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 901 records · Page 50Linked to original sources
PubMed2000

Multivariate measurement of gene expression relationships.

The operational activities of cells are based on an awareness of their current state, coupled to a programmed response to internal and external cues in a context-dependent manner. One key goal of functional genomics is to develop analytical methods for delineating the ways in which the individual actions of genes are integrated into our understanding of the increasingly complex systems of organelle, cell, organ, and organism. This paper describes a novel approach to assess the codetermination of gene transcriptional states based upon statistical evaluation of reliably informative subsets of data derived from large-scale simultaneous gene expression measurements with cDNA microarrays. The method finds associations between the expression patterns of individual genes by determining whether knowledge of the transcriptional levels of a small gene set can be used to predict the associated transcriptional state of another gene. To test this approach for identification of the relevant contextual elements of cellular response, we have modeled our approach using data from known gene response pathways including ionizing radiation and downstream targets of inactivating gene mutations. This approach strongly suggests that evaluation of the transcriptional status of a given gene(s) can be combined with data from global expression analyses to predict the expression level of another gene. With data sets of the size currently available, this approach should be useful in finding sets of genes that participate in particular biological processes. As larger data sets and more computing power become available, the method can be extended to validating and ultimately identifying biologic (transcriptional) pathways based upon large-scale gene expression analysis.

Cyclin-Dependent Kinase Inhibitor p21↗
PubMed2004

Heat shock protein 12A shows reduced expression in the prefrontal cortex of subjects with schizophrenia.

BACKGROUND: Deoxyribonucleic acid microarray analyses of dorsolateral prefrontal cortex (DLPFC) area 9 from 10 matched pairs of schizophrenic and control subjects revealed a consistent and significant decrease (p = .001; mean log2 signal difference = -.58) in transcript expression for a gene clone KIAA0417. This database entry has been recently annotated as two highly homologous members of a heat-shock protein family (HSPA12A and HSPA12B). METHODS: We followed up our initial results by in situ hybridization in subjects with schizophrenia, major depression, and a chronic haloperidol-treated nonhuman primate model. Furthermore, we investigated the distribution of HSPA12A and HSPA12B transcripts across the human and nonhuman primate brain. RESULTS: We found that HSPA12A (but not HSPA12B) is highly expressed in the human brain and shows a neuron- and region-specific transcript distribution, with strongest expression in the frontal and occipital cortical regions. HSPA12A messenger ribonucleic acid was significantly reduced (p < .01; mean log2 optical density difference = -.84) across subjects with schizophrenia but not in the DLPFC of subjects with major depression or in monkeys chronically treated with haloperidol. CONCLUSIONS: The data are consistent with metabolic alterations in schizophrenia, reflected in selective changes in the expression of certain genes encoding proteins involved in cellular metabolism or metabolic responsiveness.

Adult↗
PubMed2004

Optimisation of a silicon/silicon dioxide substrate for a fluorescence DNA microarray.

This paper presents a comprehensive theory and experimental characterisation of the modulation of the fluorescence intensity by the construction of optical interferences on oxidised silicon substrates used for DNA microarrays. The model predicts a 90-fold variation of the fluorescence signal depending on the oxide thickness. For a Cy3 dye, the signal is maximal for a 90 nm oxide thickness corresponding to a 7.5-fold enhancement with respect to a standard glass substrate. For experimental validation of the model, we have prepared Si/SiO2 substrates with different parallel steps of decreasing oxide thicknesses on the same sample using a buffered oxide etch (BOE) etching process after thermal oxidation. The SiO2 surface has been functionalized by a silane monolayer before in situ synthesis of L185 oligonucleotide probes. After hybridisation with complementary targets, the variations of the fluorescence intensity versus oxide thickness are in very good accordance with the theoretical model. The experimental comparison against a glass substrate shows a 10-fold enhancement of the detection sensitivity. Our results demonstrate that a Si/SiO2 substrate is an attractive alternative to standard glass slides for the realisation of fluorescence DNA microarrays whenever detection sensitivity is an important issue.

Coated Materials, Biocompatible↗
PubMed2005

Assessment of porous silicon substrate for well-characterised sensitive DNA chip implement.

A biochip approach based on porous silicon as substrate is presented. The goal is to enhance the sensitivity of the biochip by increasing the specific surface area on the support. The elaboration of porous silicon layers has been optimized to guarantee good accessibility for large bio-molecule targets. Oligonucleotide probes are synthesised directly on the surface using phosphoramidite chemistry. The high specific surface area of porous silicon allows the direct characterisation, by infrared spectroscopy, of the porous layer formation and the functionalisation steps. The monolayer grafting and derivatisation protocol is additionally characterized by wettability and fluorescence microscopy. The surface modification of porous layers (i.e. thermal oxidation and chemical derivatisation) ensures the stability of the structure against strong chemical reagents used during the direct oligonucleotide synthesis. Finally the protocol is successfully transferred to a flat Si/SiO(2) substrate, and validated by biological target specific recognition during hybridisation tests. In particular, radioactive measurements show a 10-fold enhancement of the oligonucleotide surface density on the porous silicon substrate compared to the flat thermal silica.

Crystallization↗
PubMed2005

Borrowing information from relevant microarray studies for sample classification using weighted partial least squares.

With an increasing number of publicly available microarray datasets, it becomes attractive to borrow information from other relevant studies to have more reliable and powerful analysis of a given dataset. We do not assume that subjects in the current study and other relevant studies are drawn from the same population as assumed by meta-analysis. In particular, the set of parameters in the current study may be different from that of the other studies. We consider sample classification based on gene expression profiles in this context. We propose two new methods, a weighted partial least squares (WPLS) method and a weighted penalized partial least squares (WPPLS) method, to build a classifier by a combined use of multiple datasets. The methods can weight the individual datasets depending on their relevance to the current study. A more standard approach is first to build a classifier using each of the individual datasets, then to combine the outputs of the multiple classifiers using a weighted voting. Using two quite different datasets on human heart failure, we show first that WPLS/WPPLS, by borrowing information from the other dataset, can improve the performance of PLS/PPLS built on only a single dataset. Second, WPLS/WPPLS performs better than the standard approach of combining multiple classifiers. Third, WPPLS can improve over WPLS, just as PPLS does over PLS for a single dataset.

Gene Expression Profiling↗
PubMed2005

Probing lipid-protein interactions using lipid microarrays.

Lipids are central to the regulation and control of several cellular functions. They form many of the important structural features of cells, and are critical members of cellular signal transduction pathways. Cellular dysfunction is often caused by errors in lipid signaling; therefore, the proteins that interact with, synthesize or metabolize the lipids are potential therapeutic targets. Characterizing the contingent of cellular lipids and their abundance and how this is associated with disease will facilitate understanding how to intervene to correct diseases caused by dysfunctional lipid signaling. Since lipid-signaling networks involve several classes of proteins it is essential to determine the identity and role of these proteins in order to understand the networks. These proteins may be receptors, effectors, transporters or enzymes. We present tools, specifically, a lipid microarray platform, to uncover lipid-binding effector proteins that function in lipid signaling pathways. Lipid microarrays will allow researchers to obtain a comparable fingerprint of the proteins from a cell or tissue that bind to lipids, and also enable the identification of functionally important lipid-binding proteins. By applying a systematic approach to the quantification of lipid-protein interactions, lipid microarrays will provide an integrated knowledge base for the human lipidome. These tools have the potential to identify and validate targets to improve personalized medicine and health.

Animals↗
PubMed2005

Assessing statistical precision, power, and robustness of alternative experimental designs for two color microarray platforms based on mixed effects models.

Recommendations on experimental designs for two color microarray systems have been generally conflicting as they pertain to the general choice between reference and non-reference loop designs. This conflict may currently exist because many previously published assessments may not have effectively connected design layout with the level of biological relative to technical replication. We reassess various reference and non-reference designs for statistical efficiency in terms of standard errors of mean differences, power of test, and robustness using recently developed mixed model software tools. In minimally replicated cases (n = 2), it appears that the reference design outperforms the classical loop design whereby a sample from each animal is used for only one particular array hybridization. Alternatively, the reference design was consistently inferior to those connected loop designs in which a sample from each animal is used in two different hybridizations. Nevertheless, the gap in power between these two designs diminished as the biological to residual variance ratio increased. The statistical efficiency of a single large classical loop design for the comparison of many treatments was demonstrated to be highly sensitive to missing arrays relative to a common reference design (n = 2). However, the use of two loops within an interwoven loop design was shown to be substantially more robust to missing arrays and statistically more efficient relative to a common reference design. Furthermore, the use of more than one loop leads to less disparity in precision and power comparisons between any two treatments.

Animals↗
PubMed2005

Clustering expressed genes on the basis of their association with a quantitative phenotype.

Cluster analyses of gene expression data are usually conducted based on their associations with the phenotype of a particular disease. Many disease traits have a clearly defined binary phenotype (presence or absence), so that genes can be clustered based on the differences of expression levels between the two contrasting phenotypic groups. For example, cluster analysis based on binary phenotype has been successfully used in tumour research. Some complex diseases have phenotypes that vary in a continuous manner and the method developed for a binary trait is not immediately applicable to a continuous trait. However, understanding the role of gene expression in these complex traits is of fundamental importance. Therefore, it is necessary to develop a new statistical method to cluster expressed genes based on their association with a quantitative trait phenotype. We developed a model-based clustering method to classify genes based on their association with a continuous phenotype. We used a linear model to describe the relationship between gene expression and the phenotypic value. The model effects of the linear model (linear regression coefficients) represent the strength of the association. We assumed that the model effects of each gene follow a mixture of several multivariate Gaussian distributions. Parameter estimation and cluster assignment were accomplished via an Expectation-Maximization (EM) algorithm. The method was verified by analysing two simulated datasets, and further demonstrated using real data generated in a microarray experiment for the study of gene expression associated with Alzheimer's disease.

Algorithms↗
PubMed2006

Discrimination of shifts in a soil microbial community associated with TNT-contamination using a functional ANOVA of 16S rRNA hybridized to oligonucleotide microarrays.

A functional ANOVA analysis of the thermal dissociation of RNA hybridized to DNA microarrays was used to improve discrimination between two soil microbial communities. Following hybridization of in vitro transcribed 16S rRNA derived from uncontaminated and 2,4,6-trinitrotoluene contaminated soils to an oligonucleotide microarray containing group- and species-specific perfect match (PM) probes and mismatch (MM) variants, thermal dissociation was used to analyze the nucleic acid bound to each PM-MM probe set. Functional ANOVA of the dissociation curves generally discriminated PM-MM probe sets when Td values (temperature at 50% probe-target dissociation) could not. Maximum discrimination for many PM and MM probes often occurred at temperatures greaterthan the Td. Comparison of signal intensities measured prior to dissociation analysis from hybridizations of the two soil samples revealed significant differences in domain-, group-, and species-specific probes. Functional ANOVA showed significantly different dissociation curves for 11 PM probes when hybridizations from the two soil samples were compared, even though initial signal intensities for 3 of the 11 did not vary.

Analysis of Variance↗
PubMed2005

Prognostic implication of laminin-5 gamma 2 chain expression in the invasive front of colorectal cancers, disclosed by area-specific four-point tissue microarrays.

The laminin-5 gamma 2 chain (LN-5gamma2) is known to be a marker of invasion in several cancer types. Our purpose was to examine the prognostic significance of LN-5gamma2 expression in different areas of individual colorectal cancers (CRCs) by using tissue microarrays (TMAs), and to clarify the optimal areas for prognostic assessment. Using formalin-fixed paraffin-embedded tissue blocks of pT3 primary CRCs resected from 120 patients, we constructed TMA blocks of tissue core specimens taken from the submucosal invasive front, subserosal invasive front, central area, and rolled edge of each tumor. Using these four-point TMA sets, cytoplasmic LN-5gamma2 expression was immunohistochemically surveyed, and the area-specific prognostic significance of LN-5gamma2 expression was evaluated. The data revealed that 35, 30, 15 and 10% of the 120 CRCs showed high-grade LN-5gamma2 expression in the submucosal invasive front, subserosal invasive front, central area and rolled edge, respectively. Disease-specific survival curves for the groups with high- and low-grade LN-5gamma2 in the submucosal invasive front and subserosal invasive front were different significantly or of marginal difference (respective 5-year survival rates: 54 and 78% for submucosal invasive front (P=0.030) and 58 and 75% for subserosal invasive front (P=0.055)). Multivariate analysis revealed that the grades of LN-5gamma2 expression in submucosal invasive front (hazard ratio=2.0, P=0.047) and subserosal invasive front (hazard ratio=2.9, P=0.0033) were independent prognostic factors. In contrast, the grades of LN-5gamma2 expression in the central area and rolled edge did not have a significant impact on patient prognosis. Analysis using area-specific four-point TMAs clearly demonstrated that LN-5gamma2 expression in the invasive front largely influences the degree of clinical aggressiveness of CRC and its tendency to metastasize.

Aged↗
PubMed2003

Expression of cyclin D1, D3, E, and p27 in human renal cell carcinoma analysed by tissue microarray.

Aberrations in the G1/S transition of the cell cycle have been observed in many malignancies and seem to be critical in the transformation process. Few studies have delineated the presence of G1/S regulatory defects and their clinical relevance in renal cell carcinoma (RCC). Therefore, we have examined the protein contents of cyclin D1, D3, E, and p27 in 218 RCCs, using tissue microarray and immunohistochemistry. The results from a subset of tumours were confirmed by Western blotting and immunohistochemical staining of regular tissue sections. Interestingly, low protein contents of cyclin D1 and p27 were associated with high nuclear grade, large tumour size, and poor prognosis for patients with conventional tumours. We further observed substantial differences in the pattern of G1/S regulatory defects between the different RCC subtypes. The majority of both conventional and papillary cases expressed p27; however, chromophobe tumours generally lacked p27 staining. In addition, conventional RCCs often expressed high cyclin D1 protein levels, while papillary RCCs exhibited high cyclin E. In summary, we have shown that G1/S regulatory defects are present in RCC and are associated with clinico-pathological parameters. The pattern of cell cycle regulatory defects also differed between RCC subtypes.

Actuarial Analysis↗
PubMed2005

Amplification of Cyclin L1 is associated with lymph node metastases in head and neck squamous cell carcinoma (HNSCC).

Overrepresentation of chromosomal bands 3q25-q29 has been associated with shortened disease-specific survival in head and neck squamous cell carcinoma (HNSCC). To assess the prevalence of copy number gains (>4 signals per cell) and high-level amplifications (>8 signals per cell) from putative oncogenes in this chromosomal region (CCNL1, SNO, PIK3CA, TP73L), tissue microarray analysis was applied on 280 HNSCCs by fluorescence in situ hybridization. Overall frequency of additional copy numbers was 34.3% for CCNL1, 31.8% for SNO, 39.0% for PIK3CA and 38.3% for TP73L, respectively. In general, gains were more frequently detected in stage IV compared to stage I-III tumours. Performing multivariate logistic regression analysis, a significant association of CCNL1 gains and the presence of lymph node metastases was found, which was independent of anatomical site and T-stage of the primary tumour (P=0.049). Site-specific subgroup analysis further showed that copy number gains of CCNL1 and SNO occurred more frequently in oral carcinomas in advanced clinical stages as compared to N0 oral lesions (CCNL1: P=0.03; SNO: P=0.03). Finally, Kaplan-Meier analysis revealed that high-level amplifications of CCNL1 correlated with shorter overall survival of the patients. Our results indicate that CCNL1 plays a critical role in the loco-regional progression of HNSCC and may serve as an indicator for occult advanced tumour stages.

Biomarkers, Tumor↗
PubMed2006

Factors influencing p53 expression in ovarian cancer as a biomarker of clinical outcome in multicentre studies.

The prognostic impact of p53 immunostaining in a large series of tumours from epithelial ovarian cancer patients in a two-centre study was analysed. The study population (n=476) comprised of a retrospective series of 188 patients (Dutch cohort) and a prospective series of 288 patients (Scottish cohort) enrolled in clinical trials. P53 expression was determined by immunohistochemistry on tissue microarrays. Association with progression-free survival (PFS) and overall survival (OS) was analysed by univariate and multivariate Cox regression analysis. Aberrant p53 overexpression was significantly associated with PFS in the Dutch and Scottish cohorts (P=0.001 and 0.038, respectively), but not with OS in univariate analysis. In multivariate analysis, when the two groups were combined and account taken of clinical factors and country of origin of the cohort, p53 expression was not an independent prognostic predictor of PFS or OS. In this well-powered study with minimal methodological variability, p53 immunostaining is not an independent prognostic marker of clinical outcome in epithelial ovarian cancer. The data demonstrate the importance of methodological standardisation, particularly defining patient characteristics and survival end-point data, if biomarker data from multicentre studies are to be combined.

Adult↗
PubMed2006

Identification of a molecular signature predictive of sensitivity to differentiation induction in acute myeloid leukemia.

Acute myeloid leukemia (AML) blasts are immature committed myeloid cells unable to spontaneously undergo terminal maturation, and characterized by heterogeneous sensitivity to natural differentiation inducers. Here, we show a molecular signature predicting the resistance or sensitivity of six myeloid cell lines to differentiation induced in vitro with retinoic acid or vitamin D. The identified signature was further validated by TaqMan assay for the prediction of response to an in vitro differentiation assay performed on 28 freshly isolated AML blast populations. The TaqMan assay successfully predicts the in vitro resistance or responsiveness of AML blasts to differentiation inducers. Furthermore, performing a meta-analysis of publicly available microarray data sets, we also show the accuracy of our prediction on known phenotypes and suggest that our signature could become useful for the identification of patients eligible for new therapeutic strategies.

Acute Disease↗
PubMed2002

A gene-expression signature as a predictor of survival in breast cancer.

BACKGROUND: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. METHODS: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. RESULTS: Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. CONCLUSIONS: The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

Adult↗
PubMed2001

Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments.

We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has previously been used to obtain confidence intervals for estimates of differential expression for individual genes. Here we apply bootstrapping to assess the stability of results from a cluster analysis. We illustrate the technique with a publicly available data set and draw conclusions about the reliability of clustering results in light of variation in the data. The bootstrapping procedure relies on experimental replication. We discuss the implications of replication and good design in microarray experiments.

Analysis of Variance↗
PubMed2004

A knowledge-based clustering algorithm driven by Gene Ontology.

We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.

Algorithms↗
PubMed2005

Nonparametric methods for microarray data based on exchangeability and borrowed power.

This article proposes nonparametric inference procedures for analyzing microarray gene expression data that are reliable, robust, and simple to implement. They are conceptually transparent and require no special-purpose software. The analysis begins by normalizing gene expression data in a unique way. The resulting adjusted observations consist of gene-treatment interaction terms (representing differential expression) and error terms. The error terms are considered to be exchangeable, which is the only substantial assumption. Thus, under a family null hypothesis of no differential expression, the adjusted observations are exchangeable and all permutations of the observations are equally probable. The investigator may use the adjusted observations directly in a distribution-free test method or use their ranks in a rank-based method, where the ranking is taken over the whole data set. For the latter, the essential steps are as follows: (1) Calculate a Wilcoxon rank-sum difference or a corresponding Kruskal-Wallis rank statistic for each gene. (2) Randomly permute the observations and repeat the previous step. (3) Independently repeat the random permutation a suitable number of times. Under the exchangeability assumption, the permutation statistics are independent random draws from a null cumulative distribution function (c.d.f) approximated by the empirical c.d.f Reference to the empirical c.d.f tells if the test statistic for a gene is outlying and, hence, shows differential expression. This feature is judged by using an appropriate rejection region or computing a p-value for each test statistic, taking into account multiple testing. The distribution-free analog of the rank-based approach is also available and has parallel steps which are described in the article. The proposed nonparametric analysis tends to give good results with no additional refinement, although a few refinements are presented that may interest some investigators. The implementation is illustrated with a case application involving differential gene expression in wild-type and knockout mice of an E. coli lipopolysaccharide (LPS) endotoxin treatment, relative to a baseline untreated condition.

Animals↗