Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Oligonucleotide Array Sequence Analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 919 records · Page 51Linked to original sources
PubMed2006

Annotation of environmental OMICS data: application to the transcriptomics domain.

Researchers working on environmentally relevant organisms, populations, and communities are increasingly turning to the application of OMICS technologies to answer fundamental questions about the natural world, how it changes over time, and how it is influenced by anthropogenic factors. In doing so, the need to capture meta-data that accurately describes the biological "source" material used in such experiments is growing in importance. Here, we provide an overview of the formation of the "Env" community of environmental OMICS researchers and its efforts at considering the meta-data capture needs of those working in environmental OMICS. Specifically, we discuss the development to date of the Env specification, an informal specification including descriptors related to geographic location, environment, organism relationship, and phenotype. We then describe its application to the description of environmental transcriptomic experiments and how we have used it to extend the Minimum Information About a Microarray Experiment (MIAME) data standard to create a domain-specific extension that we have termed MIAME/Env. Finally, we make an open call to the community for participation in the Env Community and its future activities.

Ecology↗
PubMed2005

Genomic epidemiology of complex disease: the need for an electronic evidence-based approach to research synthesis.

Modern microarray genotyping now permits simultaneous analysis of tens of thousands of polymorphisms, and this technology is being widely used to associate the role of genes with the etiology of complex disease. Genome-wide hypothesis-free mapping will also increasingly generate candidate genes that require further testing in association studies. At the same time, genetic effects are increasingly observed to be buffered by a wide array of biologic mechanisms that evolved to protect the genome from environmental insult and that serve to obscure observation of direct effects of polymorphisms on a disease phenotype. These two forces combine to make replication of genomic epidemiology extraordinarily difficult. Traditional research synthesis of emerging bodies of genomic epidemiology is problematic and often quickly outdated. The author proposes that electronic evidence-based methodology, perhaps modeled after that used by the Cochrane Collaboration in clinical medicine, would facilitate the systematic preparation and frequent updating of systematic reviews, which is essential for identifying valid and replicable gene-disease associations.

Chromosome Mapping↗
PubMed2004

Supervised cluster analysis for microarray data based on multivariate Gaussian mixture.

MOTIVATION: Grouping genes having similar expression patterns is called gene clustering, which has been proved to be a useful tool for extracting underlying biological information of gene expression data. Many clustering procedures have shown success in microarray gene clustering; most of them belong to the family of heuristic clustering algorithms. Model-based algorithms are alternative clustering algorithms, which are based on the assumption that the whole set of microarray data is a finite mixture of a certain type of distributions with different parameters. Application of the model-based algorithms to unsupervised clustering has been reported. Here, for the first time, we demonstrated the use of the model-based algorithm in supervised clustering of microarray data. RESULTS: We applied the proposed methods to real gene expression data and simulated data. We showed that the supervised model-based algorithm is superior over the unsupervised method and the support vector machines (SVM) method. AVAILABILITY: The program written in the SAS language implementing methods I-III in this report is available upon request. The software of SVMs is available in the website http://svm.sdsc.edu/cgi-bin/nph-SVMsubmit.cgi

Algorithms↗
PubMed2004

ClutrFree: cluster tree visualization and interpretation.

UNLABELLED: ClutrFree facilitates the visualization and interpretation of clusters or patterns computed from microarray data through a graphical user interface that displays patterns, membership information of the genes and annotation statistics simultaneously. ClutrFree creates a tree linking the patterns based on similarity, permitting the navigation among patterns identified by different algorithms or by the same algorithm with different parameters, and aids the inferring of conclusions from a microarray experiment. AVAILABILITY: The ClutrFree Java source code and compiled bytecode are available as a package under the GNU General Public License at http://bioinformatics.fccc.edu

Algorithms↗
PubMed2005

Hybrid hierarchical clustering with applications to microarray data.

In this paper, we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying small clusters but not large ones; the strengths are reversed for the second method. The hybrid method is built on the new idea of a mutual cluster: a group of points closer to each other than to any other points. Theoretical connections between mutual clusters and bottom-up clustering methods are established, aiding in their interpretation and providing an algorithm for identification of mutual clusters. We illustrate the technique on simulated and real microarray datasets.

Algorithms↗
PubMed2006

Gene expression profiling of primary cutaneous melanoma and clinical outcome.

BACKGROUND: Gene expression profiling data for human primary cutaneous melanomas are scarce because of the lack of retrospective collections of frozen tumors. To identify differentially expressed genes that may be involved in melanoma progression and prognosis, we investigated the relationship between gene expression profiles and clinical outcome in a cohort of patients with primary melanoma. METHODS: Labeled complementary RNA (cRNA) from each tissue sample was hybridized to a pangenomic 44K 60-mer oligonucleotide microarray. Class comparison and class prediction analyses were performed to identify genes whose expression in primary melanomas was associated with 4-year distant metastasis-free survival among 58 patients with at least 4 years of follow-up, distant metastasis, or death. Results were validated immunohistochemically at the protein level in 176 independent primary melanomas from patients with a median clinical follow-up of 8.5 years. Survival was analyzed with a Cox multivariable model and stratified log-rank test. All statistical tests were two-sided. RESULTS: We identified 254 genes that were associated with distant metastasis-free survival of patients with primary melanoma. These 254 genes include genes involved in activating DNA replication origins, such as minichromosome maintenance genes and geminin. Twenty-three of these genes were studied at the protein level; expression of five (MCM4, P = .002; MCM3, P = .030; MCM6, P = .004; KPNA2, P = .021; and geminin, P = .004) was statistically significantly associated with overall survival in the validation set. In a multivariable Cox model adjusted for tumor thickness, ulceration, age, and sex, expression of MCM4 (hazard ratio [HR] of death = 4.04, 95% confidence interval [CI] = 1.39 to 11.76; P = .010) and MCM6 (HR of death = 7.42, 95% CI = 1.99 to 27.64; P = .003) proteins was still statistically significantly associated with overall survival. CONCLUSION: We identified 254 genes whose expression was associated with metastatic dissemination of cutaneous melanomas. These genes may shed light on the molecular mechanisms underlying poor prognosis in melanoma patients.

Adolescent↗
PubMed2006

Quantitative trait associated microarray gene expression data analysis.

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis of gene expression data aims at classifying genes into several different groups based on the similarity of their expression profiles across multiple conditions. The hope is that genes that are classified into the same clusters may share underlying regulatory elements or may be a part of the same metabolic pathways. Current methods for examining the association between phenotype and gene expression are limited to linear association measured by the correlation between individual gene expression values and phenotype. Genes may be associated with the phenotype in a nonlinear fashion. In addition, groups of genes that share a particular pattern in their relationship to phenotype may be of evolutionary interest. In this study, we develop a method to group genes based on orthogonal polynomials under a multivariate Gaussian mixture model. The effect of each expressed gene on the phenotype is partitioned into a cluster mean and a random deviation from the mean. Genes can also be clustered based on a time series. Parameters are estimated using the expectation-maximization algorithm and implemented in SAS. The method is verified with simulated data and demonstrated with experimental data from 2 studies, one clusters with respect to severity of disease in Alzheimer's patients and another clusters data for a rat fracture healing study over time. We find significant evidence of nonlinear associations in both studies and successfully describe these patterns with our method. We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses.

Animals↗
PubMed2005

Bayesian error-in-variable survival model for the analysis of GeneChip arrays.

DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the molecular basis for specific diseases. An intense area of research is concerned with the identification of genes related to particular phenotypes. The technology, however, is subject to various sources of error that may lead to expression readings that are substantially different from the true transcript levels. Few methods for microarray data analysis have accounted for measurement error in a substantial way and that is the purpose of this investigation. We describe a Bayesian error-in-variable model for the analysis of microarray data from a clinical study of patients with acute lymphoblastic leukemia. We focus in particular on the problem of identifying genes whose expression patterns are associated with duration of remission. This is a question of great practical interest since relapse is a major concern in the treatment of this disease. We explore the effects of ignoring the uncertainty in the expression estimates on the selection and ranking of genes.

Bayes Theorem↗
PubMed2004

Prognostic impact of ANX7-GTPase in metastatic and HER2-negative breast cancer patients.

PURPOSE: ANX7-GTPase located on chromosome 10q21 is significantly altered and associated with hormone-refractory metastatic prostate cancers. Therefore, we investigated whether levels of ANX7 correlate with breast cancer progression and survival EXPERIMENTAL DESIGN: A diagnostic tumor tissue microarray containing 525 human breast tissue specimens at different stages of the disease was assayed for ANX7 using immunocytochemical methods with ANX7 monoclonal antibody. A separate prognostic tumor tissue microarray containing 553 human breast tissue specimens annotated with clinicopathological parameters was assayed for ANX7, HER2, estrogen receptor, progesterone receptor, and p53 protein. RESULTS: We report here for the first time that the expression of ANX7-GTPase is significantly enhanced and associated with the presence of metastatic disease (P < 0.0001) in the 525 human breast tissue specimens analyzed. Furthermore, using a separate 553 case retrospective prognostic tumor tissue microarray, we found that increased ANX7 expression is also significantly associated with poor overall patient survival (P < 0.014). This is particularly true when restricted to patients in whom the BRE clinical grade is 2 (P < 0.001) or for whom there is a lack of HER2 expression (P < 0.002). Finally, Cox regression analysis shows that as the expression of ANX7 rises, the probability of survival decreases by more than 10-fold for those patients with HER2-negative tumors. These latter patients represented 66% of the population affected with breast cancer in this study. CONCLUSIONS: High levels of ANX7 in tumor correlate strongly with poor survival of HER2-negative patients and the most aggressive forms of breast cancer. This is the first study to demonstrate that ANX7 antibody has the potential for development into an in vivo diagnostic and therapeutic tool. This simple and reliable immunohistochemical assay may therefore become an important biomarker for metastatic breast cancer diagnosis and management of HER2-negative breast tumor patients.

Adult↗
PubMed2004

Expression of the forkhead transcription factor FOXP1 is associated with estrogen receptor alpha and improved survival in primary human breast carcinomas.

PURPOSE: The FOXP1 protein belongs to a functionally diverse family of winged-helix or forkhead transcription factors that have diverse roles in cellular proliferation, differentiation, and neoplastic transformation. The FOXP1 gene, which maps to 3p14, shows common loss of heterozygosity in breast tumors and is a candidate tumor suppressor gene. However, its role in breast cancer is unknown. EXPERIMENTAL DESIGN: We have therefore investigated the pattern of FOXP1 expression in whole sections from normal (n = 16) and neoplastic (n = 90) breast tissues and correlated the level of expression in 283 invasive breast carcinomas on tissue microarrays with clinicopathological factors and survival. Because a relationship with estrogen receptor (ER) was identified, estrogen (17beta-estradiol) regulation and ER/FOXP1 colocalization was also investigated. RESULTS: Expression of FOXP1 was significantly positively associated with ER (P = 0.03) and negatively with epidermal growth factor receptor (P = 0.01) but no association with age (P = 0.91), lymph node status (P = 0.94), size (P = 0.76), or grade (P = 0.22). In a multivariate analysis of survival, FOXP1 expression was associated with a significantly improved relapse-free (P = 0.03) and borderline overall (P = 0.09) survival. Unlike normal breast, there was common coexpression of FOXP1 and ER in cell lines and tumors, but no 17beta-estradiol (10(-9) m) regulation of FOXP1 in MCF-7 cells was demonstrated. CONCLUSIONS: Our findings support a role for FOXP1 as a potential ER coregulator in human breast carcinoma and suggest that it may also independently regulate additional important pathways that control the progression of breast cancer.

Adult↗
PubMed2004

Ability to predict metastasis based on pathology findings and alterations in nuclear structure of normal-appearing and cancer peripheral zone epithelium in the prostate.

PURPOSE: Malignant transformation in the prostate produces significant alterations in glandular architecture (Gleason grade) and nuclear structure that provide valuable prognostic information. Normal-appearing nuclei (NN) adjacent to cancer may also have altered functions in response to malignancy. We studied NN adjacent to peripheral zone (PZ) prostate cancer (PCa), as well as the PZ cancer nuclei (CaN) using quantitative image cytometry. The nuclear structure information was combined with routine pathological findings to predict metastatic PCa progression and/or death. EXPERIMENTAL DESIGN: Tissue microarrays of normal-appearing and cancer areas were prepared from 182 pathologist-selected paraffin blocks. Feulgen-stained CaN and NN were captured from the tissue microarrays using the AutoCyte Pathology Workstation. Multivariate logistic regression was used to calculate quantitative nuclear grade (QNG) solutions based on nuclear morphometric descriptors determined from NN and CaN. Multivariate logistic regression and Kaplan-Meier plots were also used to predict risk for distant metastasis and/or PCa-specific death using QNG solutions and routine pathology. RESULTS: The pathology model yielded an area under the receiver operator characteristic curve of 72.5%. The QNG-NN and QNG-CaN solutions yielded an area under the receiver operator characteristic curve of 81.6 and 79.9%, respectively, but used different sets of nuclear morphometric descriptors. Kaplan-Meier plots for the pathology variables, the QNG-NN and QNG-CaN solutions, were combined with pathology to defined three statistically significantly distinct risk groups for distant metastasis and/or death (P < 0.0001). CONCLUSIONS: Alterations in cancer or normal-appearing nuclei adjacent to peripheral zone cancer areas can predict PCa progression and/or death. The QNG-NN and QNG-CA solutions could be combined with pathology variables to improve the prediction of distant metastasis.

Adult↗
PubMed2004

Reduced lecithin: retinol acyltransferase expression correlates with increased pathologic tumor stage in bladder cancer.

PURPOSE: Retinoids, which include vitamin A (retinol; ROL) and its derivatives, have been investigated in the treatment of bladder cancer. We have shown that expression of the enzyme lecithin:ROL acyltransferase (LRAT), which converts ROL to retinyl esters, is reduced in several human cancers. Here we evaluated expression of LRAT protein and mRNA in normal and malignant bladder tissue specimens from human patients. We also examined the effect of retinoids on LRAT expression in bladder cancer cell lines. EXPERIMENTAL DESIGN: We evaluated 49 bladder cancer specimens for LRAT protein expression using immunohistochemistry with affinity-purified antibodies to human LRAT. LRAT mRNA expression was assessed using reverse transcription-PCR in bladder specimens from an additional 16 patients. We examined the effect of retinoic acid and ROL on LRAT mRNA expression in five human bladder cancer cell lines. RESULTS: LRAT protein was detected throughout the nonneoplastic bladder epithelium in all of the specimens. In bladder tumors, LRAT protein expression was reduced compared with the nonneoplastic epithelium or was completely absent in 7 of 32 (21.9%) superficial tumors versus 16 of 17 (94.1%) invasive tumors (P < 0.001). All of the non-neoplastic bladder specimens tested (11 of 11) showed LRAT mRNA expression, compared with 5 of 8 (62%) superficial tumors and 0 of 5 (0%) invasive tumors (P = 0.001). Three of five human bladder cancer cell lines expressed LRAT mRNA independent of retinoid exposure, whereas in two cell lines LRAT mRNA expression was induced by retinoid treatment. CONCLUSIONS: We report a significant reduction in LRAT expression in bladder cancer. Moreover, we demonstrate an inverse correlation of LRAT mRNA and protein expression with increasing tumor stage. These data suggest that loss of LRAT expression is associated with invasive bladder cancer.

Acyltransferases↗
PubMed2004

A clinicobiological model predicting survival in medulloblastoma.

PURPOSE: The purpose of this study was to determine the relative contributions of biological and clinical predictors of survival in patients with medulloblastoma (MB). EXPERIMENTAL DESIGN: Clinical presentation and survival information were obtained for 119 patients who had undergone surgery for MB at the Hospital for Sick Children (Toronto, Ontario, Canada) between 1985 and 2001. A tissue microarray was constructed from the tumor samples. The arrays were assayed for immunohistochemical expression of MYC, p53, platelet-derived growth factor receptor-alpha, ErbB2, MIB-1, and TrkC and for apoptosis (terminal deoxynucleotidyl transferase-mediated nick end labeling). Both univariable and multivariable analyses were conducted to characterize the association between survival and both clinical and biological markers. For the strongest predictors of survival, a weighted predictive score was calculated based on their hazard ratios (HRs). The sum of these scores was then used to give an overall prediction of survival using a nomogram. RESULTS: The four strongest predictors of survival in the final multivariable model were the presence of metastatic disease at presentation (HR, 2.02; P=0.01) and p53 (HR, 2.29; P=0.02), TrkC (HR, 0.65; P=0.14), and ErbB2 (HR, 1.51; P=0.21) immunopositivity. A linear prognostic index was derived, with coefficients equal to the logarithm of these HRs. The 5-year survival rate for patients at the 10th, 50th, and 90th percentiles of the score distribution was 80.0%, 71.0%, and 35.7%, respectively, with radiation therapy and 70.5%, 58.5%, and 20.0%, respectively, without radiation therapy. CONCLUSIONS: In this study, we demonstrate an approach to combining both clinical and biological markers to quantify risk in MB patients. This provides further prognostic information than can be obtained when either clinical factors or biological markers are studied separately and establishes a framework for comparing prognostic markers in future clinical studies.

Adolescent↗
PubMed2001

Resampling method for unsupervised estimation of cluster validity.

We introduce a method for validation of results obtained by clustering analysis of data. The method is based on resampling the available data. A figure of merit that measures the stability of clustering solutions against resampling is introduced. Clusters that are stable against resampling give rise to local maxima of this figure of merit. This is presented first for a one-dimensional data set, for which an analytic approximation for the figure of merit is derived and compared with numerical measurements. Next, the applicability of the method is demonstrated for higher-dimensional data, including gene microarray expression data.

Cluster Analysis↗
PubMed2005

MILANO--custom annotation of microarray results using automatic literature searches.

BACKGROUND: High-throughput genomic research tools are becoming standard in the biologist's toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining--the process of representing literature data in a fashion that is easy to relate to genomic data--is one solution to this problem. RESULTS: We present a web-based tool, MILANO (Microarray Literature-based Annotation), that allows annotation of lists of genes derived from microarray results by user defined terms. Our annotation strategy is based on counting the number of literature co-occurrences of each gene on the list with a user defined term. This strategy allows the customization of the annotation procedure and thus overcomes one of the major limitations of the functional annotations usually provided with microarray results. MILANO expands the gene names to include all their informative synonyms while filtering out gene symbols that are likely to be less informative as literature searching terms. MILANO supports searching two literature databases: GeneRIF and Medline (through PubMed), allowing retrieval of both quick and comprehensive results. We demonstrate MILANO's ability to improve microarray analysis by analyzing a list of 150 genes that were affected by p53 overproduction. This analysis reveals that MILANO enables immediate identification of known p53 target genes on this list and assists in sorting the list into genes known to be involved in p53 related pathways, apoptosis and cell cycle arrest. CONCLUSIONS: MILANO provides a useful tool for the automatic custom annotation of microarray results which is based on all the available literature. MILANO has two major advances over similar tools: the ability to expand gene names to include all their informative synonyms while removing synonyms that are not informative and access to the GeneRIF database which provides short summaries of curated articles relevant to known genes. MILANO is available at http://milano.md.huji.ac.il.

Algorithms↗
PubMed2006

Metric for measuring the effectiveness of clustering of DNA microarray expression.

BACKGROUND: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances; hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecular functions. RESULTS: In this paper we have proposed a metric to measure the effectiveness of clustering algorithms of genes by computing inter-cluster cohesiveness and as well as the intra-cluster separation with respect to biological features such as biological processes or molecular functions. We have applied this metric to the clusters on the data set that we have created as part of a larger study to determine the cancer suppressive mechanism of a class of chemicals called retinoids.We have considered hierarchal and k-means clustering with Euclidian and Pearson correlation distances. Our results show that genes of similar expression profiles are more likely to be closely related to biological processes than they are to molecular functions. The findings have been supported by many works in the area of gene clustering. CONCLUSION: The best clustering algorithm of genes must achieve cohesiveness within a cluster with respect to some biological features, and as well as maximum separation between clusters in terms of the distribution of genes of a behavioral group across clusters. We claim that our proposed metric is novel in this respect and that it provides a measure of both inter and intra cluster cohesiveness. Best of all, computation of the proposed metric is easy and it provides a single quantitative value, which makes comparison of different algorithms easier. The maximum cluster cohesiveness and the maximum intra-cluster separation are indicated by the metric when its value is 0.We have demonstrated the metric by applying it to a data set with gene behavioral groupings such as biological process and molecular functions. The metric can be easily extended to other features of a gene such as DNA binding sites and protein-protein interactions of the gene product, special features of the intron-exon structure, promoter characteristics, etc. The metric can also be used in other domains that use two different parametric spaces; one for clustering and the other one for measuring the effectiveness.

Algorithms↗
PubMed2005

Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach.

BACKGROUND: The study of the network between transcription factors and their targets is important for understanding the complex regulatory mechanisms in a cell. Unfortunately, with standard microarray experiments it is not possible to measure the transcription factor activities (TFAs) directly, as their own transcription levels are subject to post-translational modifications. RESULTS: Here we propose a statistical approach based on partial least squares (PLS) regression to infer the true TFAs from a combination of mRNA expression and DNA-protein binding measurements. This method is also statistically sound for small samples and allows the detection of functional interactions among the transcription factors via the notion of "meta"-transcription factors. In addition, it enables false positives to be identified in ChIP data and activation and suppression activities to be distinguished. CONCLUSION: The proposed method performs very well both for simulated data and for real expression and ChIP data from yeast and E. Coli experiments. It overcomes the limitations of previously used approaches to estimating TFAs. The estimated profiles may also serve as input for further studies, such as tests of periodicity or differential regulation. An R package "plsgenomics" implementing the proposed methods is available for download from the CRAN archive.

Algorithms↗
PubMed2005

Analysis of significance patterns identifies ubiquitous and disease-specific gene-expression signatures in patient peripheral blood leukocytes.

The utilization of gene-expression microarrays in patient-based research creates new prospects for the discovery of diagnostic biomarkers and the identification of genes or pathways linked to pathogenesis. Gene-expression signatures in peripheral blood mononuclear cells isolated from over one hundred patients with conditions presenting a strong immunological component (patient with autoimmune, graft versus host and infectious diseases, as well as immunosuppressed transplant recipients) were generated. This dataset provides the opportunity to carry out comparative analyses and define disease signatures in a broader context. Transcriptional changes of 22,283 probe sets were evaluated through statistical group comparison performed systematically for seven diseases versus their respective healthy control group. Patterns of significance were generated by hierarchical clustering of P-values. This approach led to the identification of a SLE-specific "diagnostic signature," formed by genes that did not change compared to healthy subjects in the other six diseases. Conversely, a "sentinel signature" that was common to all seven diseases was characterized. These findings bring new perspectives for the application of blood leukocyte expression signatures for diagnosis and early disease detection.

Adult↗