assay, a primer is next to a polymorphic nucleotide; the nucleotide is identified by the specificity with which the primer incorporates fluorescently labeled dideoxyribo-nucleoside triphosphates. In the multiprimer assay, several primers containing different 3'-terminal nucleotides overlapping the variable nucleotide in DNA are used. The polymorphic nucleotide is identified according to the primer that is extended. The methods were compared for diagnosis of beta-thalassemia mutations. Isothermal amplification of the fluorescent signal was achieved by performing both assays at elevated temperature. Anthrax toxin genes were identified in a model system using this amplification method.

Antigens, Bacterial↗
PubMed2003

GEPAS: A web-based resource for microarray gene expression data analysis.

We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es).

Cluster Analysis↗
PubMed2006

CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis.

CARMAweb (Comprehensive R-based Microarray Analysis web service) is a web application designed for the analysis of microarray data. CARMAweb performs data preprocessing (background correction, quality control and normalization), detection of differentially expressed genes, cluster analysis, dimension reduction and visualization, classification, and Gene Ontology-term analysis. This web application accepts raw data from a variety of imaging software tools for the most widely used microarray platforms: Affymetrix GeneChips, spotted two-color microarrays and Applied Biosystems (ABI) microarrays. R and packages from the Bioconductor project are used as an analytical engine in combination with the R function Sweave, which allows automatic generation of analysis reports. These report files contain all R commands used to perform the analysis and guarantee therefore a maximum transparency and reproducibility for each analysis. The web application is implemented in Java based on the latest J2EE (Java 2 Enterprise Edition) software technology. CARMAweb is freely available at https://carmaweb.genome.tugraz.at.

Cluster Analysis↗
PubMed2004

Hypervariable genes--experimental error or hidden dynamics.

In a homogeneous group of samples, not all genes of high variability stem from experimental errors in microarray experiments. These expression variations can be attributed to many factors including natural biological oscillations or metabolic processes. The behavior of these genes can tease out important clues about naturally occurring dynamic processes in the organism or experimental system under study. We developed a statistical procedure for the selection of genes with high variability denoted hypervariable (HV) genes. After the exclusion of low expressed genes and a stabilizing log-transformation, the majority of genes have comparable residual variability. Based on an F-test, HV genes are selected as having a statistically significant difference from the majority of variability stabilized genes measured by the 'reference group'. A novel F-test clustering technique, further noted as 'F-means clustering', groups HV genes with similar variability patterns, presumably from their participation in a common dynamic biological process. F-means clustering establishes, for the first time, groups of co-expressed HV genes and is illustrated with microarray data from patients with juvenile rheumatoid arthritis and healthy controls.

Adolescent↗
PubMed2003

Macrostate data clustering.

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.

Cluster Analysis↗
PubMed2004

An unsupervised approach to identify molecular phenotypic components influencing breast cancer features.

To discover a biological basis for clinical subgroupings within breast cancers, we applied principal components (PCs) analysis to cDNA microarray data from 36 breast cancers. We correlated the resulting PCs with clinical features. The 35 PCs discovered were ranked in order of their impact on gene expression patterns. Interestingly, PC 7 identified a unique subgroup consisting of estrogen receptor (ER); (+) African-American patients. This group exhibited global molecular phenotypes significantly different from both ER (-) African-American women and ER (+) or ER (-) Caucasian women (P < 0.001). Additional significant PCs included PC 4, correlating with lymph node metastasis (P = 0.04), and PC 10, with tumor stage (stage 2 versus stage 3; P = 0.007). These results provide a molecular phenotypic basis for the existence of a biologically unique subgroup comprising ER (+) breast cancers from African-American patients. Moreover, these findings illustrate the potential of PCs analysis to detect molecular phenotypic bases for relevant clinical or biological features of human tumors in general.

Adult↗