Importing MAGE-ML format microarray data into BioConductor.

PubMed

Durinck, Steffen; Allemeersch, Joke; Carey, Vincent J; Moreau, Yves; De Moor, Bart

2004-12-12

The microarray gene expression markup language (MAGE-ML) is a widely used XML (eXtensible Markup Language) standard for describing and exchanging information about microarray experiments. It can describe microarray designs, microarray experiment designs, gene expression data and data analysis results. We describe RMAGEML, a new Bioconductor package that provides a link between cDNA microarray data stored in MAGE-ML format and the Bioconductor framework for preprocessing, visualization and analysis of microarray experiments. http://www.bioconductor.org. Open Source.

A genome-wide 20 K citrus microarray for gene expression analysis

PubMed Central

Martinez-Godoy, M Angeles; Mauri, Nuria; Juarez, Jose; Marques, M Carmen; Santiago, Julia; Forment, Javier; Gadea, Jose

2008-01-01

Background Understanding of genetic elements that contribute to key aspects of citrus biology will impact future improvements in this economically important crop. Global gene expression analysis demands microarray platforms with a high genome coverage. In the last years, genome-wide EST collections have been generated in citrus, opening the possibility to create new tools for functional genomics in this crop plant. Results We have designed and constructed a publicly available genome-wide cDNA microarray that include 21,081 putative unigenes of citrus. As a functional companion to the microarray, a web-browsable database [1] was created and populated with information about the unigenes represented in the microarray, including cDNA libraries, isolated clones, raw and processed nucleotide and protein sequences, and results of all the structural and functional annotation of the unigenes, like general description, BLAST hits, putative Arabidopsis orthologs, microsatellites, putative SNPs, GO classification and PFAM domains. We have performed a Gene Ontology comparison with the full set of Arabidopsis proteins to estimate the genome coverage of the microarray. We have also performed microarray hybridizations to check its usability. Conclusion This new cDNA microarray replaces the first 7K microarray generated two years ago and allows gene expression analysis at a more global scale. We have followed a rational design to minimize cross-hybridization while maintaining its utility for different citrus species. Furthermore, we also provide access to a website with full structural and functional annotation of the unigenes represented in the microarray, along with the ability to use this site to directly perform gene expression analysis using standard tools at different publicly available servers. Furthermore, we show how this microarray offers a good representation of the citrus genome and present the usefulness of this genomic tool for global studies in citrus by using it to catalogue genes expressed in citrus globular embryos. PMID:18598343

Automatic Identification and Quantification of Extra-Well Fluorescence in Microarray Images.

PubMed

Rivera, Robert; Wang, Jie; Yu, Xiaobo; Demirkan, Gokhan; Hopper, Marika; Bian, Xiaofang; Tahsin, Tasnia; Magee, D Mitchell; Qiu, Ji; LaBaer, Joshua; Wallstrom, Garrick

2017-11-03

In recent studies involving NAPPA microarrays, extra-well fluorescence is used as a key measure for identifying disease biomarkers because there is evidence to support that it is better correlated with strong antibody responses than statistical analysis involving intraspot intensity. Because this feature is not well quantified by traditional image analysis software, identification and quantification of extra-well fluorescence is performed manually, which is both time-consuming and highly susceptible to variation between raters. A system that could automate this task efficiently and effectively would greatly improve the process of data acquisition in microarray studies, thereby accelerating the discovery of disease biomarkers. In this study, we experimented with different machine learning methods, as well as novel heuristics, for identifying spots exhibiting extra-well fluorescence (rings) in microarray images and assigning each ring a grade of 1-5 based on its intensity and morphology. The sensitivity of our final system for identifying rings was found to be 72% at 99% specificity and 98% at 92% specificity. Our system performs this task significantly faster than a human, while maintaining high performance, and therefore represents a valuable tool for microarray image analysis.

Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice.

PubMed

Bruno, D L; Ganesamoorthy, D; Schoumans, J; Bankier, A; Coman, D; Delatycki, M; Gardner, R J M; Hunter, M; James, P A; Kannu, P; McGillivray, G; Pachter, N; Peters, H; Rieubland, C; Savarirayan, R; Scheffer, I E; Sheffield, L; Tan, T; White, S M; Yeung, A; Bowman, Z; Ngo, C; Choy, K W; Cacheux, V; Wong, L; Amor, D J; Slater, H R

2009-02-01

Microarray genome analysis is realising its promise for improving detection of genetic abnormalities in individuals with mental retardation and congenital abnormality. Copy number variations (CNVs) are now readily detectable using a variety of platforms and a major challenge is the distinction of pathogenic from ubiquitous, benign polymorphic CNVs. The aim of this study was to investigate replacement of time consuming, locus specific testing for specific microdeletion and microduplication syndromes with microarray analysis, which theoretically should detect all known syndromes with CNV aetiologies as well as new ones. Genome wide copy number analysis was performed on 117 patients using Affymetrix 250K microarrays. 434 CNVs (195 losses and 239 gains) were found, including 18 pathogenic CNVs and 9 identified as "potentially pathogenic". Almost all pathogenic CNVs were larger than 500 kb, significantly larger than the median size of all CNVs detected. Segmental regions of loss of heterozygosity larger than 5 Mb were found in 5 patients. Genome microarray analysis has improved diagnostic success in this group of patients. Several examples of recently discovered "new syndromes" were found suggesting they are more common than previously suspected and collectively are likely to be a major cause of mental retardation. The findings have several implications for clinical practice. The study revealed the potential to make genetic diagnoses that were not evident in the clinical presentation, with implications for pretest counselling and the consent process. The importance of contributing novel CNVs to high quality databases for genotype-phenotype analysis and review of guidelines for selection of individuals for microarray analysis is emphasised.

MGDB: crossing the marker genes of a user microarray with a database of public-microarrays marker genes.

PubMed

Huerta, Mario; Munyi, Marc; Expósito, David; Querol, Enric; Cedano, Juan

2014-06-15

The microarrays performed by scientific teams grow exponentially. These microarray data could be useful for researchers around the world, but unfortunately they are underused. To fully exploit these data, it is necessary (i) to extract these data from a repository of the high-throughput gene expression data like Gene Expression Omnibus (GEO) and (ii) to make the data from different microarrays comparable with tools easy to use for scientists. We have developed these two solutions in our server, implementing a database of microarray marker genes (Marker Genes Data Base). This database contains the marker genes of all GEO microarray datasets and it is updated monthly with the new microarrays from GEO. Thus, researchers can see whether the marker genes of their microarray are marker genes in other microarrays in the database, expanding the analysis of their microarray to the rest of the public microarrays. This solution helps not only to corroborate the conclusions regarding a researcher's microarray but also to identify the phenotype of different subsets of individuals under investigation, to frame the results with microarray experiments from other species, pathologies or tissues, to search for drugs that promote the transition between the studied phenotypes, to detect undesirable side effects of the treatment applied, etc. Thus, the researcher can quickly add relevant information to his/her studies from all of the previous analyses performed in other studies as long as they have been deposited in public repositories. Marker-gene database tool: http://ibb.uab.es/mgdb © The Author 2014. Published by Oxford University Press.

Identification of candidate genes in osteoporosis by integrated microarray analysis.

PubMed

Li, J J; Wang, B Q; Fei, Q; Yang, Y; Li, D

2016-12-01

In order to screen the altered gene expression profile in peripheral blood mononuclear cells of patients with osteoporosis, we performed an integrated analysis of the online microarray studies of osteoporosis. We searched the Gene Expression Omnibus (GEO) database for microarray studies of peripheral blood mononuclear cells in patients with osteoporosis. Subsequently, we integrated gene expression data sets from multiple microarray studies to obtain differentially expressed genes (DEGs) between patients with osteoporosis and normal controls. Gene function analysis was performed to uncover the functions of identified DEGs. A total of three microarray studies were selected for integrated analysis. In all, 1125 genes were found to be significantly differentially expressed between osteoporosis patients and normal controls, with 373 upregulated and 752 downregulated genes. Positive regulation of the cellular amino metabolic process (gene ontology (GO): 0033240, false discovery rate (FDR) = 1.00E + 00) was significantly enriched under the GO category for biological processes, while for molecular functions, flavin adenine dinucleotide binding (GO: 0050660, FDR = 3.66E-01) and androgen receptor binding (GO: 0050681, FDR = 6.35E-01) were significantly enriched. DEGs were enriched in many osteoporosis-related signalling pathways, including those of mitogen-activated protein kinase (MAPK) and calcium. Protein-protein interaction (PPI) network analysis showed that the significant hub proteins contained ubiquitin specific peptidase 9, X-linked (Degree = 99), ubiquitin specific peptidase 19 (Degree = 57) and ubiquitin conjugating enzyme E2 B (Degree = 57). Analysis of gene function of identified differentially expressed genes may expand our understanding of fundamental mechanisms leading to osteoporosis. Moreover, significantly enriched pathways, such as MAPK and calcium, may involve in osteoporosis through osteoblastic differentiation and bone formation.Cite this article: J. J. Li, B. Q. Wang, Q. Fei, Y. Yang, D. Li. Identification of candidate genes in osteoporosis by integrated microarray analysis. Bone Joint Res 2016;5:594-601. DOI: 10.1302/2046-3758.512.BJR-2016-0073.R1. © 2016 Fei et al.

The Longhorn Array Database (LAD): An Open-Source, MIAME compliant implementation of the Stanford Microarray Database (SMD)

PubMed Central

Killion, Patrick J; Sherlock, Gavin; Iyer, Vishwanath R

2003-01-01

Background The power of microarray analysis can be realized only if data is systematically archived and linked to biological annotations as well as analysis algorithms. Description The Longhorn Array Database (LAD) is a MIAME compliant microarray database that operates on PostgreSQL and Linux. It is a fully open source version of the Stanford Microarray Database (SMD), one of the largest microarray databases. LAD is available at Conclusions Our development of LAD provides a simple, free, open, reliable and proven solution for storage and analysis of two-color microarray data. PMID:12930545

MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform.

PubMed

Gupta, Surya; De Puysseleyr, Veronic; Van der Heyden, José; Maddelein, Davy; Lemmens, Irma; Lievens, Sam; Degroeve, Sven; Tavernier, Jan; Martens, Lennart

2017-05-01

Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments. MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT. jan.tavernier@vib-ugent.be or lennart.martens@vib-ugent.be. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

The application of DNA microarrays in gene expression analysis.

PubMed

van Hal, N L; Vorst, O; van Houwelingen, A M; Kok, E J; Peijnenburg, A; Aharoni, A; van Tunen, A J; Keijer, J

2000-03-31

DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.

The Glycan Microarray Story from Construction to Applications.

PubMed

Hyun, Ji Young; Pai, Jaeyoung; Shin, Injae

2017-04-18

Not only are glycan-mediated binding processes in cells and organisms essential for a wide range of physiological processes, but they are also implicated in various pathological processes. As a result, elucidation of glycan-associated biomolecular interactions and their consequences is of great importance in basic biological research and biomedical applications. In 2002, we and others were the first to utilize glycan microarrays in efforts aimed at the rapid analysis of glycan-associated recognition events. Because they contain a number of glycans immobilized in a dense and orderly manner on a solid surface, glycan microarrays enable multiple parallel analyses of glycan-protein binding events while utilizing only small amounts of glycan samples. Therefore, this microarray technology has become a leading edge tool in studies aimed at elucidating roles played by glycans and glycan binding proteins in biological systems. In this Account, we summarize our efforts on the construction of glycan microarrays and their applications in studies of glycan-associated interactions. Immobilization strategies of functionalized and unmodified glycans on derivatized glass surfaces are described. Although others have developed immobilization techniques, our efforts have focused on improving the efficiencies and operational simplicity of microarray construction. The microarray-based technology has been most extensively used for rapid analysis of the glycan binding properties of proteins. In addition, glycan microarrays have been employed to determine glycan-protein interactions quantitatively, detect pathogens, and rapidly assess substrate specificities of carbohydrate-processing enzymes. More recently, the microarrays have been employed to identify functional glycans that elicit cell surface lectin-mediated cellular responses. Owing to these efforts, it is now possible to use glycan microarrays to expand the understanding of roles played by glycans and glycan binding proteins in biological systems.

Diagnostic Yield of Chromosomal Microarray Analysis in a Cohort of Patients with Autism Spectrum Disorders from a Highly Consanguineous Population

ERIC Educational Resources Information Center

Al-Mamari, Watfa; Al-Saegh, Abeer; Al-Kindy, Adila; Bruwer, Zandre; Al-Murshedi, Fathiya; Al-Thihli, Khalid

2015-01-01

Autism Spectrum Disorders are a complicated group of disorders characterized with heterogeneous genetic etiologies. The genetic investigations for this group of disorders have expanded considerably over the past decade. In our study we designed a tired approach and studied the diagnostic yield of chromosomal microarray analysis on patients…

Identifying molecular features for prostate cancer with Gleason 7 based on microarray gene expression profiles.

PubMed

Bălăcescu, Loredana; Bălăcescu, O; Crişan, N; Fetica, B; Petruţ, B; Bungărdean, Cătălina; Rus, Meda; Tudoran, Oana; Meurice, G; Irimie, Al; Dragoş, N; Berindan-Neagoe, Ioana

2011-01-01

Prostate cancer represents the first leading cause of cancer among western male population, with different clinical behavior ranging from indolent to metastatic disease. Although many molecules and deregulated pathways are known, the molecular mechanisms involved in the development of prostate cancer are not fully understood. The aim of this study was to explore the molecular variation underlying the prostate cancer, based on microarray analysis and bioinformatics approaches. Normal and prostate cancer tissues were collected by macrodissection from prostatectomy pieces. All prostate cancer specimens used in our study were Gleason score 7. Gene expression microarray (Agilent Technologies) was used for Whole Human Genome evaluation. The bioinformatics and functional analysis were based on Limma and Ingenuity software. The microarray analysis identified 1119 differentially expressed genes between prostate cancer and normal prostate, which were up- or down-regulated at least 2-fold. P-values were adjusted for multiple testing using Benjamini-Hochberg method with a false discovery rate of 0.01. These genes were analyzed with Ingenuity Pathway Analysis software and were established 23 genetic networks. Our microarray results provide new information regarding the molecular networks in prostate cancer stratified as Gleason 7. These data highlighted gene expression profiles for better understanding of prostate cancer progression.

A probabilistic framework for microarray data analysis: fundamental probability models and statistical inference.

PubMed

Ogunnaike, Babatunde A; Gelmi, Claudio A; Edwards, Jeremy S

2010-05-21

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

16S rRNA gene-based phylogenetic microarray for simultaneous identification of members of the genus Burkholderia.

PubMed

Schönmann, Susan; Loy, Alexander; Wimmersberger, Céline; Sobek, Jens; Aquino, Catharine; Vandamme, Peter; Frey, Beat; Rehrauer, Hubert; Eberl, Leo

2009-04-01

For cultivation-independent and highly parallel analysis of members of the genus Burkholderia, an oligonucleotide microarray (phylochip) consisting of 131 hierarchically nested 16S rRNA gene-targeted oligonucleotide probes was developed. A novel primer pair was designed for selective amplification of a 1.3 kb 16S rRNA gene fragment of Burkholderia species prior to microarray analysis. The diagnostic performance of the microarray for identification and differentiation of Burkholderia species was tested with 44 reference strains of the genera Burkholderia, Pandoraea, Ralstonia and Limnobacter. Hybridization patterns based on presence/absence of probe signals were interpreted semi-automatically using the novel likelihood-based strategy of the web-tool Phylo- Detect. Eighty-eight per cent of the reference strains were correctly identified at the species level. The evaluated microarray was applied to investigate shifts in the Burkholderia community structure in acidic forest soil upon addition of cadmium, a condition that selected for Burkholderia species. The microarray results were in agreement with those obtained from phylogenetic analysis of Burkholderia 16S rRNA gene sequences recovered from the same cadmiumcontaminated soil, demonstrating the value of the Burkholderia phylochip for determinative and environmental studies.

Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

PubMed

Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

2008-06-18

Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.

Systematic evaluation of RNA quality, microarray data reliability and pathway analysis in fresh, fresh frozen and formalin-fixed paraffin-embedded tissue samples.

PubMed

Wimmer, Isabella; Tröscher, Anna R; Brunner, Florian; Rubino, Stephen J; Bien, Christian G; Weiner, Howard L; Lassmann, Hans; Bauer, Jan

2018-04-20

Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources commonly used in pathology. However, formalin fixation modifies nucleic acids challenging the isolation of high-quality RNA for genetic profiling. Here, we assessed feasibility and reliability of microarray studies analysing transcriptome data from fresh, fresh-frozen (FF) and FFPE tissues. We show that reproducible microarray data can be generated from only 2 ng FFPE-derived RNA. For RNA quality assessment, fragment size distribution (DV200) and qPCR proved most suitable. During RNA isolation, extending tissue lysis time to 10 hours reduced high-molecular-weight species, while additional incubation at 70 °C markedly increased RNA yields. Since FF- and FFPE-derived microarrays constitute different data entities, we used indirect measures to investigate gene signal variation and relative gene expression. Whole-genome analyses revealed high concordance rates, while reviewing on single-genes basis showed higher data variation in FFPE than FF arrays. Using an experimental model, gene set enrichment analysis (GSEA) of FFPE-derived microarrays and fresh tissue-derived RNA-Seq datasets yielded similarly affected pathways confirming the applicability of FFPE tissue in global gene expression analysis. Our study provides a workflow comprising RNA isolation, quality assessment and microarray profiling using minimal RNA input, thus enabling hypothesis-generating pathway analyses from limited amounts of precious, pathologically significant FFPE tissues.

The Microarray Revolution: Perspectives from Educators

ERIC Educational Resources Information Center

Brewster, Jay L.; Beason, K. Beth; Eckdahl, Todd T.; Evans, Irene M.

2004-01-01

In recent years, microarray analysis has become a key experimental tool, enabling the analysis of genome-wide patterns of gene expression. This review approaches the microarray revolution with a focus upon four topics: 1) the early development of this technology and its application to cancer diagnostics; 2) a primer of microarray research,…

Construction of a cDNA microarray derived from the ascidian Ciona intestinalis.

PubMed

Azumi, Kaoru; Takahashi, Hiroki; Miki, Yasufumi; Fujie, Manabu; Usami, Takeshi; Ishikawa, Hisayoshi; Kitayama, Atsusi; Satou, Yutaka; Ueno, Naoto; Satoh, Nori

2003-10-01

A cDNA microarray was constructed from a basal chordate, the ascidian Ciona intestinalis. The draft genome of Ciona has been read and inferred to contain approximately 16,000 protein-coding genes, and cDNAs for transcripts of 13,464 genes have been characterized and compiled as the "Ciona intestinalis Gene Collection Release I". In the present study, we constructed a cDNA microarray of these 13,464 Ciona genes. A preliminary experiment with Cy3- and Cy5-labeled probes showed extensive differential gene expression between fertilized eggs and larvae. In addition, there was a good correlation between results obtained by the present microarray analysis and those from previous EST analyses. This first microarray of a large collection of Ciona intestinalis cDNA clones should facilitate the analysis of global gene expression and gene networks during the embryogenesis of basal chordates.

Analysis of developmental gene conservation in the Actinomycetales using DNA/DNA microarray comparisons.

PubMed

Kirby, Ralph; Herron, Paul; Hoskisson, Paul

2011-02-01

Based on available genome sequences, Actinomycetales show significant gene synteny across a wide range of species and genera. In addition, many genera show varying degrees of complex morphological development. Using the presence of gene synteny as a basis, it is clear that an analysis of gene conservation across the Streptomyces and various other Actinomycetales will provide information on both the importance of genes and gene clusters and the evolution of morphogenesis in these bacteria. Genome sequencing, although becoming cheaper, is still relatively expensive for comparing large numbers of strains. Thus, a heterologous DNA/DNA microarray hybridization dataset based on a Streptomyces coelicolor microarray allows a cheaper and greater depth of analysis of gene conservation. This study, using both bioinformatical and microarray approaches, was able to classify genes previously identified as involved in morphogenesis in Streptomyces into various subgroups in terms of conservation across species and genera. This will allow the targeting of genes for further study based on their importance at the species level and at higher evolutionary levels.

Cross species analysis of microarray expression data

PubMed Central

Lu, Yong; Huggins, Peter; Bar-Joseph, Ziv

2009-01-01

Motivation: Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied. Results: In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them. Contact: zivbj@cs.cmu.edu PMID:19357096

A meta-data based method for DNA microarray imputation.

PubMed

Jörnsten, Rebecka; Ouyang, Ming; Wang, Hui-Yu

2007-03-29

DNA microarray experiments are conducted in logical sets, such as time course profiling after a treatment is applied to the samples, or comparisons of the samples under two or more conditions. Due to cost and design constraints of spotted cDNA microarray experiments, each logical set commonly includes only a small number of replicates per condition. Despite the vast improvement of the microarray technology in recent years, missing values are prevalent. Intuitively, imputation of missing values is best done using many replicates within the same logical set. In practice, there are few replicates and thus reliable imputation within logical sets is difficult. However, it is in the case of few replicates that the presence of missing values, and how they are imputed, can have the most profound impact on the outcome of downstream analyses (e.g. significance analysis and clustering). This study explores the feasibility of imputation across logical sets, using the vast amount of publicly available microarray data to improve imputation reliability in the small sample size setting. We download all cDNA microarray data of Saccharomyces cerevisiae, Arabidopsis thaliana, and Caenorhabditis elegans from the Stanford Microarray Database. Through cross-validation and simulation, we find that, for all three species, our proposed imputation using data from public databases is far superior to imputation within a logical set, sometimes to an astonishing degree. Furthermore, the imputation root mean square error for significant genes is generally a lot less than that of non-significant ones. Since downstream analysis of significant genes, such as clustering and network analysis, can be very sensitive to small perturbations of estimated gene effects, it is highly recommended that researchers apply reliable data imputation prior to further analysis. Our method can also be applied to cDNA microarray experiments from other species, provided good reference data are available.

EMAAS: An extensible grid-based Rich Internet Application for microarray data analysis and management

PubMed Central

Barton, G; Abbott, J; Chiba, N; Huang, DW; Huang, Y; Krznaric, M; Mack-Smith, J; Saleem, A; Sherman, BT; Tiwari, B; Tomlinson, C; Aitman, T; Darlington, J; Game, L; Sternberg, MJE; Butcher, SA

2008-01-01

Background Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management. Results EMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms. Conclusion EMAAS enables users to track and perform microarray data management and analysis tasks through a single easy-to-use web application. The system architecture is flexible and scalable to allow new array types, analysis algorithms and tools to be added with relative ease and to cope with large increases in data volume. PMID:19032776

An Introduction to MAMA (Meta-Analysis of MicroArray data) System.

PubMed

Zhang, Zhe; Fenstermacher, David

2005-01-01

Analyzing microarray data across multiple experiments has been proven advantageous. To support this kind of analysis, we are developing a software system called MAMA (Meta-Analysis of MicroArray data). MAMA utilizes a client-server architecture with a relational database on the server-side for the storage of microarray datasets collected from various resources. The client-side is an application running on the end user's computer that allows the user to manipulate microarray data and analytical results locally. MAMA implementation will integrate several analytical methods, including meta-analysis within an open-source framework offering other developers the flexibility to plug in additional statistical algorithms.

The efficacy of microarray screening for autosomal recessive retinitis pigmentosa in routine clinical practice

PubMed Central

van Huet, Ramon A. C.; Pierrache, Laurence H.M.; Meester-Smoor, Magda A.; Klaver, Caroline C.W.; van den Born, L. Ingeborgh; Hoyng, Carel B.; de Wijs, Ilse J.; Collin, Rob W. J.; Hoefsloot, Lies H.

2015-01-01

Purpose To determine the efficacy of multiple versions of a commercially available arrayed primer extension (APEX) microarray chip for autosomal recessive retinitis pigmentosa (arRP). Methods We included 250 probands suspected of arRP who were genetically analyzed with the APEX microarray between January 2008 and November 2013. The mode of inheritance had to be autosomal recessive according to the pedigree (including isolated cases). If the microarray identified a heterozygous mutation, we performed Sanger sequencing of exons and exon–intron boundaries of that specific gene. The efficacy of this microarray chip with the additional Sanger sequencing approach was determined by the percentage of patients that received a molecular diagnosis. We also collected data from genetic tests other than the APEX analysis for arRP to provide a detailed description of the molecular diagnoses in our study cohort. Results The APEX microarray chip for arRP identified the molecular diagnosis in 21 (8.5%) of the patients in our cohort. Additional Sanger sequencing yielded a second mutation in 17 patients (6.8%), thereby establishing the molecular diagnosis. In total, 38 patients (15.2%) received a molecular diagnosis after analysis using the microarray and additional Sanger sequencing approach. Further genetic analyses after a negative result of the arRP microarray (n = 107) resulted in a molecular diagnosis of arRP (n = 23), autosomal dominant RP (n = 5), X-linked RP (n = 2), and choroideremia (n = 1). Conclusions The efficacy of the commercially available APEX microarray chips for arRP appears to be low, most likely caused by the limitations of this technique and the genetic and allelic heterogeneity of RP. Diagnostic yields up to 40% have been reported for next-generation sequencing (NGS) techniques that, as expected, thereby outperform targeted APEX analysis. PMID:25999674

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ovacik, Meric A.; Sen, Banalata; Euling, Susan Y.

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significancemore » analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.« less

Clinical application of chromosomal microarray analysis for the prenatal diagnosis of chromosomal abnormalities and copy number variations in fetuses with congenital heart disease.

PubMed

Xia, Yu; Yang, Yongchao; Huang, Shufang; Wu, Yueheng; Li, Ping; Zhuang, Jian

2018-03-24

This study aimed to determine chromosomal abnormalities and copy number variations (CNVs) in fetuses with congenital heart disease (CHD) by chromosomal microarray analysis (CMA). One hundred and ten cases with CHD detected by prenatal echocardiography were enrolled in the study; 27 cases were simple CHDs, and 83 were complex CHDs. Chromosomal microarray analysis was performed on the Affymetrix CytoScan HD platform. All annotated CNVs were validated by quantitative PCR. Chromosomal microarray analysis identified 6 cases with chromosomal abnormalities, including 2 cases with trisomy 21, 2 cases with trisomy 18, 1 case with trisomy 13, and 1 unusual case of mosaic trisomy 21. Pathogenic CNVs were detected in 15.5% (17/110) of the fetuses with CHDs, including 13 cases with CHD-associated CNVs. We further identified 10 genes as likely novel CHD candidate genes through gene functional enrichment analysis. We also found that pathogenic CMA results impacted the rate of pregnancy termination. This study shows that CMA is particularly effective for identifying chromosomal abnormalities and CNVs in fetuses with CHDs as well as having an effect on obstetrical outcomes. The elucidation of the genetic basis of CHDs will continue to expand our understanding of the etiology of CHDs. © 2018 John Wiley & Sons, Ltd.

Microarray analysis of potential genes in the pathogenesis of recurrent oral ulcer.

PubMed

Han, Jingying; He, Zhiwei; Li, Kun; Hou, Lu

2015-01-01

Recurrent oral ulcer seriously threatens patients' daily life and health. This study investigated potential genes and pathways that participate in the pathogenesis of recurrent oral ulcer by high throughput bioinformatic analysis. RT-PCR and Western blot were applied to further verify screened interleukins effect. Recurrent oral ulcer related genes were collected from websites and papers, and further found out from Human Genome 280 6.0 microarray data. Each pathway of recurrent oral ulcer related genes were got through chip hybridization. RT-PCR was applied to test four recurrent oral ulcer related genes to verify the microarray data. Data transformation, scatter plot, clustering analysis, and expression pattern analysis were used to analyze recurrent oral ulcer related gene expression changes. Recurrent oral ulcer gene microarray was successfully established. Microarray showed that 551 genes involved in recurrent oral ulcer activity and 196 genes were recurrent oral ulcer related genes. Of them, 76 genes up-regulated, 62 genes down-regulated, and 58 genes up-/down-regulated. Total expression level up-regulated 752 times (60%) and down-regulated 485 times (40%). IL-2 plays an important role in the occurrence, development and recurrence of recurrent oral ulcer on the mRNA and protein levels. Gene microarray can be used to analyze potential genes and pathways in recurrent oral ulcer. IL-2 may be involved in the pathogenesis of recurrent oral ulcer.

A MICROARRAY ANALYSIS OF GENE EXPRESSION IN THE EMBRYONIC FORELIMB OF THE C57BL/6J MOUSE REVEALS SIGNIFICANT ALTERATIONS METABOLIC AND DEVELOPMENTAL REGULATION FOLLOWING ETHANOL EXPOSURE.

EPA Science Inventory

The observation of transcriptional changes following embryonic ethanol exposure may provide significant insights into the biological response to ethanol exposure. In this study, we used microarray analysis to examine the transcriptional response of the developing limb to a dose ...

MAAMD: a workflow to standardize meta-analyses and comparison of affymetrix microarray data

PubMed Central

2014-01-01

Background Mandatory deposit of raw microarray data files for public access, prior to study publication, provides significant opportunities to conduct new bioinformatics analyses within and across multiple datasets. Analysis of raw microarray data files (e.g. Affymetrix CEL files) can be time consuming, complex, and requires fundamental computational and bioinformatics skills. The development of analytical workflows to automate these tasks simplifies the processing of, improves the efficiency of, and serves to standardize multiple and sequential analyses. Once installed, workflows facilitate the tedious steps required to run rapid intra- and inter-dataset comparisons. Results We developed a workflow to facilitate and standardize Meta-Analysis of Affymetrix Microarray Data analysis (MAAMD) in Kepler. Two freely available stand-alone software tools, R and AltAnalyze were embedded in MAAMD. The inputs of MAAMD are user-editable csv files, which contain sample information and parameters describing the locations of input files and required tools. MAAMD was tested by analyzing 4 different GEO datasets from mice and drosophila. MAAMD automates data downloading, data organization, data quality control assesment, differential gene expression analysis, clustering analysis, pathway visualization, gene-set enrichment analysis, and cross-species orthologous-gene comparisons. MAAMD was utilized to identify gene orthologues responding to hypoxia or hyperoxia in both mice and drosophila. The entire set of analyses for 4 datasets (34 total microarrays) finished in ~ one hour. Conclusions MAAMD saves time, minimizes the required computer skills, and offers a standardized procedure for users to analyze microarray datasets and make new intra- and inter-dataset comparisons. PMID:24621103

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

PubMed Central

Stekel, Dov J.; Sarti, Donatella; Trevino, Victor; Zhang, Lihong; Salmon, Mike; Buckley, Chris D.; Stevens, Mark; Pallen, Mark J.; Penn, Charles; Falciani, Francesco

2005-01-01

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples. PMID:15800204

Detection of clonal evolution in hematopoietic malignancies by combining comparative genomic hybridization and single nucleotide polymorphism arrays.

PubMed

Hartmann, Luise; Stephenson, Christine F; Verkamp, Stephanie R; Johnson, Krystal R; Burnworth, Bettina; Hammock, Kelle; Brodersen, Lisa Eidenschink; de Baca, Monica E; Wells, Denise A; Loken, Michael R; Zehentner, Barbara K

2014-12-01

Array comparative genomic hybridization (aCGH) has become a powerful tool for analyzing hematopoietic neoplasms and identifying genome-wide copy number changes in a single assay. aCGH also has superior resolution compared with fluorescence in situ hybridization (FISH) or conventional cytogenetics. Integration of single nucleotide polymorphism (SNP) probes with microarray analysis allows additional identification of acquired uniparental disomy, a copy neutral aberration with known potential to contribute to tumor pathogenesis. However, a limitation of microarray analysis has been the inability to detect clonal heterogeneity in a sample. This study comprised 16 samples (acute myeloid leukemia, myelodysplastic syndrome, chronic lymphocytic leukemia, plasma cell neoplasm) with complex cytogenetic features and evidence of clonal evolution. We used an integrated manual peak reassignment approach combining analysis of aCGH and SNP microarray data for characterization of subclonal abnormalities. We compared array findings with results obtained from conventional cytogenetic and FISH studies. Clonal heterogeneity was detected in 13 of 16 samples by microarray on the basis of log2 values. Use of the manual peak reassignment analysis approach improved resolution of the sample's clonal composition and genetic heterogeneity in 10 of 13 (77%) patients. Moreover, in 3 patients, clonal disease progression was revealed by array analysis that was not evident by cytogenetic or FISH studies. Genetic abnormalities originating from separate clonal subpopulations can be identified and further characterized by combining aCGH and SNP hybridization results from 1 integrated microarray chip by use of the manual peak reassignment technique. Its clinical utility in comparison to conventional cytogenetic or FISH studies is demonstrated. © 2014 American Association for Clinical Chemistry.

Microarray technology for major chemical contaminants analysis in food: current status and prospects.

PubMed

Zhang, Zhaowei; Li, Peiwu; Hu, Xiaofeng; Zhang, Qi; Ding, Xiaoxia; Zhang, Wen

2012-01-01

Chemical contaminants in food have caused serious health issues in both humans and animals. Microarray technology is an advanced technique suitable for the analysis of chemical contaminates. In particular, immuno-microarray approach is one of the most promising methods for chemical contaminants analysis. The use of microarrays for the analysis of chemical contaminants is the subject of this review. Fabrication strategies and detection methods for chemical contaminants are discussed in detail. Application to the analysis of mycotoxins, biotoxins, pesticide residues, and pharmaceutical residues is also described. Finally, future challenges and opportunities are discussed.

MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.

PubMed

Katsigiannis, Stamos; Zacharia, Eleni; Maroulis, Dimitris

2017-05-01

Complementary DNA (cDNA) microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images that often suffer from noise, artifacts, and uneven background. In this study, the MIGS-GPU [Microarray Image Gridding and Segmentation on Graphics Processing Unit (GPU)] software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the GPU by means of the compute unified device architecture (CUDA) in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a user-friendly interface that requires minimum input in order to run.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

PubMed Central

2012-01-01

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings. PMID:16964229

Women's experiences receiving abnormal prenatal chromosomal microarray testing results.

PubMed

Bernhardt, Barbara A; Soucier, Danielle; Hanson, Karen; Savage, Melissa S; Jackson, Laird; Wapner, Ronald J

2013-02-01

Genomic microarrays can detect copy-number variants not detectable by conventional cytogenetics. This technology is diffusing rapidly into prenatal settings even though the clinical implications of many copy-number variants are currently unknown. We conducted a qualitative pilot study to explore the experiences of women receiving abnormal results from prenatal microarray testing performed in a research setting. Participants were a subset of women participating in a multicenter prospective study "Prenatal Cytogenetic Diagnosis by Array-based Copy Number Analysis." Telephone interviews were conducted with 23 women receiving abnormal prenatal microarray results. We found that five key elements dominated the experiences of women who had received abnormal prenatal microarray results: an offer too good to pass up, blindsided by the results, uncertainty and unquantifiable risks, need for support, and toxic knowledge. As prenatal microarray testing is increasingly used, uncertain findings will be common, resulting in greater need for careful pre- and posttest counseling, and more education of and resources for providers so they can adequately support the women who are undergoing testing.

High-throughput DNA microarray detection of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley, Nepal.

PubMed

Inoue, Daisuke; Hinoura, Takuji; Suzuki, Noriko; Pang, Junqin; Malla, Rabin; Shrestha, Sadhana; Chapagain, Saroj Kumar; Matsuzawa, Hiroaki; Nakamura, Takashi; Tanaka, Yasuhiro; Ike, Michihiko; Nishida, Kei; Sei, Kazunari

2015-01-01

Because of heavy dependence on groundwater for drinking water and other domestic use, microbial contamination of groundwater is a serious problem in the Kathmandu Valley, Nepal. This study investigated comprehensively the occurrence of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley by applying DNA microarray analysis targeting 941 pathogenic bacterial species/groups. Water quality measurements found significant coliform (fecal) contamination in 10 of the 11 investigated groundwater samples and significant nitrogen contamination in some samples. The results of DNA microarray analysis revealed the presence of 1-37 pathogen species/groups, including 1-27 biosafety level 2 ones, in 9 of the 11 groundwater samples. While the detected pathogens included several feces- and animal-related ones, those belonging to Legionella and Arthrobacter, which were considered not to be directly associated with feces, were detected prevalently. This study could provide a rough picture of overall pathogenic bacterial contamination in the Kathmandu Valley, and demonstrated the usefulness of DNA microarray analysis as a comprehensive screening tool of a wide variety of pathogenic bacteria.

Microarray Analysis of Long Noncoding RNAs in Female Diabetic Peripheral Neuropathy Patients.

PubMed

Luo, Lin; Ji, Lin-Dan; Cai, Jiang-Jia; Feng, Mei; Zhou, Mi; Hu, Su-Pei; Xu, Jin; Zhou, Wen-Hua

2018-01-01

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus (DM). Because of its controversial pathogenesis, DPN is still not diagnosed or managed properly in most patients. In this study, human lncRNA microarrays were used to identify the differentially expressed lncRNAs in DM and DPN patients, and some of the discovered lncRNAs were further validated in additional 78 samples by quantitative realtime PCR (qRT-PCR). The microarray analysis identified 446 and 1327 differentially expressed lncRNAs in DM and DPN, respectively. The KEGG pathway analysis further revealed that the differentially expressed lncRNA-coexpressed mRNAs between DPN and DM groups were significantly enriched in the MAPK signaling pathway. The lncRNA/mRNA coexpression network indicated that BDNF and TRAF2 correlated with 6 lncRNAs. The qRT-PCR confirmed the initial microarray results. These findings demonstrated that the interplay between lncRNAs and mRNA may be involved in the pathogenesis of DPN, especially the neurotrophin-MAPK signaling pathway, thus providing relevant information for future studies. © 2018 The Author(s). Published by S. Karger AG, Basel.

Evaluation of a low density DNA microarray for small B-cell non-Hodgkin lymphoma differential diagnosis.

PubMed

Gillet, Jean-Pierre; Molina, Thierry Jo; Jamart, Jacques; Gaulard, Philippe; Leroy, Karen; Briere, Josette; Theate, Ivan; Thieblemont, Catherine; Bosly, Andre; Herin, Michel; Hamels, Jacques; Remacle, Jose

2009-03-01

Lymphomas are classified according to the World Health Organisation (WHO) classification which defines subtypes on the basis of clinical, morphological, immunophenotypic, molecular and cytogenetic criteria. Differential diagnosis of the subtypes is sometimes difficult, especially for small B-cell lymphoma (SBCL). Standardisation of molecular genetic assays using multiple gene expression analysis by microarrays could be a useful complement to the current diagnosis. The aim of the present study was to develop a low density DNA microarray for the analysis of 107 genes associated with B-cell non-Hodgkin lymphoma and to evaluate its performance in the diagnosis of SBCL. A predictive tool based on Fisher discriminant analysis using a training set of 40 patients including four different subtypes (follicular lymphoma n = 15, mantle cell lymphoma n = 7, B-cell chronic lymphocytic leukemia n = 6 and splenic marginal zone lymphoma n = 12) was designed. A short additional preliminary analysis to gauge the accuracy of this signature was then performed on an external set of nine patients. Using this model, eight of nine of those samples were classified successfully. This pilot study demonstrates that such a microarray tool may be a promising diagnostic approach for small B-cell non-Hodgkin lymphoma.

Identifying novel glioma associated pathways based on systems biology level meta-analysis.

PubMed

Hu, Yangfan; Li, Jinquan; Yan, Wenying; Chen, Jiajia; Li, Yin; Hu, Guang; Shen, Bairong

2013-01-01

With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

Best practices for hybridization design in two-colour microarray analysis.

PubMed

Knapen, Dries; Vergauwen, Lucia; Laukens, Kris; Blust, Ronny

2009-07-01

Two-colour microarrays are a popular platform of choice in gene expression studies. Because two different samples are hybridized on a single microarray, and several microarrays are usually needed in a given experiment, there are many possible ways to combine samples on different microarrays. The actual combination employed is commonly referred to as the 'hybridization design'. Different types of hybridization designs have been developed, all aimed at optimizing the experimental setup for the detection of differentially expressed genes while coping with technical noise. Here, we first provide an overview of the different classes of hybridization designs, discussing their advantages and limitations, and then we illustrate the current trends in the use of different hybridization design types in contemporary research.

Development of DNA Microarrays for Metabolic Pathway and Bioprocess Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gregory Stephanopoulos

Transcriptional profiling experiments utilizing DNA microarrays to study the intracellular accumulation of PHB in Synechocystis has proved difficult in large part because strains that show significant differences in PHB which would justify global analysis of gene expression have not been isolated.

DigOut: viewing differential expression genes as outliers.

PubMed

Yu, Hui; Tu, Kang; Xie, Lu; Li, Yuan-Yuan

2010-12-01

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

Microarray platform for omics analysis

NASA Astrophysics Data System (ADS)

Mecklenburg, Michael; Xie, Bin

2001-09-01

Microarray technology has revolutionized genetic analysis. However, limitations in genome analysis has lead to renewed interest in establishing 'omic' strategies. As we enter the post-genomic era, new microarray technologies are needed to address these new classes of 'omic' targets, such as proteins, as well as lipids and carbohydrates. We have developed a microarray platform that combines self- assembling monolayers with the biotin-streptavidin system to provide a robust, versatile immobilization scheme. A hydrophobic film is patterned on the surface creating an array of tension wells that eliminates evaporation effects thereby reducing the shear stress to which biomolecules are exposed to during immobilization. The streptavidin linker layer makes it possible to adapt and/or develop microarray based assays using virtually any class of biomolecules including: carbohydrates, peptides, antibodies, receptors, as well as them ore traditional DNA based arrays. Our microarray technology is designed to furnish seamless compatibility across the various 'omic' platforms by providing a common blueprint for fabricating and analyzing arrays. The prototype microarray uses a microscope slide footprint patterned with 2 by 96 flat wells. Data on the microarray platform will be presented.

Robust gene selection methods using weighting schemes for microarray data analysis.

PubMed

Kang, Suyeon; Song, Jongwoo

2017-09-02

A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

Microarrays in brain research: the good, the bad and the ugly.

PubMed

Mirnics, K

2001-06-01

Making sense of microarray data is a complex process, in which the interpretation of findings will depend on the overall experimental design and judgement of the investigator performing the analysis. As a result, differences in tissue harvesting, microarray types, sample labelling and data analysis procedures make post hoc sharing of microarray data a great challenge. To ensure rapid and meaningful data exchange, we need to create some order out of the existing chaos. In these ground-breaking microarray standardization and data sharing efforts, NIH agencies should take a leading role

Gene expression profiling of whole blood: Comparison of target preparation methods for accurate and reproducible microarray analysis

PubMed Central

Vartanian, Kristina; Slottke, Rachel; Johnstone, Timothy; Casale, Amanda; Planck, Stephen R; Choi, Dongseok; Smith, Justine R; Rosenbaum, James T; Harrington, Christina A

2009-01-01

Background Peripheral blood is an accessible and informative source of transcriptomal information for many human disease and pharmacogenomic studies. While there can be significant advantages to analyzing RNA isolated from whole blood, particularly in clinical studies, the preparation of samples for microarray analysis is complicated by the need to minimize artifacts associated with highly abundant globin RNA transcripts. The impact of globin RNA transcripts on expression profiling data can potentially be reduced by using RNA preparation and labeling methods that remove or block globin RNA during the microarray assay. We compared four different methods for preparing microarray hybridization targets from human whole blood collected in PAXGene tubes. Three of the methods utilized the Affymetrix one-cycle cDNA synthesis/in vitro transcription protocol but varied treatment of input RNA as follows: i. no treatment; ii. treatment with GLOBINclear; or iii. treatment with globin PNA oligos. In the fourth method cDNA targets were prepared with the Ovation amplification and labeling system. Results We find that microarray targets generated with labeling methods that reduce globin mRNA levels or minimize the impact of globin transcripts during hybridization detect more transcripts in the microarray assay compared with the standard Affymetrix method. Comparison of microarray results with quantitative PCR analysis of a panel of genes from the NF-kappa B pathway shows good correlation of transcript measurements produced with all four target preparation methods, although method-specific differences in overall correlation were observed. The impact of freezing blood collected in PAXGene tubes on data reproducibility was also examined. Expression profiles show little or no difference when RNA is extracted from either fresh or frozen blood samples. Conclusion RNA preparation and labeling methods designed to reduce the impact of globin mRNA transcripts can significantly improve the sensitivity of the DNA microarray expression profiling assay for whole blood samples. While blockage of globin transcripts during first strand cDNA synthesis with globin PNAs resulted in the best overall performance in this study, we conclude that selection of a protocol for expression profiling studies in blood should depend on several factors, including implementation requirements of the method and study design. RNA isolated from either freshly collected or frozen blood samples stored in PAXGene tubes can be used without altering gene expression profiles. PMID:19123946

Gene expression profile of blood cells for the prediction of delayed cerebral ischemia after intracranial aneurysm rupture: a pilot study in humans.

PubMed

Baumann, Antoine; Devaux, Yvan; Audibert, Gérard; Zhang, Lu; Bracard, Serge; Colnat-Coulbois, Sophie; Klein, Olivier; Zannad, Faiez; Charpentier, Claire; Longrois, Dan; Mertes, Paul-Michel

2013-01-01

Delayed cerebral ischemia (DCI) is a potentially devastating complication after intracranial aneurysm rupture and its mechanisms remain poorly elucidated. Early identification of the patients prone to developing DCI after rupture may represent a major breakthrough in its prevention and treatment. The single gene approach of DCI has demonstrated interest in humans. We hypothesized that whole genome expression profile of blood cells may be useful for better comprehension and prediction of aneurysmal DCI. Over a 35-month period, 218 patients with aneurysm rupture were included in this study. DCI was defined as the occurrence of a new delayed neurological deficit occurring within 2 weeks after aneurysm rupture with evidence of ischemia either on perfusion-diffusion MRI, CT angiography or CT perfusion imaging, or with cerebral angiography. DCI patients were matched against controls based on 4 out of 5 criteria (age, sex, Fisher grade, aneurysm location and smoking status). Genome-wide expression analysis of blood cells obtained at admission was performed by microarrays. Transcriptomic analysis was performed using long oligonucleotide microarrays representing 25,000 genes. Quantitative PCR: 1 µg of total RNA extracted was reverse-transcribed, and the resulting cDNA was diluted 10-fold before performing quantitative PCR. Microarray data were first analyzed by 'Significance Analysis of Microarrays' software which includes the Benjamini correction for multiple testing. In a second step, microarray data fold change was compared using a two-tailed, paired t test. Analysis of receiver-operating characteristic (ROC) curves and the area under the ROC curves were used for prediction analysis. Logistic regression models were used to investigate the additive value of multiple biomarkers. A total of 16 patients demonstrated DCI. Significance Analysis of Microarrays software failed to retrieve significant genes, most probably because of the heterogeneity of the patients included in the microarray experiments and the small size of the DCI population sample. Standard two-tailed paired t test and C-statistic revealed significant associations between gene expression and the occurrence of DCI: in particular, the expression of neuroregulin 1 was 1.6-fold upregulated in patients with DCI (p = 0.01) and predicted DCI with an area under the ROC curve of 0.96. Logistic regression analyses revealed a significant association between neuroregulin 1 and DCI (odds ratio 1.46, 95% confidence interval 1.02-2.09, p = 0.02). This pilot study suggests that blood cells may be a reservoir of prognostic biomarkers of DCI in patients with intracranial aneurysm rupture. Despite an evident lack of power, this study elicited neuroregulin 1, a vasoreactivity-, inflammation- and angiogenesis-related gene, as a possible candidate predictor of DCI. Larger cohort studies are needed but genome-wide microarray-based studies are promising research tools for the understanding of DCI after intracranial aneurysm rupture. © 2013 S. Karger AG, Basel.

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

PubMed

Trivedi, Prinal; Edwards, Jode W; Wang, Jelai; Gadbury, Gary L; Srinivasasainagendra, Vinodh; Zakharkin, Stanislav O; Kim, Kyoungmi; Mehta, Tapan; Brand, Jacob P L; Patki, Amit; Page, Grier P; Allison, David B

2005-04-06

Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

PubMed Central

2016-01-01

Abstract Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications. PMID:26504096

Transcriptome analysis of salinity stress responses in common wheat using a 22k oligo-DNA microarray.

PubMed

Kawaura, Kanako; Mochida, Keiichi; Yamazaki, Yukiko; Ogihara, Yasunari

2006-04-01

In this study, we constructed a 22k wheat oligo-DNA microarray. A total of 148,676 expressed sequence tags of common wheat were collected from the database of the Wheat Genomics Consortium of Japan. These were grouped into 34,064 contigs, which were then used to design an oligonucleotide DNA microarray. Following a multistep selection of the sense strand, 21,939 60-mer oligo-DNA probes were selected for attachment on the microarray slide. This 22k oligo-DNA microarray was used to examine the transcriptional response of wheat to salt stress. More than 95% of the probes gave reproducible hybridization signals when targeted with RNAs extracted from salt-treated wheat shoots and roots. With the microarray, we identified 1,811 genes whose expressions changed more than 2-fold in response to salt. These included genes known to mediate response to salt, as well as unknown genes, and they were classified into 12 major groups by hierarchical clustering. These gene expression patterns were also confirmed by real-time reverse transcription-PCR. Many of the genes with unknown function were clustered together with genes known to be involved in response to salt stress. Thus, analysis of gene expression patterns combined with gene ontology should help identify the function of the unknown genes. Also, functional analysis of these wheat genes should provide new insight into the response to salt stress. Finally, these results indicate that the 22k oligo-DNA microarray is a reliable method for monitoring global gene expression patterns in wheat.

Single-Nucleotide Polymorphism-Microarray Ploidy Analysis of Paraffin-Embedded Products of Conception in Recurrent Pregnancy Loss Evaluations.

PubMed

Maslow, Bat-Sheva L; Budinetz, Tara; Sueldo, Carolina; Anspach, Erica; Engmann, Lawrence; Benadiva, Claudio; Nulsen, John C

2015-07-01

To compare the analysis of chromosome number from paraffin-embedded products of conception using single-nucleotide polymorphism (SNP) microarray with the recommended screening for the evaluation of couples presenting with recurrent pregnancy loss who do not have previous fetal cytogenetic data. We performed a retrospective cohort study including all women who presented for a new evaluation of recurrent pregnancy loss over a 2-year period (January 1, 2012, to December 31, 2013). All participants had at least two documented first-trimester losses and both the recommended screening tests and SNP microarray performed on at least one paraffin-embedded products of conception sample. Single-nucleotide polymorphism microarray identifies all 24 chromosomes (22 autosomes, X, and Y). Forty-two women with a total of 178 losses were included in the study. Paraffin-embedded products of conception from 62 losses were sent for SNP microarray. Single-nucleotide polymorphism microarray successfully diagnosed fetal chromosome number in 71% (44/62) of samples, of which 43% (19/44) were euploid and 57% (25/44) were noneuploid. Seven of 42 (17%) participants had abnormalities on recurrent pregnancy loss screening. The per-person detection rate for a cause of pregnancy loss was significantly higher in the SNP microarray (0.50; 95% confidence interval [CI] 0.36-0.64) compared with recurrent pregnancy loss evaluation (0.17; 95% CI 0.08-0.31) (P=.002). Participants with one or more euploid loss identified on paraffin-embedded products of conception were significantly more likely to have an abnormality on recurrent pregnancy loss screening than those with only noneuploid results (P=.028). The significance remained when controlling for age, number of losses, number of samples, and total pregnancies. These results suggest that SNP microarray testing of paraffin-embedded products of conception is a valuable tool for the evaluation of recurrent pregnancy loss in patients without prior fetal cytogenetic results. Recommended recurrent pregnancy loss screening was unnecessary in almost half the patients in our study. II.

WebArray: an online platform for microarray data analysis

PubMed Central

Xia, Xiaoqin; McClelland, Michael; Wang, Yipeng

2005-01-01

Background Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments. Results The currently implemented functions were based on limma and affy package from Bioconductor, the spacings LOESS histogram (SPLOSH) method, PCA-assisted normalization method and genome mapping method. WebArray incorporates these packages and provides a user-friendly interface for accessing a wide range of key functions of limma and others, such as spot quality weight, background correction, graphical plotting, normalization, linear modeling, empirical bayes statistical analysis, false discovery rate (FDR) estimation, chromosomal mapping for genome comparison. Conclusion WebArray offers a convenient platform for bench biologists to access several cutting-edge microarray data analysis tools. The website is freely available at . It runs on a Linux server with Apache and MySQL. PMID:16371165

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

PubMed Central

Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates. PMID:26964035

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

PubMed Central

2013-01-01

Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N > 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies. PMID:23822712

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.

PubMed

Richard, Arianne C; Lyons, Paul A; Peters, James E; Biasci, Daniele; Flint, Shaun M; Lee, James C; McKinney, Eoin F; Siegel, Richard M; Smith, Kenneth G C

2014-08-04

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

PubMed Central

Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia

2012-01-01

Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications. PMID:23071521

Dual-color Proteomic Profiling of Complex Samples with a Microarray of 810 Cancer-related Antibodies*

PubMed Central

Schröder, Christoph; Jacob, Anette; Tonack, Sarah; Radon, Tomasz P.; Sill, Martin; Zucknick, Manuela; Rüffer, Sven; Costello, Eithne; Neoptolemos, John P.; Crnogorac-Jurcevic, Tatjana; Bauer, Andrea; Fellenberg, Kurt; Hoheisel, Jörg D.

2010-01-01

Antibody microarrays have the potential to enable comprehensive proteomic analysis of small amounts of sample material. Here, protocols are presented for the production, quality assessment, and reproducible application of antibody microarrays in a two-color mode with an array of 1,800 features, representing 810 antibodies that were directed at 741 cancer-related proteins. In addition to measures of array quality, we implemented indicators for the accuracy and significance of dual-color detection. Dual-color measurements outperform a single-color approach concerning assay reproducibility and discriminative power. In the analysis of serum samples, depletion of high-abundance proteins did not improve technical assay quality. On the contrary, depletion introduced a strong bias in protein representation. In an initial study, we demonstrated the applicability of the protocols to proteins derived from urine samples. We identified differences between urine samples from pancreatic cancer patients and healthy subjects and between sexes. This study demonstrates that biomedically relevant data can be produced. As demonstrated by the thorough quality analysis, the dual-color antibody array approach proved to be competitive with other proteomic techniques and comparable in performance to transcriptional microarray analyses. PMID:20164060

The pathogenesis shared between abdominal aortic aneurysms and intracranial aneurysms: a microarray analysis.

PubMed

Wang, Wen; Li, Hao; Zhao, Zheng; Wang, Haoyuan; Zhang, Dong; Zhang, Yan; Lan, Qing; Wang, Jiangfei; Cao, Yong; Zhao, Jizong

2018-04-01

Abdominal aortic aneurysms (AAAs) and intracranial saccular aneurysms (IAs) are the most common types of aneurysms. This study was to investigate the common pathogenesis shared between these two kinds of aneurysms. We collected 12 IAs samples and 12 control arteries from the Beijing Tiantan Hospital and performed microarray analysis. In addition, we utilized the microarray datasets of IAs and AAAs from the Gene Expression Omnibus (GEO), in combination with our microarray results, to generate messenger RNA expression profiles for both AAAs and IAs in our study. Functional exploration and protein-protein interaction (PPI) analysis were performed. A total of 727 common genes were differentially expressed (404 was upregulated; 323 was downregulated) for both AAAs and IAs. The GO and pathway analyses showed that the common dysregulated genes were mainly enriched in vascular smooth muscle contraction, muscle contraction, immune response, defense response, cell activation, IL-6 signaling and chemokine signaling pathways, etc. The further protein-protein analysis identified 35 hub nodes, including TNF, IL6, MAPK13, and CCL5. These hub node genes were enriched in inflammatory response, positive regulation of IL-6 production, chemokine signaling pathway, and T/B cell receptor signaling pathway. Our study will gain new insight into the molecular mechanisms for the pathogenesis of both types of aneurysms and provide new therapeutic targets for the patients harboring AAAs and IAs.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

NASA Astrophysics Data System (ADS)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Integrated analysis of chromosome copy number variation and gene expression in cervical carcinoma

PubMed Central

Yan, Deng; Yi, Song; Chiu, Wang Chi; Qin, Liu Gui; Kin, Wong Hoi; Kwok Hung, Chung Tony; Linxiao, Han; Wai, Choy Kwong; Yi, Sui; Tao, Yang; Tao, Tang

2017-01-01

Objective This study was conducted to explore chromosomal copy number variations (CNV) and transcript expression and to examine pathways in cervical pathogenesis using genome-wide high resolution microarrays. Methods Genome-wide chromosomal CNVs were investigated in 6 cervical cancer cell lines by Human Genome CGH Microarray Kit (4x44K). Gene expression profiles in cervical cancer cell lines, primary cervical carcinoma and normal cervical epithelium tissues were also studied using the Whole Human Genome Microarray Kit (4x44K). Results Fifty common chromosomal CNVs were identified in the cervical cancer cell lines. Correlation analysis revealed that gene up-regulation or down-regulation is significantly correlated with genomic amplification (P=0.009) or deletion (P=0.006) events. Expression profiles were identified through cluster analysis. Gene annotation analysis pinpointed cell cycle pathways was significantly (P=1.15E-08) affected in cervical cancer. Common CNVs were associated with cervical cancer. Conclusion Chromosomal CNVs may contribute to their transcript expression in cervical cancer. PMID:29312578

Integrated analysis of chromosome copy number variation and gene expression in cervical carcinoma.

PubMed

Yan, Deng; Yi, Song; Chiu, Wang Chi; Qin, Liu Gui; Kin, Wong Hoi; Kwok Hung, Chung Tony; Linxiao, Han; Wai, Choy Kwong; Yi, Sui; Tao, Yang; Tao, Tang

2017-12-12

This study was conducted to explore chromosomal copy number variations (CNV) and transcript expression and to examine pathways in cervical pathogenesis using genome-wide high resolution microarrays. Genome-wide chromosomal CNVs were investigated in 6 cervical cancer cell lines by Human Genome CGH Microarray Kit (4x44K). Gene expression profiles in cervical cancer cell lines, primary cervical carcinoma and normal cervical epithelium tissues were also studied using the Whole Human Genome Microarray Kit (4x44K). Fifty common chromosomal CNVs were identified in the cervical cancer cell lines. Correlation analysis revealed that gene up-regulation or down-regulation is significantly correlated with genomic amplification ( P =0.009) or deletion ( P =0.006) events. Expression profiles were identified through cluster analysis. Gene annotation analysis pinpointed cell cycle pathways was significantly ( P =1.15E-08) affected in cervical cancer. Common CNVs were associated with cervical cancer. Chromosomal CNVs may contribute to their transcript expression in cervical cancer.

Mutation analysis of 272 Spanish families affected by autosomal recessive retinitis pigmentosa using a genotyping microarray.

PubMed

Ávila-Fernández, Almudena; Cantalapiedra, Diego; Aller, Elena; Vallespín, Elena; Aguirre-Lambán, Jana; Blanco-Kelly, Fiona; Corton, M; Riveiro-Álvarez, Rosa; Allikmets, Rando; Trujillo-Tiebas, María José; Millán, José M; Cremers, Frans P M; Ayuso, Carmen

2010-12-03

Retinitis pigmentosa (RP) is a genetically heterogeneous disorder characterized by progressive loss of vision. The aim of this study was to identify the causative mutations in 272 Spanish families using a genotyping microarray. 272 unrelated Spanish families, 107 with autosomal recessive RP (arRP) and 165 with sporadic RP (sRP), were studied using the APEX genotyping microarray. The families were also classified by clinical criteria: 86 juveniles and 186 typical RP families. Haplotype and sequence analysis were performed to identify the second mutated allele. At least one-gene variant was found in 14% and 16% of the juvenile and typical RP groups respectively. Further study identified four new mutations, providing both causative changes in 11% of the families. Retinol Dehydrogenase 12 (RDH12) was the most frequently mutated gene in the juvenile RP group, and Usher Syndrome 2A (USH2A) and Ceramide Kinase-Like (CERKL) were the most frequently mutated genes in the typical RP group. The only variant found in CERKL was p.Arg257Stop, the most frequent mutation. The genotyping microarray combined with segregation and sequence analysis allowed us to identify the causative mutations in 11% of the families. Due to the low number of characterized families, this approach should be used in tandem with other techniques.

Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping

NASA Technical Reports Server (NTRS)

Royce, Thomas E.; Rozowsky, Joel S.; Bertone, Paul; Samanta, Manoj; Stolc, Viktor; Weissman, Sherman; Snyder, Michael; Gerstein, Mark

2005-01-01

Traditional microarrays use probes complementary to known genes to quantitate the differential gene expression between two or more conditions. Genomic tiling microarray experiments differ in that probes that span a genomic region at regular intervals are used to detect the presence or absence of transcription. This difference means the same sets of biases and the methods for addressing them are unlikely to be relevant to both types of experiment. We introduce the informatics challenges arising in the analysis of tiling microarray experiments as open problems to the scientific community and present initial approaches for the analysis of this nascent technology.

Fully Automated Complementary DNA Microarray Segmentation using a Novel Fuzzy-based Algorithm.

PubMed

Saberkari, Hamidreza; Bahrami, Sheyda; Shamsi, Mousa; Amoshahy, Mohammad Javad; Ghavifekr, Habib Badri; Sedaaghi, Mohammad Hossein

2015-01-01

DNA microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. The average value of the fluorescent intensity could be calculated in a microarray experiment. The calculated intensity values are very close in amount to the levels of expression of a particular gene. However, determining the appropriate position of every spot in microarray images is a main challenge, which leads to the accurate classification of normal and abnormal (cancer) cells. In this paper, first a preprocessing approach is performed to eliminate the noise and artifacts available in microarray cells using the nonlinear anisotropic diffusion filtering method. Then, the coordinate center of each spot is positioned utilizing the mathematical morphology operations. Finally, the position of each spot is exactly determined through applying a novel hybrid model based on the principle component analysis and the spatial fuzzy c-means clustering (SFCM) algorithm. Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation. The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases. Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively.

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

PubMed Central

2012-01-01

Background The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. Results We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. Conclusion T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially. PMID:22276688

Microarray characterization of gene expression changes in blood during acute ethanol exposure

PubMed Central

2013-01-01

Background As part of the civil aviation safety program to define the adverse effects of ethanol on flying performance, we performed a DNA microarray analysis of human whole blood samples from a five-time point study of subjects administered ethanol orally, followed by breathalyzer analysis, to monitor blood alcohol concentration (BAC) to discover significant gene expression changes in response to the ethanol exposure. Methods Subjects were administered either orange juice or orange juice with ethanol. Blood samples were taken based on BAC and total RNA was isolated from PaxGene™ blood tubes. The amplified cDNA was used in microarray and quantitative real-time polymerase chain reaction (RT-qPCR) analyses to evaluate differential gene expression. Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods. Candidate genes showing distinctive expression patterns in response to ethanol were clustered by pattern and further analyzed for related function, pathway membership and common transcription factor binding within and across clusters. RT-qPCR was used with representative genes to confirm relative transcript levels across time to those detected in microarrays. Results Microarray analysis of samples representing 0%, 0.04%, 0.08%, return to 0.04%, and 0.02% wt/vol BAC showed that changes in gene expression could be detected across the time course. The expression changes were verified by qRT-PCR. The candidate genes of interest (GOI) identified from the microarray analysis and clustered by expression pattern across the five BAC points showed seven coordinately expressed groups. Analysis showed function-based networks, shared transcription factor binding sites and signaling pathways for members of the clusters. These include hematological functions, innate immunity and inflammation functions, metabolic functions expected of ethanol metabolism, and pancreatic and hepatic function. Five of the seven clusters showed links to the p38 MAPK pathway. Conclusions The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood. The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance. PMID:23883607

Differential gene expression detection and sample classification using penalized linear regression models.

PubMed

Wu, Baolin

2006-02-15

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

PubMed

Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays

PubMed Central

Hu, Guohong; Wang, Hui-Yun; Greenawalt, Danielle M.; Azaro, Marco A.; Luo, Minjie; Tereshchenko, Irina V.; Cui, Xiangfeng; Yang, Qifeng; Gao, Richeng; Shen, Li; Li, Honghua

2006-01-01

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of ∼160 000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300 000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from . PMID:16982644

The use of open source bioinformatics tools to dissect transcriptomic data.

PubMed

Nitsche, Benjamin M; Ram, Arthur F J; Meyer, Vera

2012-01-01

Microarrays are a valuable technology to study fungal physiology on a transcriptomic level. Various microarray platforms are available comprising both single and two channel arrays. Despite different technologies, preprocessing of microarray data generally includes quality control, background correction, normalization, and summarization of probe level data. Subsequently, depending on the experimental design, diverse statistical analysis can be performed, including the identification of differentially expressed genes and the construction of gene coexpression networks.We describe how Bioconductor, a collection of open source and open development packages for the statistical programming language R, can be used for dissecting microarray data. We provide fundamental details that facilitate the process of getting started with R and Bioconductor. Using two publicly available microarray datasets from Aspergillus niger, we give detailed protocols on how to identify differentially expressed genes and how to construct gene coexpression networks.

A Java-based tool for the design of classification microarrays.

PubMed

Meng, Da; Broschat, Shira L; Call, Douglas R

2008-08-04

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

Enhancing interdisciplinary mathematics and biology education: a microarray data analysis course bridging these disciplines.

PubMed

Tra, Yolande V; Evans, Irene M

2010-01-01

BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course.

Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

PubMed Central

Evans, Irene M.

2010-01-01

BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course. PMID:20810954

Chromosomal Microarray versus Karyotyping for Prenatal Diagnosis

PubMed Central

Wapner, Ronald J.; Martin, Christa Lese; Levy, Brynn; Ballif, Blake C.; Eng, Christine M.; Zachary, Julia M.; Savage, Melissa; Platt, Lawrence D.; Saltzman, Daniel; Grobman, William A.; Klugman, Susan; Scholl, Thomas; Simpson, Joe Leigh; McCall, Kimberly; Aggarwal, Vimla S.; Bunke, Brian; Nahum, Odelia; Patel, Ankita; Lamb, Allen N.; Thom, Elizabeth A.; Beaudet, Arthur L.; Ledbetter, David H.; Shaffer, Lisa G.; Jackson, Laird

2013-01-01

Background Chromosomal microarray analysis has emerged as a primary diagnostic tool for the evaluation of developmental delay and structural malformations in children. We aimed to evaluate the accuracy, efficacy, and incremental yield of chromosomal microarray analysis as compared with karyotyping for routine prenatal diagnosis. Methods Samples from women undergoing prenatal diagnosis at 29 centers were sent to a central karyotyping laboratory. Each sample was split in two; standard karyotyping was performed on one portion and the other was sent to one of four laboratories for chromosomal microarray. Results We enrolled a total of 4406 women. Indications for prenatal diagnosis were advanced maternal age (46.6%), abnormal result on Down’s syndrome screening (18.8%), structural anomalies on ultrasonography (25.2%), and other indications (9.4%). In 4340 (98.8%) of the fetal samples, microarray analysis was successful; 87.9% of samples could be used without tissue culture. Microarray analysis of the 4282 nonmosaic samples identified all the aneuploidies and unbalanced rearrangements identified on karyotyping but did not identify balanced translocations and fetal triploidy. In samples with a normal karyotype, microarray analysis revealed clinically relevant deletions or duplications in 6.0% with a structural anomaly and in 1.7% of those whose indications were advanced maternal age or positive screening results. Conclusions In the context of prenatal diagnostic testing, chromosomal microarray analysis identified additional, clinically significant cytogenetic information as compared with karyotyping and was equally efficacious in identifying aneuploidies and unbalanced rearrangements but did not identify balanced translocations and triploidies. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and others; ClinicalTrials.gov number, NCT01279733.) PMID:23215555

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

PubMed

Usadel, Björn; Nagel, Axel; Steinhauser, Dirk; Gibon, Yves; Bläsing, Oliver E; Redestig, Henning; Sreenivasulu, Nese; Krall, Leonard; Hannah, Matthew A; Poree, Fabien; Fernie, Alisdair R; Stitt, Mark

2006-12-18

Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier.

PubMed

Kumar, Mukesh; Rath, Nitish Kumar; Rath, Santanu Kumar

2016-04-01

Microarray-based gene expression profiling has emerged as an efficient technique for classification, prognosis, diagnosis, and treatment of cancer. Frequent changes in the behavior of this disease generates an enormous volume of data. Microarray data satisfies both the veracity and velocity properties of big data, as it keeps changing with time. Therefore, the analysis of microarray datasets in a small amount of time is essential. They often contain a large amount of expression, but only a fraction of it comprises genes that are significantly expressed. The precise identification of genes of interest that are responsible for causing cancer are imperative in microarray data analysis. Most existing schemes employ a two-phase process such as feature selection/extraction followed by classification. In this paper, various statistical methods (tests) based on MapReduce are proposed for selecting relevant features. After feature selection, a MapReduce-based K-nearest neighbor (mrKNN) classifier is also employed to classify microarray data. These algorithms are successfully implemented in a Hadoop framework. A comparative analysis is done on these MapReduce-based models using microarray datasets of various dimensions. From the obtained results, it is observed that these models consume much less execution time than conventional models in processing big data. Copyright © 2016 Elsevier Inc. All rights reserved.

[Diagnosis of a case with Williams-Beuren syndrome with nephrocalcinosis using chromosome microarray analysis].

PubMed

Jin, S J; Liu, M; Long, W J; Luo, X P

2016-12-02

Objective: To explore the clinical phenotypes and the genetic cause for a boy with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders. Method: Routine G-banding and chromosome microarray analysis were applied to a child with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders treated in the Department of Pediatrics of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology in September 2015 and his parents to conduct the chromosomal karyotype analysis and the whole genome scanning. Deleted genes were searched in the Decipher and NCBI databases, and their relationships with the clinical phenotypes were analyzed. Result: A six-month-old boy was refered to us because of unexplained growth retardation and feeding intolerance.The affected child presented with abnormal manifestation such as special face, umbilical hernia, growth retardation, hypothyroidism, congenital heart disease, right ear sensorineural deafness, hypercalcemia and nephrocalcinosis. The child's karyotype was 46, XY, 16qh + , and his parents' karyotypes were normal. Chromosome microarray analysis revealed a 1 436 kb deletion on the 7q11.23(72701098_74136633) region of the child. This region included 23 protein-coding genes, which were reported to be corresponding to Williams-Beuren syndrome and its certain clinical phenotypes. His parents' results of chromosome microarray analysis were normal. Conclusion: A boy with characteristic manifestation of Williams-Beuren syndrome and rare nephrocalcinosis was diagnosed using chromosome microarray analysis. The deletion on the 7q11.23 might be related to the clinical phenotypes of Williams-Beuren syndrome, yet further studies are needed.

Archetypal analysis of diverse Pseudomonas aeruginosa transcriptomes reveals adaptation in cystic fibrosis airways

PubMed Central

2013-01-01

Background Analysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the underlying biological features present in the data. The aim of this study is to introduce a method for DNA microarray analysis that provides an intuitive interpretation of data through dimension reduction and pattern recognition. We present the first “Archetypal Analysis” of global gene expression. The analysis is based on microarray data from five integrated studies of Pseudomonas aeruginosa isolated from the airways of cystic fibrosis patients. Results Our analysis clustered samples into distinct groups with comprehensible characteristics since the archetypes representing the individual groups are closely related to samples present in the data set. Significant changes in gene expression between different groups identified adaptive changes of the bacteria residing in the cystic fibrosis lung. The analysis suggests a similar gene expression pattern between isolates with a high mutation rate (hypermutators) despite accumulation of different mutations for these isolates. This suggests positive selection in the cystic fibrosis lung environment, and changes in gene expression for these isolates are therefore most likely related to adaptation of the bacteria. Conclusions Archetypal analysis succeeded in identifying adaptive changes of P. aeruginosa. The combination of clustering and matrix factorization made it possible to reveal minor similarities among different groups of data, which other analytical methods failed to identify. We suggest that this analysis could be used to supplement current methods used to analyze DNA microarray data. PMID:24059747

Recurrent pregnancy loss evaluation combined with 24-chromosome microarray of miscarriage tissue provides a probable or definite cause of pregnancy loss in over 90% of patients.

PubMed

Popescu, F; Jaslow, C R; Kutteh, W H

2018-04-01

Will the addition of 24-chromosome microarray analysis on miscarriage tissue combined with the standard American Society for Reproductive Medicine (ASRM) evaluation for recurrent miscarriage explain most losses? Over 90% of patients with recurrent pregnancy loss (RPL) will have a probable or definitive cause identified when combining genetic testing on miscarriage tissue with the standard ASRM evaluation for recurrent miscarriage. RPL is estimated to occur in 2-4% of reproductive age couples. A probable cause can be identified in approximately 50% of patients after an ASRM recommended workup including an evaluation for parental chromosomal abnormalities, congenital and acquired uterine anomalies, endocrine imbalances and autoimmune factors including antiphospholipid syndrome. Single-center, prospective cohort study that included 100 patients seen in a private RPL clinic from 2014 to 2017. All 100 women had two or more pregnancy losses, a complete evaluation for RPL as defined by the ASRM, and miscarriage tissue evaluated by 24-chromosome microarray analysis after their second or subsequent miscarriage. Frequencies of abnormal results for evidence-based diagnostic tests considered definite or probable causes of RPL (karyotyping for parental chromosomal abnormalities, and 24-chromosome microarray evaluation for products of conception (POC); pelvic sonohysterography, hysterosalpingogram, or hysteroscopy for uterine anomalies; immunological tests for lupus anticoagulant and anticardiolipin antibodies; and blood tests for thyroid stimulating hormone (TSH), prolactin and hemoglobin A1c) were evaluated. We excluded cases where there was maternal cell contamination of the miscarriage tissue or if the ASRM evaluation was incomplete. A cost analysis for the evaluation of RPL was conducted to determine whether a proposed procedure of 24-chromome microarray evaluation followed by an ASRM RPL workup (for those RPL patients who had a normal 24-chromosome microarray evaluation) was more cost-efficient than conducting ASRM RPL workups on RPL patients followed by 24-chromosome microarray analysis (for those RPL patients who had a normal RPL workup). A definite or probable cause of pregnancy loss was identified in the vast majority (95/100; 95%) of RPL patients when a 24-chromosome pair microarray evaluation of POC testing is combined with the standard ASRM RPL workup evaluation at the time of the second or subsequent loss. The ASRM RPL workup identified an abnormality and a probable explanation for pregnancy loss in only 45/100 or 45% of all patients. A definite abnormality was identified in 67/100 patients or 67% when initial testing was performed using 24-chromosome microarray analyses on the miscarriage tissue. Only 5/100 (5%) patients, who had a euploid loss and a normal ASRM RPL workup, had a pregnancy loss without a probable or definitive cause identified. All other losses were explained by an abnormal 24-chromosome microarray analysis of the miscarriage tissue, an abnormal finding of the RPL workup, or a combination of both. Results from the cost analysis indicated that an initial approach of using a 24-chromosome microarray analysis on miscarriage tissue resulted in a 50% savings in cost to the health care system and to the patient. This is a single-center study on a small group of well-characterized women with RPL. There was an incomplete follow-up on subsequent pregnancy outcomes after evaluation, however this should not affect our principal results. The maternal age of patients varied from 26 to 45 years old. More aneuploid pregnancy losses would be expected in older women, particularly over the age of 35 years old. Evaluation of POC using 24-chromosome microarray analysis adds significantly to the ASRM recommended evaluation of RPL. Genetic evaluation on miscarriage tissue obtained at the time of the second and subsequent pregnancy losses should be offered to all couples with two or more consecutive pregnancy losses. The combination of a genetic evaluation on miscarriage tissue with an evidence-based evaluation for RPL will identify a probable or definitive cause in over 90% of miscarriages. No funding was received for this study and there are no conflicts of interest to declare. Not applicable.

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

PubMed Central

2009-01-01

Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical. Conclusion Feature variability can have a strong impact on breast cancer signature composition, as well as the classification of individual patient samples. We therefore strongly recommend that feature variability is considered in analyzing data from microarray breast cancer expression profiling experiments. PMID:19941644

Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis.

PubMed

Liu, Mingyuan; Hou, Xiaojun; Zhang, Ping; Hao, Yong; Yang, Yiting; Wu, Xiongfeng; Zhu, Desheng; Guan, Yangtai

2013-05-01

Multiple sclerosis (MS) is the most prevalent demyelinating disease and the principal cause of neurological disability in young adults. Recent microarray gene expression profiling studies have identified several genetic variants contributing to the complex pathogenesis of MS, however, expressional and functional studies are still required to further understand its molecular mechanism. The present study aimed to analyze the molecular mechanism of MS using microarray analysis combined with bioinformatics techniques. We downloaded the gene expression profile of MS from Gene Expression Omnibus (GEO) and analysed the microarray data using the differentially coexpressed genes (DCGs) and links package in R and Database for Annotation, Visualization and Integrated Discovery. The regulatory impact factor (RIF) algorithm was used to measure the impact factor of transcription factor. A total of 1,297 DCGs between MS patients and healthy controls were identified. Functional annotation indicated that these DCGs were associated with immune and neurological functions. Furthermore, the RIF result suggested that IKZF1, BACH1, CEBPB, EGR1, FOS may play central regulatory roles in controlling gene expression in the pathogenesis of MS. Our findings confirm the presence of multiple molecular alterations in MS and indicate the possibility for identifying prognostic factors associated with MS pathogenesis.

Systematic Omics Analysis Review (SOAR) Tool to Support Risk Assessment

PubMed Central

McConnell, Emma R.; Bell, Shannon M.; Cote, Ila; Wang, Rong-Lin; Perkins, Edward J.; Garcia-Reyero, Natàlia; Gong, Ping; Burgoon, Lyle D.

2014-01-01

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment. PMID:25531884

DOE Office of Scientific and Technical Information (OSTI.GOV)

White, Amanda M.; Daly, Don S.; Willse, Alan R.

The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source microarray image analysis tool that allows the user to customize analysis of sets of microarray images. This tool provides several methods of identifying and quantify spot statistics, as well as extensive diagnostic statistics and images to identify poor data quality or processing. The open nature of this software allows researchers to understand the algorithms used to provide intensity estimates and to modify them easily if desired.

ELISA-BASE: An Integrated Bioinformatics Tool for Analyzing and Tracking ELISA Microarray Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

White, Amanda M.; Collett, James L.; Seurynck-Servoss, Shannon L.

ELISA-BASE is an open-source database for capturing, organizing and analyzing protein enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Soft-ware Environment (BASE) database system, which was developed for DNA microarrays. In order to make BASE suitable for protein microarray experiments, we developed several plugins for importing and analyzing quantitative ELISA microarray data. Most notably, our Protein Microarray Analysis Tool (ProMAT) for processing quantita-tive ELISA data is now available as a plugin to the database.

An evaluation of tyramide signal amplification and archived fixed and frozen tissue in microarray gene expression analysis

PubMed Central

Karsten, Stanislav L.; Van Deerlin, Vivianna M. D.; Sabatti, Chiara; Gill, Lisa H.; Geschwind, Daniel H.

2002-01-01

Archival formalin-fixed, paraffin-embedded and ethanol-fixed tissues represent a potentially invaluable resource for gene expression analysis, as they are the most widely available material for studies of human disease. Little data are available evaluating whether RNA obtained from fixed (archival) tissues could produce reliable and reproducible microarray expression data. Here we compare the use of RNA isolated from human archival tissues fixed in ethanol and formalin to frozen tissue in cDNA microarray experiments. Since an additional factor that can limit the utility of archival tissue is the often small quantities available, we also evaluate the use of the tyramide signal amplification method (TSA), which allows the use of small amounts of RNA. Detailed analysis indicates that TSA provides a consistent and reproducible signal amplification method for cDNA microarray analysis, across both arrays and the genes tested. Analysis of this method also highlights the importance of performing non-linear channel normalization and dye switching. Furthermore, archived, fixed specimens can perform well, but not surprisingly, produce more variable results than frozen tissues. Consistent results are more easily obtainable using ethanol-fixed tissues, whereas formalin-fixed tissue does not typically provide a useful substrate for cDNA synthesis and labeling. PMID:11788730

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium

PubMed Central

Howat, William J; Blows, Fiona M; Provenzano, Elena; Brook, Mark N; Morris, Lorna; Gazinska, Patrycja; Johnson, Nicola; McDuffus, Leigh‐Anne; Miller, Jodi; Sawyer, Elinor J; Pinder, Sarah; van Deurzen, Carolien H M; Jones, Louise; Sironen, Reijo; Visscher, Daniel; Caldas, Carlos; Daley, Frances; Coulson, Penny; Broeks, Annegien; Sanders, Joyce; Wesseling, Jelle; Nevanlinna, Heli; Fagerholm, Rainer; Blomqvist, Carl; Heikkilä, Päivi; Ali, H Raza; Dawson, Sarah‐Jane; Figueroa, Jonine; Lissowska, Jolanta; Brinton, Louise; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli‐Matti; Cox, Angela; Brock, Ian W; Cross, Simon S; Reed, Malcolm W; Couch, Fergus J; Olson, Janet E; Devillee, Peter; Mesker, Wilma E; Seyaneve, Caroline M; Hollestelle, Antoinette; Benitez, Javier; Perez, Jose Ignacio Arias; Menéndez, Primitiva; Bolla, Manjeet K; Easton, Douglas F; Schmidt, Marjanka K; Pharoah, Paul D; Sherman, Mark E

2014-01-01

Abstract Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large‐scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65–70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose‐response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96–98%), but yielded many false positives (positive predictive value = 30–32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large‐scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker‐specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results. PMID:27499890

Chipster: user-friendly analysis software for microarray and other high-throughput data.

PubMed

Kallio, M Aleksi; Tuimala, Jarno T; Hupponen, Taavi; Klemelä, Petri; Gentile, Massimiliano; Scheinin, Ilari; Koski, Mikko; Käki, Janne; Korpelainen, Eija I

2011-10-14

The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.

Chipster: user-friendly analysis software for microarray and other high-throughput data

PubMed Central

2011-01-01

Background The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Results Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Conclusions Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available. PMID:21999641

Development and experimental validation of a 20K Atlantic cod (Gadus morhua) oligonucleotide microarray based on a collection of over 150,000 ESTs.

PubMed

Booman, Marije; Borza, Tudor; Feng, Charles Y; Hori, Tiago S; Higgins, Brent; Culf, Adrian; Léger, Daniel; Chute, Ian C; Belkaid, Anissa; Rise, Marlies; Gamperl, A Kurt; Hubert, Sophie; Kimball, Jennifer; Ouellette, Rodney J; Johnson, Stewart C; Bowman, Sharen; Rise, Matthew L

2011-08-01

The collapse of Atlantic cod (Gadus morhua) wild populations strongly impacted the Atlantic cod fishery and led to the development of cod aquaculture. In order to improve aquaculture and broodstock quality, we need to gain knowledge of genes and pathways involved in Atlantic cod responses to pathogens and other stressors. The Atlantic Cod Genomics and Broodstock Development Project has generated over 150,000 expressed sequence tags from 42 cDNA libraries representing various tissues, developmental stages, and stimuli. We used this resource to develop an Atlantic cod oligonucleotide microarray containing 20,000 unique probes. Selection of sequences from the full range of cDNA libraries enables application of the microarray for a broad spectrum of Atlantic cod functional genomics studies. We included sequences that were highly abundant in suppression subtractive hybridization (SSH) libraries, which were enriched for transcripts responsive to pathogens or other stressors. These sequences represent genes that potentially play an important role in stress and/or immune responses, making the microarray particularly useful for studies of Atlantic cod gene expression responses to immune stimuli and other stressors. To demonstrate its value, we used the microarray to analyze the Atlantic cod spleen response to stimulation with formalin-killed, atypical Aeromonas salmonicida, resulting in a gene expression profile that indicates a strong innate immune response. These results were further validated by quantitative PCR analysis and comparison to results from previous analysis of an SSH library. This study shows that the Atlantic cod 20K oligonucleotide microarray is a valuable new tool for Atlantic cod functional genomics research.

Curation of microarray oligonucleotides and corresponding ESTs/cDNAs used for gene expression analysis in zebra finches.

PubMed

Lovell, Peter V; Huizinga, Nicole A; Getachew, Abel; Mees, Brianna; Friedrich, Samantha R; Wirthlin, Morgan; Mello, Claudio V

2018-05-18

Zebra finches are a major model organism for investigating mechanisms of vocal learning, a trait that enables spoken language in humans. The development of cDNA collections with expressed sequence tags (ESTs) and microarrays has allowed for extensive molecular characterizations of circuitry underlying vocal learning and production. However, poor database curation can lead to errors in transcriptome and bioinformatics analyses, limiting the impact of these resources. Here we used genomic alignments and synteny analysis for orthology verification to curate and reannotate ~ 35% of the oligonucleotides and corresponding ESTs/cDNAs that make-up Agilent microarrays for gene expression analysis in finches. We found that: (1) 5475 out of 43,084 oligos (a) failed to align to the zebra finch genome, (b) aligned to multiple loci, or (c) aligned to Chr_un only, and thus need to be flagged until a better genome assembly is available, or (d) reflect cloning artifacts; (2) Out of 9635 valid oligos examined further, 3120 were incorrectly named, including 1533 with no known orthologs; and (3) 2635 oligos required name update. The resulting curated dataset provides a reference for correcting gene identification errors in previous finch microarrays studies, and avoiding such errors in future studies.

A Platform for Combined DNA and Protein Microarrays Based on Total Internal Reflection Fluorescence

PubMed Central

Asanov, Alexander; Zepeda, Angélica; Vaca, Luis

2012-01-01

We have developed a novel microarray technology based on total internal reflection fluorescence (TIRF) in combination with DNA and protein bioassays immobilized at the TIRF surface. Unlike conventional microarrays that exhibit reduced signal-to-background ratio, require several stages of incubation, rinsing and stringency control, and measure only end-point results, our TIRF microarray technology provides several orders of magnitude better signal-to-background ratio, performs analysis rapidly in one step, and measures the entire course of association and dissociation kinetics between target DNA and protein molecules and the bioassays. In many practical cases detection of only DNA or protein markers alone does not provide the necessary accuracy for diagnosing a disease or detecting a pathogen. Here we describe TIRF microarrays that detect DNA and protein markers simultaneously, which reduces the probabilities of false responses. Supersensitive and multiplexed TIRF DNA and protein microarray technology may provide a platform for accurate diagnosis or enhanced research studies. Our TIRF microarray system can be mounted on upright or inverted microscopes or interfaced directly with CCD cameras equipped with a single objective, facilitating the development of portable devices. As proof-of-concept we applied TIRF microarrays for detecting molecular markers from Bacillus anthracis, the pathogen responsible for anthrax. PMID:22438738

Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays

PubMed Central

2010-01-01

Background Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. Results We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. Conclusions MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions. PMID:20509979

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform.

PubMed

Salehi, Reza; Tsoi, Stephen C M; Colazo, Marcos G; Ambrose, Divakar J; Robert, Claude; Dyck, Michael K

2017-01-30

Early embryonic loss is a large contributor to infertility in cattle. Moreover, bovine becomes an interesting model to study human preimplantation embryo development due to their similar developmental process. Although genetic factors are known to affect early embryonic development, the discovery of such factors has been a serious challenge. Microarray technology allows quantitative measurement and gene expression profiling of transcript levels on a genome-wide basis. One of the main decisions that have to be made when planning a microarray experiment is whether to use a one- or two-color approach. Two-color design increases technical replication, minimizes variability, improves sensitivity and accuracy as well as allows having loop designs, defining the common reference samples. Although microarray is a powerful biological tool, there are potential pitfalls that can attenuate its power. Hence, in this technical paper we demonstrate an optimized protocol for RNA extraction, amplification, labeling, hybridization of the labeled amplified RNA to the array, array scanning and data analysis using the two-color analysis strategy.

Microarray analysis of gene expression profiles in ripening pineapple fruits.

PubMed

Koia, Jonni H; Moyle, Richard L; Botella, Jose R

2012-12-18

Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the molecular basis of pineapple fruit ripening and non-climacteric fruit ripening in general.

Microarray analysis of gene expression profiles in ripening pineapple fruits

PubMed Central

2012-01-01

Background Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Results Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. Conclusions This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the molecular basis of pineapple fruit ripening and non-climacteric fruit ripening in general. PMID:23245313

Fully automated analysis of multi-resolution four-channel micro-array genotyping data

NASA Astrophysics Data System (ADS)

Abbaspour, Mohsen; Abugharbieh, Rafeef; Podder, Mohua; Tebbutt, Scott J.

2006-03-01

We present a fully-automated and robust microarray image analysis system for handling multi-resolution images (down to 3-micron with sizes up to 80 MBs per channel). The system is developed to provide rapid and accurate data extraction for our recently developed microarray analysis and quality control tool (SNP Chart). Currently available commercial microarray image analysis applications are inefficient, due to the considerable user interaction typically required. Four-channel DNA microarray technology is a robust and accurate tool for determining genotypes of multiple genetic markers in individuals. It plays an important role in the state of the art trend where traditional medical treatments are to be replaced by personalized genetic medicine, i.e. individualized therapy based on the patient's genetic heritage. However, fast, robust, and precise image processing tools are required for the prospective practical use of microarray-based genetic testing for predicting disease susceptibilities and drug effects in clinical practice, which require a turn-around timeline compatible with clinical decision-making. In this paper we have developed a fully-automated image analysis platform for the rapid investigation of hundreds of genetic variations across multiple genes. Validation tests indicate very high accuracy levels for genotyping results. Our method achieves a significant reduction in analysis time, from several hours to just a few minutes, and is completely automated requiring no manual interaction or guidance.

Parallel characterization of anaerobic toluene- and ethylbenzene-degrading microbial consortia by PCR-denaturing gradient gel electrophoresis, RNA-DNA membrane hybridization, and DNA microarray technology

NASA Technical Reports Server (NTRS)

Koizumi, Yoshikazu; Kelly, John J.; Nakagawa, Tatsunori; Urakawa, Hidetoshi; El-Fantroussi, Said; Al-Muzaini, Saleh; Fukui, Manabu; Urushigawa, Yoshikuni; Stahl, David A.

2002-01-01

A mesophilic toluene-degrading consortium (TDC) and an ethylbenzene-degrading consortium (EDC) were established under sulfate-reducing conditions. These consortia were first characterized by denaturing gradient gel electrophoresis (DGGE) fingerprinting of PCR-amplified 16S rRNA gene fragments, followed by sequencing. The sequences of the major bands (T-1 and E-2) belonging to TDC and EDC, respectively, were affiliated with the family Desulfobacteriaceae. Another major band from EDC (E-1) was related to an uncultured non-sulfate-reducing soil bacterium. Oligonucleotide probes specific for the 16S rRNAs of target organisms corresponding to T-1, E-1, and E-2 were designed, and hybridization conditions were optimized for two analytical formats, membrane and DNA microarray hybridization. Both formats were used to characterize the TDC and EDC, and the results of both were consistent with DGGE analysis. In order to assess the utility of the microarray format for analysis of environmental samples, oil-contaminated sediments from the coast of Kuwait were analyzed. The DNA microarray successfully detected bacterial nucleic acids from these samples, but probes targeting specific groups of sulfate-reducing bacteria did not give positive signals. The results of this study demonstrate the limitations and the potential utility of DNA microarrays for microbial community analysis.

Parallel Characterization of Anaerobic Toluene- and Ethylbenzene-Degrading Microbial Consortia by PCR-Denaturing Gradient Gel Electrophoresis, RNA-DNA Membrane Hybridization, and DNA Microarray Technology

PubMed Central

Koizumi, Yoshikazu; Kelly, John J.; Nakagawa, Tatsunori; Urakawa, Hidetoshi; El-Fantroussi, Saïd; Al-Muzaini, Saleh; Fukui, Manabu; Urushigawa, Yoshikuni; Stahl, David A.

2002-01-01

A mesophilic toluene-degrading consortium (TDC) and an ethylbenzene-degrading consortium (EDC) were established under sulfate-reducing conditions. These consortia were first characterized by denaturing gradient gel electrophoresis (DGGE) fingerprinting of PCR-amplified 16S rRNA gene fragments, followed by sequencing. The sequences of the major bands (T-1 and E-2) belonging to TDC and EDC, respectively, were affiliated with the family Desulfobacteriaceae. Another major band from EDC (E-1) was related to an uncultured non-sulfate-reducing soil bacterium. Oligonucleotide probes specific for the 16S rRNAs of target organisms corresponding to T-1, E-1, and E-2 were designed, and hybridization conditions were optimized for two analytical formats, membrane and DNA microarray hybridization. Both formats were used to characterize the TDC and EDC, and the results of both were consistent with DGGE analysis. In order to assess the utility of the microarray format for analysis of environmental samples, oil-contaminated sediments from the coast of Kuwait were analyzed. The DNA microarray successfully detected bacterial nucleic acids from these samples, but probes targeting specific groups of sulfate-reducing bacteria did not give positive signals. The results of this study demonstrate the limitations and the potential utility of DNA microarrays for microbial community analysis. PMID:12088997

Ontology-based, Tissue MicroArray oriented, image centered tissue bank

PubMed Central

Viti, Federica; Merelli, Ivan; Caprera, Andrea; Lazzari, Barbara; Stella, Alessandra; Milanesi, Luciano

2008-01-01

Background Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information. Results In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data. Conclusions Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes. PMID:18460177

Bioinformatic tools for inferring functional information from plant microarray data: tools for the first steps.

PubMed

Page, Grier P; Coulibaly, Issa

2008-01-01

Microarrays are a very powerful tool for quantifying the amount of RNA in samples; however, their ability to query essentially every gene in a genome, which can number in the tens of thousands, presents analytical and interpretative problems. As a result, a variety of software and web-based tools have been developed to help with these issues. This article highlights and reviews some of the tools for the first steps in the analysis of a microarray study. We have tried for a balance between free and commercial systems. We have organized the tools by topics including image processing tools (Section 2), power analysis tools (Section 3), image analysis tools (Section 4), database tools (Section 5), databases of functional information (Section 6), annotation tools (Section 7), statistical and data mining tools (Section 8), and dissemination tools (Section 9).

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

PubMed

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Employing image processing techniques for cancer detection using microarray images.

PubMed

Dehghan Khalilabad, Nastaran; Hassanpour, Hamid

2017-02-01

Microarray technology is a powerful genomic tool for simultaneously studying and analyzing the behavior of thousands of genes. The analysis of images obtained from this technology plays a critical role in the detection and treatment of diseases. The aim of the current study is to develop an automated system for analyzing data from microarray images in order to detect cancerous cases. The proposed system consists of three main phases, namely image processing, data mining, and the detection of the disease. The image processing phase performs operations such as refining image rotation, gridding (locating genes) and extracting raw data from images the data mining includes normalizing the extracted data and selecting the more effective genes. Finally, via the extracted data, cancerous cell is recognized. To evaluate the performance of the proposed system, microarray database is employed which includes Breast cancer, Myeloid Leukemia and Lymphomas from the Stanford Microarray Database. The results indicate that the proposed system is able to identify the type of cancer from the data set with an accuracy of 95.45%, 94.11%, and 100%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method.

PubMed

Bengtsson, Henrik; Hössjer, Ola

2006-03-01

Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general. A methodological study of affine models for gene expression data is carried out. Focus is on two-channel comparative studies, but the findings generalize also to single- and multi-channel data. The discussion applies to spotted as well as in-situ synthesized microarray data. Existing normalization methods such as curve-fit ("lowess") normalization, parallel and perpendicular translation normalization, and quantile normalization, but also dye-swap normalization are revisited in the light of the affine model and their strengths and weaknesses are investigated in this context. As a direct result from this study, we propose a robust non-parametric multi-dimensional affine normalization method, which can be applied to any number of microarrays with any number of channels either individually or all at once. A high-quality cDNA microarray data set with spike-in controls is used to demonstrate the power of the affine model and the proposed normalization method. We find that an affine model can explain non-linear intensity-dependent systematic effects in observed log-ratios. Affine normalization removes such artifacts for non-differentially expressed genes and assures that symmetry between negative and positive log-ratios is obtained, which is fundamental when identifying differentially expressed genes. In addition, affine normalization makes the empirical distributions in different channels more equal, which is the purpose of quantile normalization, and may also explain why dye-swap normalization works or fails. All methods are made available in the aroma package, which is a platform-independent package for R.

An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in LEukemia study prephase

PubMed Central

Kohlmann, Alexander; Kipps, Thomas J; Rassenti, Laura Z; Downing, James R; Shurtleff, Sheila A; Mills, Ken I; Gilkes, Amanda F; Hofmann, Wolf-Karsten; Basso, Giuseppe; Dell’Orto, Marta Campo; Foà, Robin; Chiaretti, Sabina; De Vos, John; Rauhut, Sonja; Papenhausen, Peter R; Hernández, Jesus M; Lumbreras, Eva; Yeoh, Allen E; Koay, Evelyn S; Li, Rachel; Liu, Wei-min; Williams, Paul M; Wieczorek, Lothar; Haferlach, Torsten

2008-01-01

Gene expression profiling has the potential to enhance current methods for the diagnosis of haematological malignancies. Here, we present data on 204 analyses from an international standardization programme that was conducted in 11 laboratories as a prephase to the Microarray Innovations in LEukemia (MILE) study. Each laboratory prepared two cell line samples, together with three replicate leukaemia patient lysates in two distinct stages: (i) a 5-d course of protocol training, and (ii) independent proficiency testing. Unsupervised, supervised, and r2 correlation analyses demonstrated that microarray analysis can be performed with remarkably high intra-laboratory reproducibility and with comparable quality and reliability. PMID:18573112

Analysis of Protein-DNA Interaction by Chromatin Immunoprecipitation and DNA Tiling Microarray (ChIP-on-chip).

PubMed

Gao, Hui; Zhao, Chunyan

2018-01-01

Chromatin immunoprecipitation (ChIP) has become the most effective and widely used tool to study the interactions between specific proteins or modified forms of proteins and a genomic DNA region. Combined with genome-wide profiling technologies, such as microarray hybridization (ChIP-on-chip) or massively parallel sequencing (ChIP-seq), ChIP could provide a genome-wide mapping of in vivo protein-DNA interactions in various organisms. Here, we describe a protocol of ChIP-on-chip that uses tiling microarray to obtain a genome-wide profiling of ChIPed DNA.

A model of binding on DNA microarrays: understanding the combined effect of probe synthesis failure, cross-hybridization, DNA fragmentation and other experimental details of affymetrix arrays

PubMed Central

2012-01-01

Background DNA microarrays are used both for research and for diagnostics. In research, Affymetrix arrays are commonly used for genome wide association studies, resequencing, and for gene expression analysis. These arrays provide large amounts of data. This data is analyzed using statistical methods that quite often discard a large portion of the information. Most of the information that is lost comes from probes that systematically fail across chips and from batch effects. The aim of this study was to develop a comprehensive model for hybridization that predicts probe intensities for Affymetrix arrays and that could provide a basis for improved microarray analysis and probe development. The first part of the model calculates probe binding affinities to all the possible targets in the hybridization solution using the Langmuir isotherm. In the second part of the model we integrate details that are specific to each experiment and contribute to the differences between hybridization in solution and on the microarray. These details include fragmentation, wash stringency, temperature, salt concentration, and scanner settings. Furthermore, the model fits probe synthesis efficiency and target concentration parameters directly to the data. All the parameters used in the model have a well-established physical origin. Results For the 302 chips that were analyzed the mean correlation between expected and observed probe intensities was 0.701 with a range of 0.88 to 0.55. All available chips were included in the analysis regardless of the data quality. Our results show that batch effects arise from differences in probe synthesis, scanner settings, wash strength, and target fragmentation. We also show that probe synthesis efficiencies for different nucleotides are not uniform. Conclusions To date this is the most complete model for binding on microarrays. This is the first model that includes both probe synthesis efficiency and hybridization kinetics/cross-hybridization. These two factors are sequence dependent and have a large impact on probe intensity. The results presented here provide novel insight into the effect of probe synthesis errors on Affymetrix microarrays; furthermore, the algorithms developed in this work provide useful tools for the analysis of cross-hybridization, probe synthesis efficiency, fragmentation, wash stringency, temperature, and salt concentration on microarray intensities. PMID:23270536

Deletion of the transcriptional coactivator PGC1α in skeletal muscles is associated with reduced expression of genes related to oxidative muscle function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hatazawa, Yukino; Research Fellow of Japan Society for the Promotion of Science, Tokyo; Minami, Kimiko

The expression of the transcriptional coactivator PGC1α is increased in skeletal muscles during exercise. Previously, we showed that increased PGC1α leads to prolonged exercise performance (the duration for which running can be continued) and, at the same time, increases the expression of branched-chain amino acid (BCAA) metabolism-related enzymes and genes that are involved in supplying substrates for the TCA cycle. We recently created mice with PGC1α knockout specifically in the skeletal muscles (PGC1α KO mice), which show decreased mitochondrial content. In this study, global gene expression (microarray) analysis was performed in the skeletal muscles of PGC1α KO mice compared withmore » that of wild-type control mice. As a result, decreased expression of genes involved in the TCA cycle, oxidative phosphorylation, and BCAA metabolism were observed. Compared with previously obtained microarray data on PGC1α-overexpressing transgenic mice, each gene showed the completely opposite direction of expression change. Bioinformatic analysis of the promoter region of genes with decreased expression in PGC1α KO mice predicted the involvement of several transcription factors, including a nuclear receptor, ERR, in their regulation. As PGC1α KO microarray data in this study show opposing findings to the PGC1α transgenic data, a loss-of-function experiment, as well as a gain-of-function experiment, revealed PGC1α’s function in the oxidative energy metabolism of skeletal muscles. - Highlights: • Microarray analysis was performed in the skeletal muscle of PGC1α KO mice. • Expression of genes in the oxidative energy metabolism was decreased. • Bioinformatic analysis of promoter region of the genes predicted involvement of ERR. • PGC1α KO microarray data in this study show the mirror image of transgenic data.« less

Gene expression profiling and pathway analysis in MCF-7 and MDA-MB-231 human breast cancer cell lines treated with dioscin

USDA-ARS?s Scientific Manuscript database

The long-term goal of our study is to understand the genetic and epigenetic mechanisms of breast cancer metastasis in human and to discover new possible genetic markers for use in clinical practice. We have used microarray technology (Human OneArray microarray, phylanxbiotech.com) to compare gene ex...

Microarray-Based Mapping for the Detection of Molecular Markers in Response to Aspergillus flavus Infection in Susceptible and Resistant Maize Lines

USDA-ARS?s Scientific Manuscript database

The objectives of this study were (1) to evaluate differential gene expression levels for resistance to A. flavus kernel infection in susceptible (Va35) and resistant (Mp313E) maize lines using Oligonucleotide and cDNA microarray analysis, (2) to evaluate differences in A. flavus accumulation betwee...

ABRF-MARG RESEARCH STUDY: EVALUATION OF SMALL SAMPLE NUCLEIC ACID AMPLIFICATION TECHNOLOGIES FOR GENE EXPRESSION PROFILING

EPA Science Inventory

Microarrays have had a significant impact on many areas of biology. However, there are still many fertile research areas that would benefit from microarray analysis but are limited by the amount of biological material that can be obtained (e.g. samples obtained by small biopsy, f...

Microarray platform affords improved product analysis in mammalian cell growth studies

PubMed Central

Li, Lingyun; Migliore, Nicole; Schaefer, Eugene; Sharfstein, Susan T.; Dordick, Jonathan S.; Linhardt, Robert J.

2014-01-01

High throughput (HT) platforms serve as cost-efficient and rapid screening method for evaluating the effect of cell culture conditions and screening of chemicals. The aim of the current study was to develop a high-throughput cell-based microarray platform to assess the effect of culture conditions on Chinese hamster ovary (CHO) cells. Specifically, growth, transgene expression and metabolism of a GS/MSX CHO cell line, which produces a therapeutic monoclonal antibody, was examined using microarray system in conjunction with conventional shake flask platform in a non-proprietary medium. The microarray system consists of 60 nl spots of cells encapsulated in alginate and separated in groups via an 8-well chamber system attached to the chip. Results show the non-proprietary medium developed allows cell growth, production and normal glycosylation of recombinant antibody and metabolism of the recombinant CHO cells in both the microarray and shake flask platforms. In addition, 10.3 mM glutamate addition to the defined base media results in lactate metabolism shift in the recombinant GS/MSX CHO cells in the shake flask platform. Ultimately, the results demonstrate that the high-throughput microarray platform has the potential to be utilized for evaluating the impact of media additives on cellular processes, such as, cell growth, metabolism and productivity. PMID:24227746

Comparative transcriptional and translational analysis of leptospiral outer membrane protein expression in response to temperature.

PubMed

Lo, Miranda; Cordwell, Stuart J; Bulach, Dieter M; Adler, Ben

2009-12-08

Leptospirosis is a global zoonosis affecting millions of people annually. Transcriptional changes in response to temperature were previously investigated using microarrays to identify genes potentially expressed upon host entry. Past studies found that various leptospiral outer membrane proteins are differentially expressed at different temperatures. However, our microarray studies highlighted a divergence between protein abundance and transcript levels for some proteins. Given the abundance of post-transcriptional expression control mechanisms, this finding highlighted the importance of global protein analysis systems. To complement our previous transcription study, we evaluated differences in the proteins of the leptospiral outer membrane fraction in response to temperature upshift. Outer membrane protein-enriched fractions from Leptospira interrogans grown at 30 degrees C or overnight upshift to 37 degrees C were isolated and the relative abundance of each protein was determined by iTRAQ analysis coupled with two-dimensional liquid chromatography and tandem mass spectrometry (2-DLC/MS-MS). We identified 1026 proteins with 99% confidence; 27 and 66 were present at elevated and reduced abundance respectively. Protein abundance changes were compared with transcriptional differences determined from the microarray studies. While there was some correlation between the microarray and iTRAQ data, a subset of genes that showed no differential expression by microarray was found to encode temperature-regulated proteins. This set of genes is of particular interest as it is likely that regulation of their expression occurs post-transcriptionally, providing an opportunity to develop hypotheses about the molecular dynamics of the outer membrane of Leptospira in response to changing environments. This is the first study to compare transcriptional and translational responses to temperature shift in L. interrogans. The results thus provide an insight into the mechanisms used by L. interrogans to adapt to conditions encountered in the host and to cause disease. Our results suggest down-regulation of protein expression in response to temperature, and decreased expression of outer membrane proteins may facilitate minimal interaction with host immune mechanisms.

GeneXplorer: an interactive web application for microarray data visualization and analysis.

PubMed

Rees, Christian A; Demeter, Janos; Matese, John C; Botstein, David; Sherlock, Gavin

2004-10-01

When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/.

Nanotechnology: moving from microarrays toward nanoarrays.

PubMed

Chen, Hua; Li, Jun

2007-01-01

Microarrays are important tools for high-throughput analysis of biomolecules. The use of microarrays for parallel screening of nucleic acid and protein profiles has become an industry standard. A few limitations of microarrays are the requirement for relatively large sample volumes and elongated incubation time, as well as the limit of detection. In addition, traditional microarrays make use of bulky instrumentation for the detection, and sample amplification and labeling are quite laborious, which increase analysis cost and delays the time for obtaining results. These problems limit microarray techniques from point-of-care and field applications. One strategy for overcoming these problems is to develop nanoarrays, particularly electronics-based nanoarrays. With further miniaturization, higher sensitivity, and simplified sample preparation, nanoarrays could potentially be employed for biomolecular analysis in personal healthcare and monitoring of trace pathogens. In this chapter, it is intended to introduce the concept and advantage of nanotechnology and then describe current methods and protocols for novel nanoarrays in three aspects: (1) label-free nucleic acids analysis using nanoarrays, (2) nanoarrays for protein detection by conventional optical fluorescence microscopy as well as by novel label-free methods such as atomic force microscopy, and (3) nanoarray for enzymatic-based assay. These nanoarrays will have significant applications in drug discovery, medical diagnosis, genetic testing, environmental monitoring, and food safety inspection.

BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets

PubMed Central

2010-01-01

Background The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for 'systems'-level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log2- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells. Results The only input required by BABAR is unprocessed GenePix or BlueFuse microarray data files. BABAR provides a combination of 'within' and 'between' microarray normalisation steps and diagnostic boxplots. When applied to a real heterogeneous dataset, BABAR normalised the dataset to produce a comparable scaling between the microarrays, with the microarray data in excellent agreement with RT-PCR analysis. When applied to a real non-heterogeneous dataset and a simulated dataset, BABAR's performance in identifying differentially expressed genes showed some benefits over standard techniques. Conclusions BABAR is an easy-to-use software tool, simplifying the simultaneous normalisation of heterogeneous two-colour common reference design cDNA microarray-based transcriptomic datasets. We show BABAR transforms real and simulated datasets to allow for the correct interpretation of these data, and is the ideal tool to facilitate the identification of differentially expressed genes or network inference analysis from transcriptomic datasets. PMID:20128918

MiMiR – an integrated platform for microarray data sharing, mining and analysis

PubMed Central

Tomlinson, Chris; Thimma, Manjula; Alexandrakis, Stelios; Castillo, Tito; Dennis, Jayne L; Brooks, Anthony; Bradley, Thomas; Turnbull, Carly; Blaveri, Ekaterini; Barton, Geraint; Chiba, Norie; Maratou, Klio; Soutter, Pat; Aitman, Tim; Game, Laurence

2008-01-01

Background Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data. Results A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package. Conclusion The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies. PMID:18801157

MiMiR--an integrated platform for microarray data sharing, mining and analysis.

PubMed

Tomlinson, Chris; Thimma, Manjula; Alexandrakis, Stelios; Castillo, Tito; Dennis, Jayne L; Brooks, Anthony; Bradley, Thomas; Turnbull, Carly; Blaveri, Ekaterini; Barton, Geraint; Chiba, Norie; Maratou, Klio; Soutter, Pat; Aitman, Tim; Game, Laurence

2008-09-18

Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data. A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package. The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies.

Biomarkers of the Hedgehog/Smoothened pathway in healthy volunteers

PubMed Central

Kadam, Sunil K; Patel, Bharvin K R; Jones, Emma; Nguyen, Tuan S; Verma, Lalit K; Landschulz, Katherine T; Stepaniants, Sergey; Li, Bin; Brandt, John T; Brail, Leslie H

2012-01-01

The Hedgehog (Hh) pathway is involved in oncogenic transformation and tumor maintenance. The primary objective of this study was to select surrogate tissue to measure messenger ribonucleic acid (mRNA) levels of Hh pathway genes for measurement of pharmacodynamic effect. Expression of Hh pathway specific genes was measured by quantitative real time polymerase chain reaction (qRT-PCR) and global gene expression using Affymetrix U133 microarrays. Correlations were made between the expression of specific genes determined by qRT-PCR and normalized microarray data. Gene ontology analysis using microarray data for a broader set of Hh pathway genes was performed to identify additional Hh pathway-related markers in the surrogate tissue. RNA extracted from blood, hair follicle, and skin obtained from healthy subjects was analyzed by qRT-PCR for 31 genes, whereas 8 samples were analyzed for a 7-gene subset. Twelve sample sets, each with ≤500 ng total RNA derived from hair, skin, and blood, were analyzed using Affymetrix U133 microarrays. Transcripts for several Hh pathway genes were undetectable in blood using qRT-PCR. Skin was the most desirable matrix, followed by hair follicle. Whether processed by robust multiarray average or microarray suite 5 (MAS5), expression patterns of individual samples showed co-clustered signals; both normalization methods were equally effective for unsupervised analysis. The MAS5- normalized probe sets appeared better suited for supervised analysis. This work provides the basis for selection of a surrogate tissue and an expression analysis-based approach to evaluate pathway-related genes as markers of pharmacodynamic effect with novel inhibitors of the Hh pathway. PMID:22611475

permGPU: Using graphics processing units in RNA microarray association studies.

PubMed

Shterev, Ivo D; Jung, Sin-Ho; George, Stephen L; Owzar, Kouros

2010-06-16

Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data.

PubMed

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R; Del Río-Navarro, Blanca E; Mendoza-Vargas, Alfredo; Sánchez, Filiberto; Ochoa-Leyva, Adrian

2017-01-01

In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6-10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments.

Integrated Microfluidic Devices for Automated Microarray-Based Gene Expression and Genotyping Analysis

NASA Astrophysics Data System (ADS)

Liu, Robin H.; Lodes, Mike; Fuji, H. Sho; Danley, David; McShea, Andrew

Microarray assays typically involve multistage sample processing and fluidic handling, which are generally labor-intensive and time-consuming. Automation of these processes would improve robustness, reduce run-to-run and operator-to-operator variation, and reduce costs. In this chapter, a fully integrated and self-contained microfluidic biochip device that has been developed to automate the fluidic handling steps for microarray-based gene expression or genotyping analysis is presented. The device consists of a semiconductor-based CustomArray® chip with 12,000 features and a microfluidic cartridge. The CustomArray was manufactured using a semiconductor-based in situ synthesis technology. The micro-fluidic cartridge consists of microfluidic pumps, mixers, valves, fluid channels, and reagent storage chambers. Microarray hybridization and subsequent fluidic handling and reactions (including a number of washing and labeling steps) were performed in this fully automated and miniature device before fluorescent image scanning of the microarray chip. Electrochemical micropumps were integrated in the cartridge to provide pumping of liquid solutions. A micromixing technique based on gas bubbling generated by electrochemical micropumps was developed. Low-cost check valves were implemented in the cartridge to prevent cross-talk of the stored reagents. Gene expression study of the human leukemia cell line (K562) and genotyping detection and sequencing of influenza A subtypes have been demonstrated using this integrated biochip platform. For gene expression assays, the microfluidic CustomArray device detected sample RNAs with a concentration as low as 0.375 pM. Detection was quantitative over more than three orders of magnitude. Experiment also showed that chip-to-chip variability was low indicating that the integrated microfluidic devices eliminate manual fluidic handling steps that can be a significant source of variability in genomic analysis. The genotyping results showed that the device identified influenza A hemagglutinin and neuraminidase subtypes and sequenced portions of both genes, demonstrating the potential of integrated microfluidic and microarray technology for multiple virus detection. The device provides a cost-effective solution to eliminate labor-intensive and time-consuming fluidic handling steps and allows microarray-based DNA analysis in a rapid and automated fashion.

ArraySolver: an algorithm for colour-coded graphical display and Wilcoxon signed-rank statistics for comparing microarray gene expression data.

PubMed

Khan, Haseeb Ahmad

2004-01-01

The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann-Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n < or = 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform.

ArraySolver: An Algorithm for Colour-Coded Graphical Display and Wilcoxon Signed-Rank Statistics for Comparing Microarray Gene Expression Data

PubMed Central

2004-01-01

The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann–Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n ≤ 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform. PMID:18629036

Principles of gene microarray data analysis.

PubMed

Mocellin, Simone; Rossi, Carlo Riccardo

2007-01-01

The development of several gene expression profiling methods, such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE), and gene microarray, together with the sequencing of the human genome, has provided an opportunity to monitor and investigate the complex cascade of molecular events leading to tumor development and progression. The availability of such large amounts of information has shifted the attention of scientists towards a nonreductionist approach to biological phenomena. High throughput technologies can be used to follow changing patterns of gene expression over time. Among them, gene microarray has become prominent because it is easier to use, does not require large-scale DNA sequencing, and allows for the parallel quantification of thousands of genes from multiple samples. Gene microarray technology is rapidly spreading worldwide and has the potential to drastically change the therapeutic approach to patients affected with tumor. Therefore, it is of paramount importance for both researchers and clinicians to know the principles underlying the analysis of the huge amount of data generated with microarray technology.

Identification of differentially expressed genes and false discovery rate in microarray studies.

PubMed

Gusnanto, Arief; Calza, Stefano; Pawitan, Yudi

2007-04-01

To highlight the development in microarray data analysis for the identification of differentially expressed genes, particularly via control of false discovery rate. The emergence of high-throughput technology such as microarrays raises two fundamental statistical issues: multiplicity and sensitivity. We focus on the biological problem of identifying differentially expressed genes. First, multiplicity arises due to testing tens of thousands of hypotheses, rendering the standard P value meaningless. Second, known optimal single-test procedures such as the t-test perform poorly in the context of highly multiple tests. The standard approach of dealing with multiplicity is too conservative in the microarray context. The false discovery rate concept is fast becoming the key statistical assessment tool replacing the P value. We review the false discovery rate approach and argue that it is more sensible for microarray data. We also discuss some methods to take into account additional information from the microarrays to improve the false discovery rate. There is growing consensus on how to analyse microarray data using the false discovery rate framework in place of the classical P value. Further research is needed on the preprocessing of the raw data, such as the normalization step and filtering, and on finding the most sensitive test procedure.

Support vector machine and principal component analysis for microarray data classification

NASA Astrophysics Data System (ADS)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Experimental Approaches to Microarray Analysis of Tumor Samples

ERIC Educational Resources Information Center

Furge, Laura Lowe; Winter, Michael B.; Meyers, Jacob I.; Furge, Kyle A.

2008-01-01

Comprehensive measurement of gene expression using high-density nucleic acid arrays (i.e. microarrays) has become an important tool for investigating the molecular differences in clinical and research samples. Consequently, inclusion of discussion in biochemistry, molecular biology, or other appropriate courses of microarray technologies has…

Multiplex cDNA quantification method that facilitates the standardization of gene expression data

PubMed Central

Gotoh, Osamu; Murakami, Yasufumi; Suyama, Akira

2011-01-01

Microarray-based gene expression measurement is one of the major methods for transcriptome analysis. However, current microarray data are substantially affected by microarray platforms and RNA references because of the microarray method can provide merely the relative amounts of gene expression levels. Therefore, valid comparisons of the microarray data require standardized platforms, internal and/or external controls and complicated normalizations. These requirements impose limitations on the extensive comparison of gene expression data. Here, we report an effective approach to removing the unfavorable limitations by measuring the absolute amounts of gene expression levels on common DNA microarrays. We have developed a multiplex cDNA quantification method called GEP-DEAN (Gene expression profiling by DCN-encoding-based analysis). The method was validated by using chemically synthesized DNA strands of known quantities and cDNA samples prepared from mouse liver, demonstrating that the absolute amounts of cDNA strands were successfully measured with a sensitivity of 18 zmol in a highly multiplexed manner in 7 h. PMID:21415008

Spot detection and image segmentation in DNA microarray data.

PubMed

Qin, Li; Rueda, Luis; Ali, Adnan; Ngom, Alioune

2005-01-01

Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.

Split-plot microarray experiments: issues of design, power and sample size.

PubMed

Tsai, Pi-Wen; Lee, Mei-Ling Ting

2005-01-01

This article focuses on microarray experiments with two or more factors in which treatment combinations of the factors corresponding to the samples paired together onto arrays are not completely random. A main effect of one (or more) factor(s) is confounded with arrays (the experimental blocks). This is called a split-plot microarray experiment. We utilise an analysis of variance (ANOVA) model to assess differentially expressed genes for between-array and within-array comparisons that are generic under a split-plot microarray experiment. Instead of standard t- or F-test statistics that rely on mean square errors of the ANOVA model, we use a robust method, referred to as 'a pooled percentile estimator', to identify genes that are differentially expressed across different treatment conditions. We illustrate the design and analysis of split-plot microarray experiments based on a case application described by Jin et al. A brief discussion of power and sample size for split-plot microarray experiments is also presented.

Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

PubMed

Chavan, Shweta S; Bauer, Michael A; Peterson, Erich A; Heuck, Christoph J; Johann, Donald J

2013-01-01

Transcriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, and novel gene fusions. Given the voluminous amounts of historical microarray data, there is now a need to associate and integrate microarray and RNA-seq data via advanced bioinformatic approaches. Custom software was developed following a model-view-controller (MVC) approach to integrate Affymetrix probe set-IDs, and gene annotation information from a variety of sources. The tool/approach employs an assortment of strategies to integrate, cross reference, and associate microarray and RNA-seq datasets. Output from a variety of transcriptome reconstruction and quantitation tools (e.g., Cufflinks) can be directly integrated, and/or associated with Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a diversity of sources. Strategies are employed to maximize the annotation and cross referencing process. Custom gene sets (e.g., MM 70 risk score (GEP-70)) can be specified, and the tool can be directly assimilated into an RNA-seq pipeline. A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data, in conjunction with richer RNA-seq data, is now assisting with the study of MM cancer biology.

Microarray analysis of the rat lacrimal gland following the loss of parasympathetic control of secretion

PubMed Central

Nguyen, Doan H.; Toshida, Hiroshi; Schurr, Jill; Beuerman, Roger W.

2010-01-01

Previous studies showed that loss of muscarinic parasympathetic input to the lacrimal gland (LG) leads to a dramatic reduction in tear secretion and profound changes to LG structure. In this study, we used DNA microarrays to examine the regulation of the gene expression of the genes for secretory function and organization of the LG. Long-Evans rats anesthetized with a mixture of ketamine/xylazine (80:10 mg/kg) underwent unilateral sectioning of the greater superficial petrosal nerve, the input to the pterygopalatine ganglion. After 7 days, tear secretion was measured, the animals were killed, and structural changes in the LG were examined by light microscopy. Total RNA from control and experimental LGs (n = 5) was used for DNA microarray analysis employing the U34A GeneChip. Three statistical algorithms (detection, change call, and signal log ratio) were used to determine differential gene expression using the Microarray Suite (5.0) and Data Mining Tools (3.0). Tear secretion was significantly reduced and corneal ulcers developed in all experimental eyes. Light microscopy showed breakdown of the acinar structure of the LG. DNA microarray analysis showed downregulation of genes associated with the endoplasmic reticulum and Golgi, including genes involved in protein folding and processing. Conversely, transcripts for cytoskeleton and extracellular matrix components, inflammation, and apoptosis were upregulated. The number of significantly upregulated genes (116) was substantially greater than the number of downregulated genes (49). Removal of the main secretory input to the rat LG resulted in clinical symptoms associated with severe dry eye. Components of the secretory pathway were negatively affected, and the increase in cell proliferation and inflammation may lead to loss of organization in the parasympathectomized lacrimal gland. PMID:15084711

Identification of novel and known oocyte-specific genes using complementary DNA subtraction and microarray analysis in three different species.

PubMed

Vallée, Maud; Gravel, Catherine; Palin, Marie-France; Reghenas, Hélène; Stothard, Paul; Wishart, David S; Sirard, Marc-André

2005-07-01

The main objective of the present study was to identify novel oocyte-specific genes in three different species: bovine, mouse, and Xenopus laevis. To achieve this goal, two powerful technologies were combined: a polymerase chain reaction (PCR)-based cDNA subtraction, and cDNA microarrays. Three subtractive libraries consisting of 3456 clones were established and enriched for oocyte-specific transcripts. Sequencing analysis of the positive insert-containing clones resulted in the following classification: 53% of the clones corresponded to known cDNAs, 26% were classified as uncharacterized cDNAs, and a final 9% were classified as novel sequences. All these clones were used for cDNA microarray preparation. Results from these microarray analyses revealed that in addition to already known oocyte-specific genes, such as GDF9, BMP15, and ZP, known genes with unknown function in the oocyte were identified, such as a MLF1-interacting protein (MLF1IP), B-cell translocation gene 4 (BTG4), and phosphotyrosine-binding protein (xPTB). Furthermore, 15 novel oocyte-specific genes were validated by reverse transcription-PCR to confirm their preferential expression in the oocyte compared to somatic tissues. The results obtained in the present study confirmed that microarray analysis is a robust technique to identify true positives from the suppressive subtractive hybridization experiment. Furthermore, obtaining oocyte-specific genes from three species simultaneously allowed us to look at important genes that are conserved across species. Further characterization of these novel oocyte-specific genes will lead to a better understanding of the molecular mechanisms related to the unique functions found in the oocyte.

Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

PubMed Central

Weniger, Markus; Engelmann, Julia C; Schultz, Jörg

2007-01-01

Background Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. Results We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at . Conclusion GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at . PMID:17543125

Factorial microarray analysis of zebra mussel (Dreissena polymorpha: Dreissenidae, Bivalvia) adhesion

PubMed Central

2010-01-01

Background The zebra mussel (Dreissena polymorpha) has been well known for its expertise in attaching to substances under the water. Studies in past decades on this underwater adhesion focused on the adhesive protein isolated from the byssogenesis apparatus of the zebra mussel. However, the mechanism of the initiation, maintenance, and determination of the attachment process remains largely unknown. Results In this study, we used a zebra mussel cDNA microarray previously developed in our lab and a factorial analysis to identify the genes that were involved in response to the changes of four factors: temperature (Factor A), current velocity (Factor B), dissolved oxygen (Factor C), and byssogenesis status (Factor D). Twenty probes in the microarray were found to be modified by one of the factors. The transcription products of four selected genes, DPFP-BG20_A01, EGP-BG97/192_B06, EGP-BG13_G05, and NH-BG17_C09 were unique to the zebra mussel foot based on the results of quantitative reverse transcription PCR (qRT-PCR). The expression profiles of these four genes under the attachment and non-attachment were also confirmed by qRT-PCR and the result is accordant to that from microarray assay. The in situ hybridization with the RNA probes of two identified genes DPFP-BG20_A01 and EGP-BG97/192_B06 indicated that both of them were expressed by a type of exocrine gland cell located in the middle part of the zebra mussel foot. Conclusions The results of this study suggested that the changes of D. polymorpha byssogenesis status and the environmental factors can dramatically affect the expression profiles of the genes unique to the foot. It turns out that the factorial design and analysis of the microarray experiment is a reliable method to identify the influence of multiple factors on the expression profiles of the probesets in the microarray; therein it provides a powerful tool to reveal the mechanism of zebra mussel underwater attachment. PMID:20509938

A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells.

PubMed

Wolff, Alexander; Bayerlová, Michaela; Gaedcke, Jochen; Kube, Dieter; Beißbarth, Tim

2018-01-01

Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines.

Clear cell papillary renal cell carcinoma: a chromosomal microarray analysis of two cases using a novel Molecular Inversion Probe (MIP) technology.

PubMed

Alexiev, Borislav A; Zou, Ying S

2014-12-01

Chromosomal microarray analysis using novel Molecular Inversion Probe (MIP) technology demonstrated 2,570 kb copy neutral LOH of 10q11.22 in two clear cell papillary renal cell carcinomas. In addition, one of the tumors had a big 29,784 kb deletion of 13q11-q14.2. There were two variants of unknown significance, a 2,509 kb gain of Xp22.33 and a 257 kb homozygous deletion of 8p11.22. The somatic mutation panel containing 74 mutations in nine genes did not reveal any mutations. Besides identification of submicroscopic duplications or deletions, SNP microarrays can reveal abnormal allelic imbalances including LOH and copy neutral LOH, which cannot be recognized by chromosome, FISH, and non-SNP microarray arrays. To the best of our knowledge, this is the first study demonstrating copy neutral LOH of 10q11.22 in clear cell papillary renal cell carcinomas using the new MIP SNP OncoScan FFPE Assay Kit on formalin-fixed paraffin-embedded tumor samples. Copyright © 2014 Elsevier GmbH. All rights reserved.

A Unique Procedure to Identify Cell Surface Markers Through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis.

PubMed

Sugii, Yuh; Kasai, Tomonari; Ikeda, Masashi; Vaidyanath, Arun; Kumon, Kazuki; Mizutani, Akifumi; Seno, Akimasa; Tokutaka, Heizo; Kudoh, Takayuki; Seno, Masaharu

2016-01-01

To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.

Characterization of regulatory pathways in Xylella fastidiosa: genes and phenotypes controlled by algU.

PubMed

Shi, Xiang Yang; Dumenyo, C Korsi; Hernandez-Martinez, Rufina; Azad, Hamid; Cooksey, Donald A

2007-11-01

Many virulence genes in plant bacterial pathogens are coordinately regulated by "global" regulatory genes. Conducting DNA microarray analysis of bacterial mutants of such genes, compared with the wild type, can help to refine the list of genes that may contribute to virulence in bacterial pathogens. The regulatory gene algU, with roles in stress response and regulation of the biosynthesis of the exopolysaccharide alginate in Pseudomonas aeruginosa and many other bacteria, has been extensively studied. The role of algU in Xylella fastidiosa, the cause of Pierce's disease of grapevines, was analyzed by mutation and whole-genome microarray analysis to define its involvement in aggregation, biofilm formation, and virulence. In this study, an algU::nptII mutant had reduced cell-cell aggregation, attachment, and biofilm formation and lower virulence in grapevines. Microarray analysis showed that 42 genes had significantly lower expression in the algU::nptII mutant than in the wild type. Among these are several genes that could contribute to cell aggregation and biofilm formation, as well as other physiological processes such as virulence, competition, and survival.

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

NASA Astrophysics Data System (ADS)

Tchagang, Alain B.; Tewfik, Ahmed H.

2006-12-01

Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.

Identification of rat lung-specific microRNAs by micoRNA microarray: valuable discoveries for the facilitation of lung research.

PubMed

Wang, Yang; Weng, Tingting; Gou, Deming; Chen, Zhongming; Chintagari, Narendranath Reddy; Liu, Lin

2007-01-24

An important mechanism for gene regulation utilizes small non-coding RNAs called microRNAs (miRNAs). These small RNAs play important roles in tissue development, cell differentiation and proliferation, lipid and fat metabolism, stem cells, exocytosis, diseases and cancers. To date, relatively little is known about functions of miRNAs in the lung except lung cancer. In this study, we utilized a rat miRNA microarray containing 216 miRNA probes, printed in-house, to detect the expression of miRNAs in the rat lung compared to the rat heart, brain, liver, kidney and spleen. Statistical analysis using Significant Analysis of Microarray (SAM) and Tukey Honestly Significant Difference (HSD) revealed 2 miRNAs (miR-195 and miR-200c) expressed specifically in the lung and 9 miRNAs co-expressed in the lung and another organ. 12 selected miRNAs were verified by Northern blot analysis. The identified lung-specific miRNAs from this work will facilitate functional studies of miRNAs during normal physiological and pathophysiological processes of the lung.

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed Central

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-01-01

Background Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Results Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. Conclusion In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects. PMID:12962547

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-09-08

Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.

Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data.

PubMed

Tong, Dong Ling; Schierz, Amanda C

2011-09-01

Suitable techniques for microarray analysis have been widely researched, particularly for the study of marker genes expressed to a specific type of cancer. Most of the machine learning methods that have been applied to significant gene selection focus on the classification ability rather than the selection ability of the method. These methods also require the microarray data to be preprocessed before analysis takes place. The objective of this study is to develop a hybrid genetic algorithm-neural network (GANN) model that emphasises feature selection and can operate on unpreprocessed microarray data. The GANN is a hybrid model where the fitness value of the genetic algorithm (GA) is based upon the number of samples correctly labelled by a standard feedforward artificial neural network (ANN). The model is evaluated by using two benchmark microarray datasets with different array platforms and differing number of classes (a 2-class oligonucleotide microarray data for acute leukaemia and a 4-class complementary DNA (cDNA) microarray dataset for SRBCTs (small round blue cell tumours)). The underlying concept of the GANN algorithm is to select highly informative genes by co-evolving both the GA fitness function and the ANN weights at the same time. The novel GANN selected approximately 50% of the same genes as the original studies. This may indicate that these common genes are more biologically significant than other genes in the datasets. The remaining 50% of the significant genes identified were used to build predictive models and for both datasets, the models based on the set of genes extracted by the GANN method produced more accurate results. The results also suggest that the GANN method not only can detect genes that are exclusively associated with a single cancer type but can also explore the genes that are differentially expressed in multiple cancer types. The results show that the GANN model has successfully extracted statistically significant genes from the unpreprocessed microarray data as well as extracting known biologically significant genes. We also show that assessing the biological significance of genes based on classification accuracy may be misleading and though the GANN's set of extra genes prove to be more statistically significant than those selected by other methods, a biological assessment of these genes is highly recommended to confirm their functionality. Copyright © 2011 Elsevier B.V. All rights reserved.

Comparative analysis of gene expression profiles of hip articular cartilage between non-traumatic necrosis and osteoarthritis.

PubMed

Wang, Wenyu; Liu, Yang; Hao, Jingcan; Zheng, Shuyu; Wen, Yan; Xiao, Xiao; He, Awen; Fan, Qianrui; Zhang, Feng; Liu, Ruiyu

2016-10-10

Hip cartilage destruction is consistently observed in the non-traumatic osteonecrosis of femoral head (NOFH) and accelerates its bone necrosis. The molecular mechanism underlying the cartilage damage of NOFH remains elusive. In this study, we conducted a systematically comparative study of gene expression profiles between NOFH and osteoarthritis (OA). Hip articular cartilage specimens were collected from 12 NOFH patients and 12 controls with traumatic femoral neck fracture for microarray (n=4) and quantitative real-time PCR validation experiments (n=8). Gene expression profiling of articular cartilage was performed using Agilent Human 4×44K Microarray chip. The accuracy of microarray experiment was further validated by qRT-PCR. Gene expression results of OA hip cartilage were derived from previously published study. Significance Analysis of Microarrays (SAM) software was applied for identifying differently expressed genes. Gene ontology (GO) and pathway enrichment analysis were conducted by Gene Set Enrichment Analysis software and DAVID tool, respectively. Totally, 27 differently expressed genes were identified for NOFH. Comparing the gene expression profiles of NOFH cartilage and OA cartilage detected 8 common differently expressed genes, including COL5A1, OGN, ANGPTL4, CRIP1, NFIL3, METRNL, ID2 and STEAP1. GO comparative analysis identified 10 common significant GO terms, mainly implicated in apoptosis and development process. Pathway comparative analysis observed that ECM-receptor interaction pathway and focal adhesion pathway were enriched in the differently expressed genes of both NOFH and hip OA. In conclusion, we identified a set of differently expressed genes, GO and pathways for NOFH articular destruction, some of which were also involved in the hip OA. Our study results may help to reveal the pathogenetic similarities and differences of cartilage damage of NOFH and hip OA. Copyright © 2016 Elsevier B.V. All rights reserved.

The effect of column purification on cDNA indirect labelling for microarrays

PubMed Central

Molas, M Lia; Kiss, John Z

2007-01-01

Background The success of the microarray reproducibility is dependent upon the performance of standardized procedures. Since the introduction of microarray technology for the analysis of global gene expression, reproducibility of results among different laboratories has been a major problem. Two of the main contributors to this variability are the use of different microarray platforms and different laboratory practices. In this paper, we address the latter question in terms of how variation in one of the steps of a labelling procedure affects the cDNA product prior to microarray hybridization. Results We used a standard procedure to label cDNA for microarray hybridization and employed different types of column chromatography for cDNA purification. After purifying labelled cDNA, we used the Agilent 2100 Bioanalyzer and agarose gel electrophoresis to assess the quality of the labelled cDNA before its hybridization onto a microarray platform. There were major differences in the cDNA profile (i.e. cDNA fragment lengths and abundance) as a result of using four different columns for purification. In addition, different columns have different efficiencies to remove rRNA contamination. This study indicates that the appropriate column to use in this type of protocol has to be experimentally determined. Finally, we present new evidence establishing the importance of testing the method of purification used during an indirect labelling procedure. Our results confirm the importance of assessing the quality of the sample in the labelling procedure prior to hybridization onto a microarray platform. Conclusion Standardization of column purification systems to be used in labelling procedures will improve the reproducibility of microarray results among different laboratories. In addition, implementation of a quality control check point of the labelled samples prior to microarray hybridization will prevent hybridizing a poor quality sample to expensive micorarrays. PMID:17597522

The effect of column purification on cDNA indirect labelling for microarrays.

PubMed

Molas, M Lia; Kiss, John Z

2007-06-27

The success of the microarray reproducibility is dependent upon the performance of standardized procedures. Since the introduction of microarray technology for the analysis of global gene expression, reproducibility of results among different laboratories has been a major problem. Two of the main contributors to this variability are the use of different microarray platforms and different laboratory practices. In this paper, we address the latter question in terms of how variation in one of the steps of a labelling procedure affects the cDNA product prior to microarray hybridization. We used a standard procedure to label cDNA for microarray hybridization and employed different types of column chromatography for cDNA purification. After purifying labelled cDNA, we used the Agilent 2100 Bioanalyzer and agarose gel electrophoresis to assess the quality of the labelled cDNA before its hybridization onto a microarray platform. There were major differences in the cDNA profile (i.e. cDNA fragment lengths and abundance) as a result of using four different columns for purification. In addition, different columns have different efficiencies to remove rRNA contamination. This study indicates that the appropriate column to use in this type of protocol has to be experimentally determined. Finally, we present new evidence establishing the importance of testing the method of purification used during an indirect labelling procedure. Our results confirm the importance of assessing the quality of the sample in the labelling procedure prior to hybridization onto a microarray platform. Standardization of column purification systems to be used in labelling procedures will improve the reproducibility of microarray results among different laboratories. In addition, implementation of a quality control check point of the labelled samples prior to microarray hybridization will prevent hybridizing a poor quality sample to expensive micorarrays.

Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*.

PubMed

Barrett, Tanya; Edgar, Ron

2006-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Profiling In Situ Microbial Community Structure with an Amplification Microarray

PubMed Central

Knickerbocker, Christopher; Bryant, Lexi; Golova, Julia; Wiles, Cory; Williams, Kenneth H.; Peacock, Aaron D.; Long, Philip E.

2013-01-01

The objectives of this study were to unify amplification, labeling, and microarray hybridization chemistries within a single, closed microfluidic chamber (an amplification microarray) and verify technology performance on a series of groundwater samples from an in situ field experiment designed to compare U(VI) mobility under conditions of various alkalinities (as HCO3−) during stimulated microbial activity accompanying acetate amendment. Analytical limits of detection were between 2 and 200 cell equivalents of purified DNA. Amplification microarray signatures were well correlated with 16S rRNA-targeted quantitative PCR results and hybridization microarray signatures. The succession of the microbial community was evident with and consistent between the two microarray platforms. Amplification microarray analysis of acetate-treated groundwater showed elevated levels of iron-reducing bacteria (Flexibacter, Geobacter, Rhodoferax, and Shewanella) relative to the average background profile, as expected. Identical molecular signatures were evident in the transect treated with acetate plus NaHCO3, but at much lower signal intensities and with a much more rapid decline (to nondetection). Azoarcus, Thaurea, and Methylobacterium were responsive in the acetate-only transect but not in the presence of bicarbonate. Observed differences in microbial community composition or response to bicarbonate amendment likely had an effect on measured rates of U reduction, with higher rates probable in the part of the field experiment that was amended with bicarbonate. The simplification in microarray-based work flow is a significant technological advance toward entirely closed-amplicon microarray-based tests and is generally extensible to any number of environmental monitoring applications. PMID:23160129

The Diagnostic Yield of Array Comparative Genomic Hybridization Is High Regardless of Severity of Intellectual Disability/Developmental Delay in Children.

PubMed

D'Arrigo, Stefano; Gavazzi, Francesco; Alfei, Enrico; Zuffardi, Orsetta; Montomoli, Cristina; Corso, Barbara; Buzzi, Erika; Sciacca, Francesca L; Bulgheroni, Sara; Riva, Daria; Pantaleoni, Chiara

2016-05-01

Microarray-based comparative genomic hybridization is a method of molecular analysis that identifies chromosomal anomalies (or copy number variants) that correlate with clinical phenotypes. The aim of the present study was to apply a clinical score previously designated by de Vries to 329 patients with intellectual disability/developmental disorder (intellectual disability/developmental delay) referred to our tertiary center and to see whether the clinical factors are associated with a positive outcome of aCGH analyses. Another goal was to test the association between a positive microarray-based comparative genomic hybridization result and the severity of intellectual disability/developmental delay. Microarray-based comparative genomic hybridization identified structural chromosomal alterations responsible for the intellectual disability/developmental delay phenotype in 16% of our sample. Our study showed that causative copy number variants are frequently found even in cases of mild intellectual disability (30.77%). We want to emphasize the need to conduct microarray-based comparative genomic hybridization on all individuals with intellectual disability/developmental delay, regardless of the severity, because the degree of intellectual disability/developmental delay does not predict the diagnostic yield of microarray-based comparative genomic hybridization. © The Author(s) 2015.

A preliminary result of three-dimensional microarray technology to gene analysis with endoscopic ultrasound-guided fine-needle aspiration specimens and pancreatic juices

PubMed Central

2010-01-01

Background Analysis of gene expression and gene mutation may add information to be different from ordinary pathological tissue diagnosis. Since samples obtained endoscopically are very small, it is desired that more sensitive technology is developed for gene analysis. We investigated whether gene expression and gene mutation analysis by newly developed ultra-sensitive three-dimensional (3D) microarray is possible using small amount samples from endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) specimens and pancreatic juices. Methods Small amount samples from 17 EUS-FNA specimens and 16 pancreatic juices were obtained. After nucleic acid extraction, the samples were amplified with labeling and analyzed by the 3D microarray. Results The analyzable rate with the microarray was 46% (6/13) in EUS-FNA specimens of RNAlater® storage, and RNA degradations were observed in all the samples of frozen storage. In pancreatic juices, the analyzable rate was 67% (4/6) in frozen storage samples and 20% (2/10) in RNAlater® storage. EUS-FNA specimens were classified into cancer and non-cancer by gene expression analysis and K-ras codon 12 mutations were also detected using the 3D microarray. Conclusions Gene analysis from small amount samples obtained endoscopically was possible by newly developed 3D microarray technology. High quality RNA from EUS-FNA samples were obtained and remained in good condition only using RNA stabilizer. In contrast, high quality RNA from pancreatic juice samples were obtained only in frozen storage without RNA stabilizer. PMID:20416107

Gene Expression Browser: Large-Scale and Cross-Experiment Microarray Data Management, Search & Visualization

USDA-ARS?s Scientific Manuscript database

The amount of microarray gene expression data in public repositories has been increasing exponentially for the last couple of decades. High-throughput microarray data integration and analysis has become a critical step in exploring the large amount of expression data for biological discovery. Howeve...

A pilot study of gene expression analysis in workers with hand-arm vibration syndrome.

PubMed

Maeda, Setsuo; Yu, Xiaozhong; Wang, Rui-Sheng; Sakakibara, Hisataka

2008-04-01

The purpose of this pilot study was to examine differences in gene expressions by cDNA microarray analysis of hand-arm vibration syndrome (HAVS) patients. Vein blood samples were collected and total RNA was extracted. All blood samples were obtained in the morning in one visit after a standard light breakfast. We performed microarray analysis with the labeled cDNA prepared by reverse transcription from RNA samples, using the Human CHIP version 1 (DNA Chip Research Inc, Yokohama, Japan). There are 2,976 genes on the chip, and these genes were selected from a cDNA library prepared with human peripheral white blood cells (WBC). Different gene levels between the HAVS patients and controls, and between groups of HAVS with different levels of symptoms, were indicated by the randomized variance model. The most up-regulated genes were analyzed for their possible functions and association with the occurrence of HAVS. From the results of this pilot study, although the results were obtained a limited number of subjects, it would appear that cDNA microarray analysis of HAVS patients has potential as a new objective method of HAVS diagnosis. Further research is needed to examine the gene expression with increased numbers of patients at different stages of HAVS.

Parallel, confocal, and complete spectrum imager for fluorescent detection of high-density microarray

NASA Astrophysics Data System (ADS)

Bogdanov, Valery L.; Boyce-Jacino, Michael

1999-05-01

Confined arrays of biochemical probes deposited on a solid support surface (analytical microarray or 'chip') provide an opportunity to analysis multiple reactions simultaneously. Microarrays are increasingly used in genetics, medicine and environment scanning as research and analytical instruments. A power of microarray technology comes from its parallelism which grows with array miniaturization, minimization of reagent volume per reaction site and reaction multiplexing. An optical detector of microarray signals should combine high sensitivity, spatial and spectral resolution. Additionally, low-cost and a high processing rate are needed to transfer microarray technology into biomedical practice. We designed an imager that provides confocal and complete spectrum detection of entire fluorescently-labeled microarray in parallel. Imager uses microlens array, non-slit spectral decomposer, and high- sensitive detector (cooled CCD). Two imaging channels provide a simultaneous detection of localization, integrated and spectral intensities for each reaction site in microarray. A dimensional matching between microarray and imager's optics eliminates all in moving parts in instrumentation, enabling highly informative, fast and low-cost microarray detection. We report theory of confocal hyperspectral imaging with microlenses array and experimental data for implementation of developed imager to detect fluorescently labeled microarray with a density approximately 103 sites per cm2.

Fiber-optic microarray for simultaneous detection of multiple harmful algal bloom species.

PubMed

Ahn, Soohyoun; Kulis, David M; Erdner, Deana L; Anderson, Donald M; Walt, David R

2006-09-01

Harmful algal blooms (HABs) are a serious threat to coastal resources, causing a variety of impacts on public health, regional economies, and ecosystems. Plankton analysis is a valuable component of many HAB monitoring and research programs, but the diversity of plankton poses a problem in discriminating toxic from nontoxic species using conventional detection methods. Here we describe a sensitive and specific sandwich hybridization assay that combines fiber-optic microarrays with oligonucleotide probes to detect and enumerate the HAB species Alexandrium fundyense, Alexandrium ostenfeldii, and Pseudo-nitzschia australis. Microarrays were prepared by loading oligonucleotide probe-coupled microspheres (diameter, 3 mum) onto the distal ends of chemically etched imaging fiber bundles. Hybridization of target rRNA from HAB cells to immobilized probes on the microspheres was visualized using Cy3-labeled secondary probes in a sandwich-type assay format. We applied these microarrays to the detection and enumeration of HAB cells in both cultured and field samples. Our study demonstrated a detection limit of approximately 5 cells for all three target organisms within 45 min, without a separate amplification step, in both sample types. We also developed a multiplexed microarray to detect the three HAB species simultaneously, which successfully detected the target organisms, alone and in combination, without cross-reactivity. Our study suggests that fiber-optic microarrays can be used for rapid and sensitive detection and potential enumeration of HAB species in the environment.

Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

PubMed Central

Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; Haaland, D. M.; Timlin, J. A.; Elbourne, L. D. H.; Palenik, B.; Paulsen, I. T.

2009-01-01

Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in part to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition. PMID:19404483

Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

DOE PAGES

Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; ...

2009-01-01

Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in partmore » to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition.« less

Multiplexed Analysis of Serum Breast and Ovarian Cancer Markers by Means of Suspension Bead-quantum Dot Microarrays

NASA Astrophysics Data System (ADS)

Brazhnik, Kristina; Sokolova, Zinaida; Baryshnikova, Maria; Bilan, Regina; Nabiev, Igor; Sukhanova, Alyona

Multiplexed analysis of cancer markers is crucial for early tumor diagnosis and screening. We have designed lab-on-a-bead microarray for quantitative detection of three breast cancer markers in human serum. Quantum dots were used as bead-bound fluorescent tags for identifying each marker by means of flow cytometry. Antigen-specific beads reliably detected CA 15-3, CEA, and CA 125 in serum samples, providing clear discrimination between the samples with respect to the antigen levels. The novel microarray is advantageous over the routine single-analyte ones due to the simultaneous detection of various markers. Therefore the developed microarray is a promising tool for serum tumor marker profiling.

An integrated bioinformatics approach to improve two-color microarray quality-control: impact on biological conclusions.

PubMed

van Haaften, Rachel I M; Luceri, Cristina; van Erk, Arie; Evelo, Chris T A

2009-06-01

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

Mothers' appreciation of chromosomal microarray analysis for autism spectrum disorder.

PubMed

Giarelli, Ellen; Reiff, Marian

2015-10-01

The aim of this study was to examine mothers' experiences with chromosomal microarray analysis (CMA) for a child with autism spectrum disorder (ASD). This is a descriptive qualitative study using thematic content analysis of in-depth interview with 48 mothers of children who had genetic testing for ASD. The principal theme, "something is missing," included missing knowledge about genetics, information on use of the results, explanations of the relevance to the diagnosis, and relevance to life-long care. Two subordinate themes were (a) disappreciation of the helpfulness of scientific information to explain the diagnosis, and (b) returning to personal experience for interpretation. The test "appreciated" in value when results could be linked to the phenotype. © 2015, Wiley Periodicals, Inc.

Microarray Meta-Analysis of RNA-Binding Protein Functions in Alternative Polyadenylation

PubMed Central

Hu, Wenchao; Liu, Yuting; Yan, Jun

2014-01-01

Alternative polyadenylation (APA) is a post-transcriptional mechanism to generate diverse mRNA transcripts with different 3′UTRs from the same gene. In this study, we systematically searched for the APA events with differential expression in public mouse microarray data. Hundreds of genes with over-represented differential APA events and the corresponding experiments were identified. We further revealed that global APA differential expression occurred prevalently in tissues such as brain comparing to peripheral tissues, and biological processes such as development, differentiation and immune responses. Interestingly, we also observed widespread differential APA events in RNA-binding protein (RBP) genes such as Rbm3, Eif4e2 and Elavl1. Given the fact that RBPs are considered as the main regulators of differential APA expression, we constructed a co-expression network between APAs and RBPs using the microarray data. Further incorporation of CLIP-seq data of selected RBPs showed that Nova2 represses and Mbnl1 promotes the polyadenylation of closest poly(A) sites respectively. Altogether, our study is the first microarray meta-analysis in a mammal on the regulation of APA by RBPs that integrated massive mRNA expression data under a wide-range of biological conditions. Finally, we present our results as a comprehensive resource in an online website for the research community. PMID:24622240

In vitro study of the effects of ELF electric fields on gene expression in human epidermal cells.

PubMed

Collard, Jean-Francois; Mertens, Benjamin; Hinsenkamp, Maurice

2011-01-01

An acceleration of differentiation, at the expense of proliferation, is observed after exposure of various biological models to low frequency and low amplitude electric and electromagnetic fields. Following these results showing significant modifications, we try to identify the biological mechanism involved at the cell level through microarray screening. For this study, we use epidermis cultures harvested from human abdominoplasty. Two platinum electrodes are used to apply the electric signal. The gene expressions of 38,500 well-characterized human genes are analyzed using Affymetrix(®) microarray U133 Plus 2.0 chips. The protocol is repeated on three different patients. After three periods of exposure, a total of 24 chips have been processed. After the application of ELF electric fields, the microarray analysis confirms a modification of the gene expression of epidermis cells. Particularly, four up-regulated genes (DKK1, TXNRD1, ATF3, and MME) and one down-regulated gene (MACF1) are involved in the regulation of proliferation and differentiation. Expression of these five genes was also confirmed by real-time rtPCR in all samples used for microarray analysis. These results corroborate an acceleration of cell differentiation at the expense of cell proliferation. © 2010 Wiley-Liss, Inc.

Development and Validation of Sandwich ELISA Microarrays with Minimal Assay Interference

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gonzalez, Rachel M.; Servoss, Shannon; Crowley, Sheila A.

Sandwich enzyme-linked immunosorbent assay (ELISA) microarrays are emerging as a strong candidate platform for multiplex biomarker analysis because of the ELISA’s ability to quantitatively measure rare proteins in complex biological fluids. Advantages of this platform are high-throughput potential, assay sensitivity and stringency, and the similarity to the standard ELISA test, which facilitates assay transfer from a research setting to a clinical laboratory. However, a major concern with the multiplexing of ELISAs is maintaining high assay specificity. In this study, we systematically determine the amount of assay interference and noise contributed by individual components of the multiplexed 24-assay system. We findmore » that non-specific reagent cross-reactivity problems are relatively rare. We did identify the presence of contaminant antigens in a “purified antigen”. We tested the validated ELISA microarray chip using paired serum samples that had been collected from four women at a 6-month interval. This analysis demonstrated that protein levels typically vary much more between individuals then within an individual over time, a result which suggests that longitudinal studies may be useful in controlling for biomarker variability across a population. Overall, this research demonstrates the importance of a stringent screening protocol and the value of optimizing the antibody and antigen concentrations when designing chips for ELISA microarrays.« less

Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes.

PubMed

Feng, Yinling; Wang, Xuefeng

2017-03-01

In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co‑expression networks and clinical information was adopted, using weighted gene co‑expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co‑pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution‑based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD‑associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.

Implementation of GenePattern within the Stanford Microarray Database.

PubMed

Hubble, Jeremy; Demeter, Janos; Jin, Heng; Mao, Maria; Nitzberg, Michael; Reddy, T B K; Wymore, Farrell; Zachariah, Zachariah K; Sherlock, Gavin; Ball, Catherine A

2009-01-01

Hundreds of researchers across the world use the Stanford Microarray Database (SMD; http://smd.stanford.edu/) to store, annotate, view, analyze and share microarray data. In addition to providing registered users at Stanford access to their own data, SMD also provides access to public data, and tools with which to analyze those data, to any public user anywhere in the world. Previously, the addition of new microarray data analysis tools to SMD has been limited by available engineering resources, and in addition, the existing suite of tools did not provide a simple way to design, execute and share analysis pipelines, or to document such pipelines for the purposes of publication. To address this, we have incorporated the GenePattern software package directly into SMD, providing access to many new analysis tools, as well as a plug-in architecture that allows users to directly integrate and share additional tools through SMD. In this article, we describe our implementation of the GenePattern microarray analysis software package into the SMD code base. This extension is available with the SMD source code that is fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD with an enriched data analysis capability.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. Aim The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Methods Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Results Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. Conclusion To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research. PMID:26080057

Fabrication of Carbohydrate Microarrays by Boronate Formation.

PubMed

Adak, Avijit K; Lin, Ting-Wei; Li, Ben-Yuan; Lin, Chun-Cheng

2017-01-01

The interactions between soluble carbohydrates and/or surface displayed glycans and protein receptors are essential to many biological processes and cellular recognition events. Carbohydrate microarrays provide opportunities for high-throughput quantitative analysis of carbohydrate-protein interactions. Over the past decade, various techniques have been implemented for immobilizing glycans on solid surfaces in a microarray format. Herein, we describe a detailed protocol for fabricating carbohydrate microarrays that capitalizes on the intrinsic reactivity of boronic acid toward carbohydrates to form stable boronate diesters. A large variety of unprotected carbohydrates ranging in structure from simple disaccharides and trisaccharides to considerably more complex human milk and blood group (oligo)saccharides have been covalently immobilized in a single step on glass slides, which were derivatized with high-affinity boronic acid ligands. The immobilized ligands in these microarrays maintain the receptor-binding activities including those of lectins and antibodies according to the structures of their pendant carbohydrates for rapid analysis of a number of carbohydrate-recognition events within 30 h. This method facilitates the direct construction of otherwise difficult to obtain carbohydrate microarrays from underivatized glycans.

Cloud-Scale Genomic Signals Processing for Robust Large-Scale Cancer Genomic Microarray Data Analysis.

PubMed

Harvey, Benjamin Simeon; Ji, Soo-Yeon

2017-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring forth oncological inference to the bioinformatics community through the analysis of large-scale cancer genomic (LSCG) DNA and mRNA microarray data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological interpretation by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale distributed parallel (CSDP) separable 1-D wavelet decomposition technique for denoising through differential expression thresholding and classification of LSCG microarray data. This research presents a novel methodology that utilizes a CSDP separable 1-D method for wavelet-based transformation in order to initialize a threshold which will retain significantly expressed genes through the denoising process for robust classification of cancer patients. Additionally, the overall study was implemented and encompassed within CSDP environment. The utilization of cloud computing and wavelet-based thresholding for denoising was used for the classification of samples within the Global Cancer Map, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas. The results proved that separable 1-D parallel distributed wavelet denoising in the cloud and differential expression thresholding increased the computational performance and enabled the generation of higher quality LSCG microarray datasets, which led to more accurate classification results.

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data

PubMed Central

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R.; del Río-Navarro, Blanca E.; Mendoza-Vargas, Alfredo; Sánchez, Filiberto

2017-01-01

Background In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. Methods We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6–10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). Results From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Discussion Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments. PMID:29230367

Cytokine-related genes and oxidation-related genes detected in preeclamptic placentas.

PubMed

Lee, Gui Se Ra; Joe, Yoon Seong; Kim, Sa Jin; Shin, Jong Chul

2010-10-01

To investigate cytokine- and oxidation-related genes for preeclampsia using DNA microarray analysis. Placentas were collected from 13 normal pregnancies and 13 patients with preeclampsia. Gene expression was studied using DNA microarray. Among significantly expressed genes, we focused on genes associated with cytokines and oxidation, and the results were confirmed using quantitative real time-polymerase chain reaction (QRT-PCR). 415 genes out of 30,940 genes were altered by > or =2-fold in the microarray analysis. 121 up-regulated genes and 294 down-regulated genes were found to be in preeclamptic placenta. Six cytokine-related genes and 5 oxidation-related genes were found from among the 121 up-regulated genes. The cytokine-related genes studied included oncostatin M (OSM), fms-related tyrosine kinase (FLT1) and vascular endothelial growth factor A (VEGFA), and the oxidation-related genes studied included spermine oxidase (SMOX), l cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1), acetate dehydrogenase A (LDHA). These six genes were also significantly higher in placentas from patients with preeclampsia than in those from women with normal pregnancies. The placental tissue of patients with preeclampsia showed significantly higher mRNA expression of these six genes than the normal group, using QRT-PCR. DNA microarray analysis is one of the great methods for simultaneously detecting the functionally associated genes of preeclampsia. The cytokine-related genes such as OSM, FLT1 and VEGFA, and the oxidation-related genes such as LDHA, CYP26A1 and SMOX might prove to be the starting point in the elucidation of the pathogenesis of preeclampsia.

LS-CAP: an algorithm for identifying cytogenetic aberrations in hepatocellular carcinoma using microarray data.

PubMed

He, Xianmin; Wei, Qing; Sun, Meiqian; Fu, Xuping; Fan, Sichang; Li, Yao

2006-05-01

Biological techniques such as Array-Comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH) and affymetrix single nucleotide pleomorphism (SNP) array have been used to detect cytogenetic aberrations. However, on genomic scale, these techniques are labor intensive and time consuming. Comparative genomic microarray analysis (CGMA) has been used to identify cytogenetic changes in hepatocellular carcinoma (HCC) using gene expression microarray data. However, CGMA algorithm can not give precise localization of aberrations, fails to identify small cytogenetic changes, and exhibits false negatives and positives. Locally un-weighted smoothing cytogenetic aberrations prediction (LS-CAP) based on local smoothing and binomial distribution can be expected to address these problems. LS-CAP algorithm was built and used on HCC microarray profiles. Eighteen cytogenetic abnormalities were identified, among them 5 were reported previously, and 12 were proven by CGH studies. LS-CAP effectively reduced the false negatives and positives, and precisely located small fragments with cytogenetic aberrations.

Microarray RNA expression analysis of cerebral white matter lesions reveals changes in multiple functional pathways.

PubMed

Simpson, Julie E; Hosny, Ola; Wharton, Stephen B; Heath, Paul R; Holden, Hazel; Fernando, Malee S; Matthews, Fiona; Forster, Gill; O'Brien, John T; Barber, Robert; Kalaria, Raj N; Brayne, Carol; Shaw, Pamela J; Lewis, Claire E; Ince, Paul G

2009-02-01

White matter lesions (WML) in brain aging are linked to dementia and depression. Ischemia contributes to their pathogenesis but other mechanisms may contribute. We used RNA microarray analysis with functional pathway grouping as an unbiased approach to investigate evidence for additional pathogenetic mechanisms. WML were identified by MRI and pathology in brains donated to the Medical Research Council Cognitive Function and Ageing Study Cognitive Function and Aging Study. RNA was extracted to compare WML with nonlesional white matter samples from cases with lesions (WM[L]), and from cases with no lesions (WM[C]) using RNA microarray and pathway analysis. Functional pathways were validated for selected genes by quantitative real-time polymerase chain reaction and immunocytochemistry. We identified 8 major pathways in which multiple genes showed altered RNA transcription (immune regulation, cell cycle, apoptosis, proteolysis, ion transport, cell structure, electron transport, metabolism) among 502 genes that were differentially expressed in WML compared to WM[C]. In WM[L], 409 genes were altered involving the same pathways. Genes selected to validate this microarray data all showed the expected changes in RNA levels and immunohistochemical expression of protein. WML represent areas with a complex molecular phenotype. From this and previous evidence, WML may arise through tissue ischemia but may also reflect the contribution of additional factors like blood-brain barrier dysfunction. Differential expression of genes in WM[L] compared to WM[C] indicate a "field effect" in the seemingly normal surrounding white matter.

EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments

PubMed Central

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-01-01

Background The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE3 was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. Results EDGE3 has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE3 is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Conclusion Here, we present EDGE3, an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE3 provides a means for managing RNA samples and arrays during the hybridization process. EDGE3 is freely available for download at . PMID:19732451

EDGE(3): a web-based solution for management and analysis of Agilent two color microarray experiments.

PubMed

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-09-04

The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. EDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Here, we present EDGE(3), an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE(3) provides a means for managing RNA samples and arrays during the hybridization process. EDGE(3) is freely available for download at http://edge.oncology.wisc.edu/.

Nutrient control of gene expression in Drosophila: microarray analysis of starvation and sugar-dependent response

PubMed Central

Zinke, Ingo; Schütz, Christina S.; Katzenberger, Jörg D.; Bauer, Matthias; Pankratz, Michael J.

2002-01-01

We have identified genes regulated by starvation and sugar signals in Drosophila larvae using whole-genome microarrays. Based on expression profiles in the two nutrient conditions, they were organized into different categories that reflect distinct physiological pathways mediating sugar and fat metabolism, and cell growth. In the category of genes regulated in sugar-fed, but not in starved, animals, there is an upregulation of genes encoding key enzymes of the fat biosynthesis pathway and a downregulation of genes encoding lipases. The highest and earliest activated gene upon sugar ingestion is sugarbabe, a zinc finger protein that is induced in the gut and the fat body. Identification of potential targets using microarrays suggests that sugarbabe functions to repress genes involved in dietary fat breakdown and absorption. The current analysis provides a basis for studying the genetic mechanisms underlying nutrient signalling. PMID:12426388

Finding Groups in Gene Expression Data

PubMed Central

2005-01-01

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups. PMID:16046827

Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline

PubMed Central

Rahmatallah, Yasir; Emmert-Streib, Frank

2016-01-01

Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret experimental results from microarrays, gene set analysis (GSA) has become the method of choice, in particular because it incorporates pre-existing biological knowledge (in a form of functionally related gene sets) into the analysis. Here we provide a brief review of several statistically different GSA approaches (competitive and self-contained) that can be adapted from microarrays practice as well as those specifically designed for RNA-seq. We evaluate their performance (in terms of Type I error rate, power, robustness to the sample size and heterogeneity, as well as the sensitivity to different types of selection biases) on simulated and real RNA-seq data. Not surprisingly, the performance of various GSA approaches depends only on the statistical hypothesis they test and does not depend on whether the test was developed for microarrays or RNA-seq data. Interestingly, we found that competitive methods have lower power as well as robustness to the samples heterogeneity than self-contained methods, leading to poor results reproducibility. We also found that the power of unsupervised competitive methods depends on the balance between up- and down-regulated genes in tested gene sets. These properties of competitive methods have been overlooked before. Our evaluation provides a concise guideline for selecting GSA approaches, best performing under particular experimental settings in the context of RNA-seq. PMID:26342128

Prenatal chromosomal microarray analysis in fetuses with congenital heart disease: a prospective cohort study.

PubMed

Wang, Yan; Cao, Li; Liang, Dong; Meng, Lulu; Wu, Yun; Qiao, Fengchang; Ji, Xiuqing; Luo, Chunyu; Zhang, Jingjing; Xu, Tianhui; Yu, Bin; Wang, Leilei; Wang, Ting; Pan, Qiong; Ma, Dingyuan; Hu, Ping; Xu, Zhengfeng

2018-02-01

Currently, chromosomal microarray analysis is considered the first-tier test in pediatric care and prenatal diagnosis. However, the diagnostic yield of chromosomal microarray analysis for prenatal diagnosis of congenital heart disease has not been evaluated based on a large cohort. Our aim was to evaluate the clinical utility of chromosomal microarray as the first-tier test for chromosomal abnormalities in fetuses with congenital heart disease. In this prospective study, 602 prenatal cases of congenital heart disease were investigated using single nucleotide polymorphism array over a 5-year period. Overall, pathogenic chromosomal abnormalities were identified in 125 (20.8%) of 602 prenatal cases of congenital heart disease, with 52.0% of them being numerical chromosomal abnormalities. The detection rates of likely pathogenic copy number variations and variants of uncertain significance were 1.3% and 6.0%, respectively. The detection rate of pathogenic chromosomal abnormalities in congenital heart disease plus additional structural anomalies (48.9% vs 14.3%, P < .0001) or intrauterine growth retardation group (50.0% vs 14.3%, P = .044) was significantly higher than that in isolated congenital heart disease group. Additionally, the detection rate in congenital heart disease with additional structural anomalies group was significantly higher than that in congenital heart disease with soft markers group (48.9% vs 19.8%, P < .0001). No significant difference was observed in the detection rates between congenital heart disease with additional structural anomalies and congenital heart disease with intrauterine growth retardation groups (48.9% vs 50.0%), congenital heart disease with soft markers and congenital heart disease with intrauterine growth retardation groups (19.8% vs 50.0%), or congenital heart disease with soft markers and isolated congenital heart disease groups (19.8% vs 14.3%). The detection rate in fetuses with congenital heart disease plus mild ventriculomegaly was significantly higher than in those with other types of soft markers (50.0% vs 15.6%, P < .05). Our study suggests chromosomal microarray analysis is a reliable and high-resolution technology and should be used as the first-tier test for prenatal diagnosis of congenital heart disease in clinical practice. Copyright © 2017 Elsevier Inc. All rights reserved.

Mining Microarray Data at NCBI’s Gene Expression Omnibus (GEO)*

PubMed Central

Barrett, Tanya; Edgar, Ron

2006-01-01

Summary The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo. PMID:16888359

Implementation of mutual information and bayes theorem for classification microarray data

NASA Astrophysics Data System (ADS)

Dwifebri Purbolaksono, Mahendra; Widiastuti, Kurnia C.; Syahrul Mubarok, Mohamad; Adiwijaya; Aminy Ma’ruf, Firda

2018-03-01

Microarray Technology is one of technology which able to read the structure of gen. The analysis is important for this technology. It is for deciding which attribute is more important than the others. Microarray technology is able to get cancer information to diagnose a person’s gen. Preparation of microarray data is a huge problem and takes a long time. That is because microarray data contains high number of insignificant and irrelevant attributes. So, it needs a method to reduce the dimension of microarray data without eliminating important information in every attribute. This research uses Mutual Information to reduce dimension. System is built with Machine Learning approach specifically Bayes Theorem. This theorem uses a statistical and probability approach. By combining both methods, it will be powerful for Microarray Data Classification. The experiment results show that system is good to classify Microarray data with highest F1-score using Bayesian Network by 91.06%, and Naïve Bayes by 88.85%.

Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

PubMed

Chatziioannou, Aristotelis; Moulos, Panagiotis; Kolisis, Fragiskos N

2009-10-27

The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Gene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.

Microarray data mining using Bioconductor packages.

PubMed

Nie, Haisheng; Neerincx, Pieter B T; van der Poel, Jan; Ferrari, Francesco; Bicciato, Silvio; Leunissen, Jack A M; Groenen, Martien A M

2009-07-16

This paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge. GO enrichment analysis identified significant (raw p-value < 0.05) GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 (q-value < 0.01). Overall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysis.

Polyadenylation state microarray (PASTA) analysis.

PubMed

Beilharz, Traude H; Preiss, Thomas

2011-01-01

Nearly all eukaryotic mRNAs terminate in a poly(A) tail that serves important roles in mRNA utilization. In the cytoplasm, the poly(A) tail promotes both mRNA stability and translation, and these functions are frequently regulated through changes in tail length. To identify the scope of poly(A) tail length control in a transcriptome, we developed the polyadenylation state microarray (PASTA) method. It involves the purification of mRNA based on poly(A) tail length using thermal elution from poly(U) sepharose, followed by microarray analysis of the resulting fractions. In this chapter we detail our PASTA approach and describe some methods for bulk and mRNA-specific poly(A) tail length measurements of use to monitor the procedure and independently verify the microarray data.

The Use of Atomic Force Microscopy for 3D Analysis of Nucleic Acid Hybridization on Microarrays.

PubMed

Dubrovin, E V; Presnova, G V; Rubtsova, M Yu; Egorov, A M; Grigorenko, V G; Yaminsky, I V

2015-01-01

Oligonucleotide microarrays are considered today to be one of the most efficient methods of gene diagnostics. The capability of atomic force microscopy (AFM) to characterize the three-dimensional morphology of single molecules on a surface allows one to use it as an effective tool for the 3D analysis of a microarray for the detection of nucleic acids. The high resolution of AFM offers ways to decrease the detection threshold of target DNA and increase the signal-to-noise ratio. In this work, we suggest an approach to the evaluation of the results of hybridization of gold nanoparticle-labeled nucleic acids on silicon microarrays based on an AFM analysis of the surface both in air and in liquid which takes into account of their three-dimensional structure. We suggest a quantitative measure of the hybridization results which is based on the fraction of the surface area occupied by the nanoparticles.

The Utility of Chromosomal Microarray Analysis in Developmental and Behavioral Pediatrics

ERIC Educational Resources Information Center

Beaudet, Arthur L.

2013-01-01

Chromosomal microarray analysis (CMA) has emerged as a powerful new tool to identify genomic abnormalities associated with a wide range of developmental disabilities including congenital malformations, cognitive impairment, and behavioral abnormalities. CMA includes array comparative genomic hybridization (CGH) and single nucleotide polymorphism…

Evaluation of chronic lymphocytic leukemia by oligonucleotide-based microarray analysis uncovers novel aberrations not detected by FISH or cytogenetic analysis

PubMed Central

2011-01-01

Background Cytogenetic evaluation is a key component of the diagnosis and prognosis of chronic lymphocytic leukemia (CLL). We performed oligonucleotide-based comparative genomic hybridization microarray analysis on 34 samples with CLL and known abnormal karyotypes previously determined by cytogenetics and/or fluorescence in situ hybridization (FISH). Results Using a custom designed microarray that targets >1800 genes involved in hematologic disease and other malignancies, we identified additional cryptic aberrations and novel findings in 59% of cases. These included gains and losses of genes associated with cell cycle regulation, apoptosis and susceptibility loci on 3p21.31, 5q35.2q35.3, 10q23.31q23.33, 11q22.3, and 22q11.23. Conclusions Our results show that microarray analysis will detect known aberrations, including microscopic and cryptic alterations. In addition, novel genomic changes will be uncovered that may become important prognostic predictors or treatment targets for CLL in the future. PMID:22087757

Customizing microarrays for neuroscience drug discovery.

PubMed

Girgenti, Matthew J; Newton, Samuel S

2007-08-01

Microarray-based gene profiling has become the centerpiece of gene expression studies in the biological sciences. The ability to now interrogate the entire genome using a single chip demonstrates the progress in technology and instrumentation that has been made over the last two decades. Although this unbiased approach provides researchers with an immense quantity of data, obtaining meaningful insight is not possible without intensive data analysis and processing. Custom developed arrays have emerged as a viable and attractive alternative that can take advantage of this robust technology and tailor it to suit the needs and requirements of individual investigations. The ability to simplify data analysis, reduce noise and carefully optimize experimental conditions makes it a suitable tool that can be effectively utilized in neuroscience drug discovery efforts. Furthermore, incorporating recent advancements in fine focusing gene profiling to include specific cellular phenotypes can help resolve the complex cellular heterogeneity of the brain. This review surveys the use of microarray technology in neuroscience paying special attention to customized arrays and their potential in drug discovery. Novel applications of microarrays and ancillary techniques, such as laser microdissection, FAC sorting and RNA amplification, have also been discussed. The notion that a hypothesis-driven approach can be integrated into drug development programs is highlighted.

Methodological Challenges in Protein Microarray and Immunohistochemistry for the Discovery of Novel Autoantibodies in Paediatric Acute Disseminated Encephalomyelitis

PubMed Central

Peschl, Patrick; Ramberger, Melanie; Höftberger, Romana; Jöhrer, Karin; Baumann, Matthias; Rostásy, Kevin; Reindl, Markus

2017-01-01

Acute disseminated encephalomyelitis (ADEM) is a rare autoimmune-mediated demyelinating disease affecting mainly children and young adults. Differentiation to multiple sclerosis is not always possible, due to overlapping clinical symptoms and recurrent and multiphasic forms. Until now, immunoglobulins reactive to myelin oligodendrocyte glycoprotein (MOG antibodies) have been found in a subset of patients with ADEM. However, there are still patients lacking autoantibodies, necessitating the identification of new autoantibodies as biomarkers in those patients. Therefore, we aimed to identify novel autoantibody targets in ADEM patients. Sixteen ADEM patients (11 seronegative, 5 seropositive for MOG antibodies) were analysed for potential new biomarkers, using a protein microarray and immunohistochemistry on rat brain tissue to identify antibodies against intracellular and surface neuronal and glial antigens. Nine candidate antigens were identified in the protein microarray analysis in at least two patients per group. Immunohistochemistry on rat brain tissue did not reveal new target antigens. Although no new autoantibody targets could be found here, future studies should aim to identify new biomarkers for therapeutic and prognostic purposes. The microarray analysis and immunohistochemistry methods used here have several limitations, which should be considered in future searches for biomarkers. PMID:28327523

[Preparation of the cDNA microarray on the differential expressed cDNA of senescence-accelerated mouse's hippocampus].

PubMed

Cheng, Xiao-Rui; Zhou, Wen-Xia; Zhang, Yong-Xiang

2006-05-01

Alzheimer' s disease (AD) is the most common form of dementia in the elderly. AD is an invariably fatal neurodegenerative disorder with no effective treatment. Senescence-accelerated mouse prone 8 (SAMP8) is a model for studying age-related cognitive impairments and also is a good model to study brain aging and one of mouse model of AD. The technique of cDNA microarray can monitor the expression levels of thousands of genes simultaneously and can be used to study AD with the character of multi-mechanism, multi-targets and multi-pathway. In order to disclose the mechanism of AD and find the drug targets of AD, cDNA microarray containing 3136 cDNAs amplified from the suppression subtracted cDNA library of hippocampus of SAMP8 and SAMR1 was prepared with 16 blocks and 14 x 14 pins, the housekeeping gene beta-actin and G3PDH as inner conference. The background of this microarray was low and unanimous, and dots divided evenly. The conditions of hybridization and washing were optimized during the hybridization of probe and target molecule. After the data of hybridization analysis, the differential expressed cDNAs were sequenced and analyzed by the bioinformatics, and some of genes were quantified by the real time RT-PCR and the reliability of this cDNA microarray were validated. This cDNA microarray may be the good means to select the differential expressed genes and disclose the molecular mechanism of SAMP8's brain aging and AD.

Gene Expression Profiling of Gastric Cancer

PubMed Central

Marimuthu, Arivusudar; Jacob, Harrys K.C.; Jakharia, Aniruddha; Subbannayya, Yashwanth; Keerthikumar, Shivakumar; Kashyap, Manoj Kumar; Goel, Renu; Balakrishnan, Lavanya; Dwivedi, Sutopa; Pathare, Swapnali; Dikshit, Jyoti Bajpai; Maharudraiah, Jagadeesha; Singh, Sujay; Sameer Kumar, Ghantasala S; Vijayakumar, M.; Veerendra Kumar, Kariyanakatte Veeraiah; Premalatha, Chennagiri Shrinivasamurthy; Tata, Pramila; Hariharan, Ramesh; Roa, Juan Carlos; Prasad, T.S.K; Chaerkady, Raghothama; Kumar, Rekha Vijay; Pandey, Akhilesh

2015-01-01

Gastric cancer is the second leading cause of cancer death worldwide, both in men and women. A genomewide gene expression analysis was carried out to identify differentially expressed genes in gastric adenocarcinoma tissues as compared to adjacent normal tissues. We used Agilent’s whole human genome oligonucleotide microarray platform representing ~41,000 genes to carry out gene expression analysis. Two-color microarray analysis was employed to directly compare the expression of genes between tumor and normal tissues. Through this approach, we identified several previously known candidate genes along with a number of novel candidate genes in gastric cancer. Testican-1 (SPOCK1) was one of the novel molecules that was 10-fold upregulated in tumors. Using tissue microarrays, we validated the expression of testican-1 by immunohistochemical staining. It was overexpressed in 56% (160/282) of the cases tested. Pathway analysis led to the identification of several networks in which SPOCK1 was among the topmost networks of interacting genes. By gene enrichment analysis, we identified several genes involved in cell adhesion and cell proliferation to be significantly upregulated while those corresponding to metabolic pathways were significantly downregulated. The differentially expressed genes identified in this study are candidate biomarkers for gastric adenoacarcinoma. PMID:27030788

Development of the first oligonucleotide microarray for global gene expression profiling in guinea pigs: defining the transcription signature of infectious diseases.

PubMed

Jain, Ruchi; Dey, Bappaditya; Tyagi, Anil K

2012-10-02

The Guinea pig (Cavia porcellus) is one of the most extensively used animal models to study infectious diseases. However, despite its tremendous contribution towards understanding the establishment, progression and control of a number of diseases in general and tuberculosis in particular, the lack of fully annotated guinea pig genome sequence as well as appropriate molecular reagents has severely hampered detailed genetic and immunological analysis in this animal model. By employing the cross-species hybridization technique, we have developed an oligonucleotide microarray with 44,000 features assembled from different mammalian species, which to the best of our knowledge is the first attempt to employ microarray to study the global gene expression profile in guinea pigs. To validate and demonstrate the merit of this microarray, we have studied, as an example, the expression profile of guinea pig lungs during the advanced phase of M. tuberculosis infection. A significant upregulation of 1344 genes and a marked down regulation of 1856 genes in the lungs identified a disease signature of pulmonary tuberculosis infection. We report the development of first comprehensive microarray for studying the global gene expression profile in guinea pigs and validation of its usefulness with tuberculosis as a case study. An important gap in the area of infectious diseases has been addressed and a valuable molecular tool is provided to optimally harness the potential of guinea pig model to develop better vaccines and therapies against human diseases.

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment.

PubMed

Severgnini, Marco; Bicciato, Silvio; Mangano, Eleonora; Scarlatti, Francesca; Mezzelani, Alessandra; Mattioli, Michela; Ghidoni, Riccardo; Peano, Clelia; Bonnal, Raoul; Viti, Federica; Milanesi, Luciano; De Bellis, Gianluca; Battaglia, Cristina

2006-06-01

Meta-analysis of microarray data is increasingly important, considering both the availability of multiple platforms using disparate technologies and the accumulation in public repositories of data sets from different laboratories. We addressed the issue of comparing gene expression profiles from two microarray platforms by devising a standardized investigative strategy. We tested this procedure by studying MDA-MB-231 cells, which undergo apoptosis on treatment with resveratrol. Gene expression profiles were obtained using high-density, short-oligonucleotide, single-color microarray platforms: GeneChip (Affymetrix) and CodeLink (Amersham). Interplatform analyses were carried out on 8414 common transcripts represented on both platforms, as identified by LocusLink ID, representing 70.8% and 88.6% of annotated GeneChip and CodeLink features, respectively. We identified 105 differentially expressed genes (DEGs) on CodeLink and 42 DEGs on GeneChip. Among them, only 9 DEGs were commonly identified by both platforms. Multiple analyses (BLAST alignment of probes with target sequences, gene ontology, literature mining, and quantitative real-time PCR) permitted us to investigate the factors contributing to the generation of platform-dependent results in single-color microarray experiments. An effective approach to cross-platform comparison involves microarrays of similar technologies, samples prepared by identical methods, and a standardized battery of bioinformatic and statistical analyses.

Interim report on updated microarray probes for the LLNL Burkholderia pseudomallei SNP array

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gardner, S; Jaing, C

2012-03-27

The overall goal of this project is to forensically characterize 100 unknown Burkholderia isolates in the US-Australia collaboration. We will identify genome-wide single nucleotide polymorphisms (SNPs) from B. pseudomallei and near neighbor species including B. mallei, B. thailandensis and B. oklahomensis. We will design microarray probes to detect these SNP markers and analyze 100 Burkholderia genomic DNAs extracted from environmental, clinical and near neighbor isolates from Australian collaborators on the Burkholderia SNP microarray. We will analyze the microarray genotyping results to characterize the genetic diversity of these new isolates and triage the samples for whole genome sequencing. In this interimmore » report, we described the SNP analysis and the microarray probe design for the Burkholderia SNP microarray.« less

Porous Silicon Antibody Microarrays for Quantitative Analysis: Measurement of Free and Total PSA in Clinical Plasma Samples

PubMed Central

Tojo, Axel; Malm, Johan; Marko-Varga, György; Lilja, Hans; Laurell, Thomas

2014-01-01

The antibody microarrays have become widespread, but their use for quantitative analyses in clinical samples has not yet been established. We investigated an immunoassay based on nanoporous silicon antibody microarrays for quantification of total prostate-specific-antigen (PSA) in 80 clinical plasma samples, and provide quantitative data from a duplex microarray assay that simultaneously quantifies free and total PSA in plasma. To further develop the assay the porous silicon chips was placed into a standard 96-well microtiter plate for higher throughput analysis. The samples analyzed by this quantitative microarray were 80 plasma samples obtained from men undergoing clinical PSA testing (dynamic range: 0.14-44ng/ml, LOD: 0.14ng/ml). The second dataset, measuring free PSA (dynamic range: 0.40-74.9ng/ml, LOD: 0.47ng/ml) and total PSA (dynamic range: 0.87-295ng/ml, LOD: 0.76ng/ml), was also obtained from the clinical routine. The reference for the quantification was a commercially available assay, the ProStatus PSA Free/Total DELFIA. In an analysis of 80 plasma samples the microarray platform performs well across the range of total PSA levels. This assay might have the potential to substitute for the large-scale microtiter plate format in diagnostic applications. The duplex assay paves the way for a future quantitative multiplex assay, which analyses several prostate cancer biomarkers simultaneously. PMID:22921878

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

PubMed Central

Nookaew, Intawat; Papini, Marta; Pornputtapong, Natapol; Scalcinati, Gionata; Fagerberg, Linn; Uhlén, Matthias; Nielsen, Jens

2012-01-01

RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. As a case study for our work we, used the Saccharomyces cerevisiae strain CEN.PK 113-7D, grown under two different conditions (batch and chemostat). Here, we asses the influence of genetic variation on the estimation of gene expression level using three different aligners for read-mapping (Gsnap, Stampy and TopHat) on S288c genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and we explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach. High reproducibility among biological replicates (correlation ≥0.99) and high consistency between the two platforms for analysis of gene expression levels (correlation ≥0.91) are reported. The results from differential gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays) for gene expression analysis and addresses the contribution of the different steps involved in the analysis of RNA-seq data. PMID:22965124

High throughput gene expression profiling: a molecular approach to integrative physiology

PubMed Central

Liang, Mingyu; Cowley, Allen W; Greene, Andrew S

2004-01-01

Integrative physiology emphasizes the importance of understanding multiple pathways with overlapping, complementary, or opposing effects and their interactions in the context of intact organisms. The DNA microarray technology, the most commonly used method for high-throughput gene expression profiling, has been touted as an integrative tool that provides insights into regulatory pathways. However, the physiology community has been slow in acceptance of these techniques because of early failure in generating useful data and the lack of a cohesive theoretical framework in which experiments can be analysed. With recent advances in both technology and analysis, we propose a concept of multidimensional integration of physiology that incorporates data generated by DNA microarray and other functional, genomic, and proteomic approaches to achieve a truly integrative understanding of physiology. Analysis of several studies performed in simpler organisms or in mammalian model animals supports the feasibility of such multidimensional integration and demonstrates the power of DNA microarray as an indispensable molecular tool for such integration. Evaluation of DNA microarray techniques indicates that these techniques, despite limitations, have advanced to a point where the question-driven profiling research has become a feasible complement to the conventional, hypothesis-driven research. With a keen sense of homeostasis, global regulation, and quantitative analysis, integrative physiologists are uniquely positioned to apply these techniques to enhance the understanding of complex physiological functions. PMID:14678487

Derivation of Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Teleost Reproductive Axis.

EPA Science Inventory

Oligonucleotide microarrays are a powerful tool for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-based analyses to detect diffe...

Microarray-based screening of heat shock protein inhibitors.

PubMed

Schax, Emilia; Walter, Johanna-Gabriela; Märzhäuser, Helene; Stahl, Frank; Scheper, Thomas; Agard, David A; Eichner, Simone; Kirschning, Andreas; Zeilinger, Carsten

2014-06-20

Based on the importance of heat shock proteins (HSPs) in diseases such as cancer, Alzheimer's disease or malaria, inhibitors of these chaperons are needed. Today's state-of-the-art techniques to identify HSP inhibitors are performed in microplate format, requiring large amounts of proteins and potential inhibitors. In contrast, we have developed a miniaturized protein microarray-based assay to identify novel inhibitors, allowing analysis with 300 pmol of protein. The assay is based on competitive binding of fluorescence-labeled ATP and potential inhibitors to the ATP-binding site of HSP. Therefore, the developed microarray enables the parallel analysis of different ATP-binding proteins on a single microarray. We have demonstrated the possibility of multiplexing by immobilizing full-length human HSP90α and HtpG of Helicobacter pylori on microarrays. Fluorescence-labeled ATP was competed by novel geldanamycin/reblastatin derivatives with IC50 values in the range of 0.5 nM to 4 μM and Z(*)-factors between 0.60 and 0.96. Our results demonstrate the potential of a target-oriented multiplexed protein microarray to identify novel inhibitors for different members of the HSP90 family. Copyright © 2014 Elsevier B.V. All rights reserved.

Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data

PubMed Central

Watson, Christopher M.; Crinnion, Laura A.; Gurgel‐Gianetti, Juliana; Harrison, Sally M.; Daly, Catherine; Antanavicuite, Agne; Lascelles, Carolina; Markham, Alexander F.; Pena, Sergio D. J.; Bonthron, David T.

2015-01-01

ABSTRACT Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution. PMID:26037133

Thermodynamically optimal whole-genome tiling microarray design and validation.

PubMed

Cho, Hyejin; Chou, Hui-Hsien

2016-06-13

Microarray is an efficient apparatus to interrogate the whole transcriptome of species. Microarray can be designed according to annotated gene sets, but the resulted microarrays cannot be used to identify novel transcripts and this design method is not applicable to unannotated species. Alternatively, a whole-genome tiling microarray can be designed using only genomic sequences without gene annotations, and it can be used to detect novel RNA transcripts as well as known genes. The difficulty with tiling microarray design lies in the tradeoff between probe-specificity and coverage of the genome. Sequence comparison methods based on BLAST or similar software are commonly employed in microarray design, but they cannot precisely determine the subtle thermodynamic competition between probe targets and partially matched probe nontargets during hybridizations. Using the whole-genome thermodynamic analysis software PICKY to design tiling microarrays, we can achieve maximum whole-genome coverage allowable under the thermodynamic constraints of each target genome. The resulted tiling microarrays are thermodynamically optimal in the sense that all selected probes share the same melting temperature separation range between their targets and closest nontargets, and no additional probes can be added without violating the specificity of the microarray to the target genome. This new design method was used to create two whole-genome tiling microarrays for Escherichia coli MG1655 and Agrobacterium tumefaciens C58 and the experiment results validated the design.

Visible red and infrared light alters gene expression in human marrow stromal fibroblast cells.

PubMed

Guo, J; Wang, Q; Wai, D; Zhang, Q Z; Shi, S H; Le, A D; Shi, S T; Yen, S L-K

2015-04-01

This study tested whether or not gene expression in human marrow stromal fibroblast (MSF) cells depends on light wavelength and energy density. Primary cultures of isolated human bone marrow stem cells (hBMSC) were exposed to visible red (VR, 633 nm) and infrared (IR, 830 nm) radiation wavelengths from a light emitting diode (LED) over a range of energy densities (0.5, 1.0, 1.5, and 2.0 Joules/cm2) Cultured cells were assayed for cell proliferation, osteogenic potential, adipogenesis, mRNA and protein content. mRNA was analyzed by microarray and compared among different wavelengths and energy densities. Mesenchymal and epithelial cell responses were compared to determine whether responses were cell type specific. Protein array analysis was used to further analyze key pathways identified by microarrays. Different wavelengths and energy densities produced unique sets of genes identified by microarray analysis. Pathway analysis pointed to TGF-beta 1 in the visible red and Akt 1 in the infrared wavelengths as key pathways to study. TGF-beta protein arrays suggested switching from canonical to non-canonical TGF-beta pathways with increases to longer IR wavelengths. Microarrays suggest RANKL and MMP 10 followed IR energy density dose-response curves. Epithelial and mesenchymal cells respond differently to stimulation by light suggesting cell type-specific response is possible. These studies demonstrate differential gene expression with different wavelengths, energy densities and cell types. These differences in gene expression have the potential to be exploited for therapeutic purposes and can help explain contradictory results in the literature when wavelengths, energy densities and cell types differ. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comparing microarrays and next-generation sequencing technologies for microbial ecology research.

PubMed

Roh, Seong Woon; Abell, Guy C J; Kim, Kyoung-Ho; Nam, Young-Do; Bae, Jin-Woo

2010-06-01

Recent advances in molecular biology have resulted in the application of DNA microarrays and next-generation sequencing (NGS) technologies to the field of microbial ecology. This review aims to examine the strengths and weaknesses of each of the methodologies, including depth and ease of analysis, throughput and cost-effectiveness. It also intends to highlight the optimal application of each of the individual technologies toward the study of a particular environment and identify potential synergies between the two main technologies, whereby both sample number and coverage can be maximized. We suggest that the efficient use of microarray and NGS technologies will allow researchers to advance the field of microbial ecology, and importantly, improve our understanding of the role of microorganisms in their various environments.

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling

PubMed Central

Fang, H; Tong, W; Perkins, R; Shi, L; Hong, H; Cao, X; Xie, Q; Yim, SH; Ward, JM; Pitot, HC; Dragan, YP

2005-01-01

Background The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer. Results In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism. Conclusion The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation. PMID:16026603

Workflows for microarray data processing in the Kepler environment.

PubMed

Stropp, Thomas; McPhillips, Timothy; Ludäscher, Bertram; Bieda, Mark

2012-05-17

Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or R/BioConductor scripting approaches to pipeline design. Finally, we suggest that microarray data processing task workflows may provide a basis for future example-based comparison of different workflow systems. We provide a set of tools and complete workflows for microarray data analysis in the Kepler environment, which has the advantages of offering graphical, clear display of conceptual steps and parameters and the ability to easily integrate other resources such as remote data and web services.

Microarray-based DNA methylation study of Ewing's sarcoma of the bone.

PubMed

Park, Hye-Rim; Jung, Woon-Won; Kim, Hyun-Sook; Park, Yong-Koo

2014-10-01

Alterations in DNA methylation patterns are a hallmark of malignancy. However, the majority of epigenetic studies of Ewing's sarcoma have focused on the analysis of only a few candidate genes. Comprehensive studies are thus lacking and are required. The aim of the present study was to identify novel methylation markers in Ewing's sarcoma using microarray analysis. The current study reports the microarray-based DNA methylation study of 1,505 CpG sites of 807 cancer-related genes from 69 Ewing's sarcoma samples. The Illumina GoldenGate Methylation Cancer Panel I microarray was used, and with the appropriate controls (n=14), a total of 92 hypermethylated genes were identified in the Ewing's sarcoma samples. The majority of the hypermethylated genes were associated with cell adhesion, cell regulation, development and signal transduction. The overall methylation mean values were compared between patients who survived and those that did not. The overall methylation mean was significantly higher in the patients who did not survive (0.25±0.03) than in those who did (0.22±0.05) (P=0.0322). However, the overall methylation mean was not found to significantly correlate with age, gender or tumor location. GDF10 , OSM , APC and HOXA11 were the most significant differentially-methylated genes, however, their methylation levels were not found to significantly correlate with the survival rate. The DNA methylation profile of Ewing's sarcoma was characterized and 92 genes that were significantly hypermethylated were detected. A trend towards a more aggressive behavior was identified in the methylated group. The results of this study indicated that methylation may be significant in the development of Ewing's sarcoma.

Microarray-based DNA methylation study of Ewing’s sarcoma of the bone

PubMed Central

PARK, HYE-RIM; JUNG, WOON-WON; KIM, HYUN-SOOK; PARK, YONG-KOO

2014-01-01

Alterations in DNA methylation patterns are a hallmark of malignancy. However, the majority of epigenetic studies of Ewing’s sarcoma have focused on the analysis of only a few candidate genes. Comprehensive studies are thus lacking and are required. The aim of the present study was to identify novel methylation markers in Ewing’s sarcoma using microarray analysis. The current study reports the microarray-based DNA methylation study of 1,505 CpG sites of 807 cancer-related genes from 69 Ewing’s sarcoma samples. The Illumina GoldenGate Methylation Cancer Panel I microarray was used, and with the appropriate controls (n=14), a total of 92 hypermethylated genes were identified in the Ewing’s sarcoma samples. The majority of the hypermethylated genes were associated with cell adhesion, cell regulation, development and signal transduction. The overall methylation mean values were compared between patients who survived and those that did not. The overall methylation mean was significantly higher in the patients who did not survive (0.25±0.03) than in those who did (0.22±0.05) (P=0.0322). However, the overall methylation mean was not found to significantly correlate with age, gender or tumor location. GDF10, OSM, APC and HOXA11 were the most significant differentially-methylated genes, however, their methylation levels were not found to significantly correlate with the survival rate. The DNA methylation profile of Ewing’s sarcoma was characterized and 92 genes that were significantly hypermethylated were detected. A trend towards a more aggressive behavior was identified in the methylated group. The results of this study indicated that methylation may be significant in the development of Ewing’s sarcoma. PMID:25202378

Variation of gene expression in Bacillus subtilis samples of fermentation replicates.

PubMed

Zhou, Ying; Yu, Wen-Bang; Ye, Bang-Ce

2011-06-01

The application of comprehensive gene expression profiling technologies to compare wild and mutated microorganism samples or to assess molecular differences between various treatments has been widely used. However, little is known about the normal variation of gene expression in microorganisms. In this study, an Agilent customized microarray representing 4,106 genes was used to quantify transcript levels of five-repeated flasks to assess normal variation in Bacillus subtilis gene expression. CV analysis and analysis of variance were employed to investigate the normal variance of genes and the components of variance, respectively. The results showed that above 80% of the total variation was caused by biological variance. For the 12 replicates, 451 of 4,106 genes exhibited variance with CV values over 10%. The functional category enrichment analysis demonstrated that these variable genes were mainly involved in cell type differentiation, cell type localization, cell cycle and DNA processing, and spore or cyst coat. Using power analysis, the minimal biological replicate number for a B. subtilis microarray experiment was determined to be six. The results contribute to the definition of the baseline level of variability in B. subtilis gene expression and emphasize the importance of replicate microarray experiments.

Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages.

PubMed

Taminau, Jonatan; Meganck, Stijn; Lazar, Cosmin; Steenhoff, David; Coletta, Alain; Molter, Colin; Duque, Robin; de Schaetzen, Virginie; Weiss Solís, David Y; Bersini, Hugues; Nowé, Ann

2012-12-24

With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/].

CNV-ROC: A cost effective, computer-aided analytical performance evaluator of chromosomal microarrays

PubMed Central

Goodman, Corey W.; Major, Heather J.; Walls, William D.; Sheffield, Val C.; Casavant, Thomas L.; Darbro, Benjamin W.

2016-01-01

Chromosomal microarrays (CMAs) are routinely used in both research and clinical laboratories; yet, little attention has been given to the estimation of genome-wide true and false negatives during the assessment of these assays and how such information could be used to calibrate various algorithmic metrics to improve performance. Low-throughput, locus-specific methods such as fluorescence in situ hybridization (FISH), quantitative PCR (qPCR), or multiplex ligation-dependent probe amplification (MLPA) preclude rigorous calibration of various metrics used by copy number variant (CNV) detection algorithms. To aid this task, we have established a comparative methodology, CNV-ROC, which is capable of performing a high throughput, low cost, analysis of CMAs that takes into consideration genome-wide true and false negatives. CNV-ROC uses a higher resolution microarray to confirm calls from a lower resolution microarray and provides for a true measure of genome-wide performance metrics at the resolution offered by microarray testing. CNV-ROC also provides for a very precise comparison of CNV calls between two microarray platforms without the need to establish an arbitrary degree of overlap. Comparison of CNVs across microarrays is done on a per-probe basis and receiver operator characteristic (ROC) analysis is used to calibrate algorithmic metrics, such as log2 ratio threshold, to enhance CNV calling performance. CNV-ROC addresses a critical and consistently overlooked aspect of analytical assessments of genome-wide techniques like CMAs which is the measurement and use of genome-wide true and false negative data for the calculation of performance metrics and comparison of CNV profiles between different microarray experiments. PMID:25595567

Optimized Probe Masking for Comparative Transcriptomics of Closely Related Species

PubMed Central

Poeschl, Yvonne; Delker, Carolin; Trenner, Jana; Ullrich, Kristian Karsten; Quint, Marcel; Grosse, Ivo

2013-01-01

Microarrays are commonly applied to study the transcriptome of specific species. However, many available microarrays are restricted to model organisms, and the design of custom microarrays for other species is often not feasible. Hence, transcriptomics approaches of non-model organisms as well as comparative transcriptomics studies among two or more species often make use of cost-intensive RNAseq studies or, alternatively, by hybridizing transcripts of a query species to a microarray of a closely related species. When analyzing these cross-species microarray expression data, differences in the transcriptome of the query species can cause problems, such as the following: (i) lower hybridization accuracy of probes due to mismatches or deletions, (ii) probes binding multiple transcripts of different genes, and (iii) probes binding transcripts of non-orthologous genes. So far, methods for (i) exist, but these neglect (ii) and (iii). Here, we propose an approach for comparative transcriptomics addressing problems (i) to (iii), which retains only transcript-specific probes binding transcripts of orthologous genes. We apply this approach to an Arabidopsis lyrata expression data set measured on a microarray designed for Arabidopsis thaliana, and compare it to two alternative approaches, a sequence-based approach and a genomic DNA hybridization-based approach. We investigate the number of retained probe sets, and we validate the resulting expression responses by qRT-PCR. We find that the proposed approach combines the benefit of sequence-based stringency and accuracy while allowing the expression analysis of much more genes than the alternative sequence-based approach. As an added benefit, the proposed approach requires probes to detect transcripts of orthologous genes only, which provides a superior base for biological interpretation of the measured expression responses. PMID:24260119

MICROARRAY DATA ANALYSIS USING MULTIPLE STATISTICAL MODELS

EPA Science Inventory

Microarray Data Analysis Using Multiple Statistical Models

Wenjun Bao1, Judith E. Schmid1, Amber K. Goetz1, Ming Ouyang2, William J. Welsh2,Andrew I. Brooks3,4, ChiYi Chu3,Mitsunori Ogihara3,4, Yinhe Cheng5, David J. Dix1. 1National Health and Environmental Effects Researc...

Parents' Perceptions of the Usefulness of Chromosomal Microarray Analysis for Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Reiff, Marian; Giarelli, Ellen; Bernhardt, Barbara A.; Easley, Ebony; Spinner, Nancy B.; Sankar, Pamela L.; Mulchandani, Surabhi

2015-01-01

Clinical guidelines recommend chromosomal microarray analysis (CMA) for all children with autism spectrum disorders (ASDs). We explored the test's perceived usefulness among parents of children with ASD who had undergone CMA, and received a result categorized as pathogenic, variant of uncertain significance, or negative. Fifty-seven parents…

A Graphical Systems Model and Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Female Teleost Reproductive Axis

EPA Science Inventory

Oligonucleotide microarrays and other ‘omics’ approaches are powerful tools for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-b...

CGO: utilizing and integrating gene expression microarray data in clinical research and data management.

PubMed

Bumm, Klaus; Zheng, Mingzhong; Bailey, Clyde; Zhan, Fenghuang; Chiriva-Internati, M; Eddlemon, Paul; Terry, Julian; Barlogie, Bart; Shaughnessy, John D

2002-02-01

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.

cluML: A markup language for clustering and cluster validity assessment of microarray data.

PubMed

Bolshakova, Nadia; Cunningham, Pádraig

2005-01-01

cluML is a new markup language for microarray data clustering and cluster validity assessment. The XML-based format has been designed to address some of the limitations observed in traditional formats, such as inability to store multiple clustering (including biclustering) and validation results within a dataset. cluML is an effective tool to support biomedical knowledge representation in gene expression data analysis. Although cluML was developed for DNA microarray analysis applications, it can be effectively used for the representation of clustering and for the validation of other biomedical and physical data that has no limitations.

Direct profiling of environmental microbial populations by thermal dissociation analysis of native rRNAs hybridized to oligonucleotide microarrays

NASA Technical Reports Server (NTRS)

El Fantroussi, Said; Urakawa, Hidetoshi; Bernhard, Anne E.; Kelly, John J.; Noble, Peter A.; Smidt, H.; Yershov, G. M.; Stahl, David A.

2003-01-01

Oligonucleotide microarrays were used to profile directly extracted rRNA from environmental microbial populations without PCR amplification. In our initial inspection of two distinct estuarine study sites, the hybridization patterns were reproducible and varied between estuarine sediments of differing salinities. The determination of a thermal dissociation curve (i.e., melting profile) for each probe-target duplex provided information on hybridization specificity, which is essential for confirming adequate discrimination between target and nontarget sequences.

Separation and Analysis of Adherent and Non-Adherent Cancer Cells Using a Single-Cell Microarray Chip.

PubMed

Yamamura, Shohei; Yamada, Eriko; Kimura, Fukiko; Miyajima, Kumiko; Shigeto, Hajime

2017-10-21

A new single-cell microarray chip was designed and developed to separate and analyze single adherent and non-adherent cancer cells. The single-cell microarray chip is made of polystyrene with over 60,000 microchambers of 10 different size patterns (31-40 µm upper diameter, 11-20 µm lower diameter). A drop of suspension of adherent carcinoma (NCI-H1650) and non-adherent leukocyte (CCRF-CEM) cells was placed onto the chip, and single-cell occupancy of NCI-H1650 and CCRF-CEM was determined to be 79% and 84%, respectively. This was achieved by controlling the chip design and surface treatment. Analysis of protein expression in single NCI-H1650 and CCRF-CEM cells was performed on the single-cell microarray chip by multi-antibody staining. Additionally, with this system, we retrieved positive single cells from the microchambers by a micromanipulator. Thus, this system demonstrates the potential for easy and accurate separation and analysis of various types of single cells.

Expression profiling and pathway analysis of Krüppel-like factor 4 in mouse embryonic fibroblasts

PubMed Central

Hagos, Engda G; Ghaleb, Amr M; Kumar, Amrita; Neish, Andrew S; Yang, Vincent W

2011-01-01

Background: Krüppel-like factor 4 (KLF4) is a zinc-finger transcription factor with diverse regulatory functions in proliferation, differentiation, and development. KLF4 also plays a role in inflammation, tumorigenesis, and reprogramming of somatic cells to induced pluripotent stem (iPS) cells. To gain insight into the mechanisms by which KLF4 regulates these processes, we conducted DNA microarray analyses to identify differentially expressed genes in mouse embryonic fibroblasts (MEFs) wild type and null for Klf4. Methods: Expression profiles of fibroblasts isolated from mouse embryos wild type or null for the Klf4 alleles were examined by DNA microarrays. Differentially expressed genes were subjected to the Database for Annotation, Visualization and Integrated Discovery (DAVID). The microarray data were also interrogated with the Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA) for pathway identification. Results obtained from the microarray analysis were confirmed by Western blotting for select genes with biological relevance to determine the correlation between mRNA and protein levels. Results: One hundred and sixty three up-regulated and 88 down-regulated genes were identified that demonstrated a fold-change of at least 1.5 and a P-value < 0.05 in Klf4-null MEFs compared to wild type MEFs. Many of the up-regulated genes in Klf4-null MEFs encode proto-oncogenes, growth factors, extracellular matrix, and cell cycle activators. In contrast, genes encoding tumor suppressors and those involved in JAK-STAT signaling pathways are down-regulated in Klf4-null MEFs. IPA and GSEA also identified various pathways that are regulated by KLF4. Lastly, Western blotting of select target genes confirmed the changes revealed by microarray data. Conclusions: These data are not only consistent with previous functional studies of KLF4's role in tumor suppression and somatic cell reprogramming, but also revealed novel target genes that mediate KLF4's functions. PMID:21892412

Cambridge Healthtech Institute's Third Annual Conference on Lab-on-a-Chip and Microarrays. 22-24 January 2001, Zurich, Switzerland.

PubMed

Jain, K K

2001-02-01

Cambridge Healthtech Institute's Third Annual Conference on Lab-on-a-Chip and Microarray technology covered the latest advances in this technology and applications in life sciences. Highlights of the meetings are reported briefly with emphasis on applications in genomics, drug discovery and molecular diagnostics. There was an emphasis on microfluidics because of the wide applications in laboratory and drug discovery. The lab-on-a-chip provides the facilities of a complete laboratory in a hand-held miniature device. Several microarray systems have been used for hybridisation and detection techniques. Oligonucleotide scanning arrays provide a versatile tool for the analysis of nucleic acid interactions and provide a platform for improving the array-based methods for investigation of antisense therapeutics. A method for analysing combinatorial DNA arrays using oligonucleotide-modified gold nanoparticle probes and a conventional scanner has considerable potential in molecular diagnostics. Various applications of microarray technology for high-throughput screening in drug discovery and single nucleotide polymorphisms (SNP) analysis were discussed. Protein chips have important applications in proteomics. With the considerable amount of data generated by the different technologies using microarrays, it is obvious that the reading of the information and its interpretation and management through the use of bioinformatics is essential. Various techniques for data analysis were presented. Biochip and microarray technology has an essential role to play in the evolving trends in healthcare, which integrate diagnosis with prevention/treatment and emphasise personalised medicines.

Is this the real time for genomics?

PubMed

Guarnaccia, Maria; Gentile, Giulia; Alessi, Enrico; Schneider, Claudio; Petralia, Salvatore; Cavallaro, Sebastiano

2014-01-01

In the last decades, molecular biology has moved from gene-by-gene analysis to more complex studies using a genome-wide scale. Thanks to high-throughput genomic technologies, such as microarrays and next-generation sequencing, a huge amount of information has been generated, expanding our knowledge on the genetic basis of various diseases. Although some of this information could be transferred to clinical diagnostics, the technologies available are not suitable for this purpose. In this review, we will discuss the drawbacks associated with the use of traditional DNA microarrays in diagnostics, pointing out emerging platforms that could overcome these obstacles and offer a more reproducible, qualitative and quantitative multigenic analysis. New miniaturized and automated devices, called Lab-on-Chip, begin to integrate PCR and microarray on the same platform, offering integrated sample-to-result systems. The introduction of this kind of innovative devices may facilitate the transition of genome-based tests into clinical routine. Copyright © 2014. Published by Elsevier Inc.

Derivation of an artificial gene to improve classification accuracy upon gene selection.

PubMed

Seo, Minseok; Oh, Sejong

2012-02-01

Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset. Copyright © 2011 Elsevier Ltd. All rights reserved.

Customizing chemotherapy for colon cancer: the potential of gene expression profiling.

PubMed

Mariadason, John M; Arango, Diego; Augenlicht, Leonard H

2004-06-01

The value of gene expression profiling, or microarray analysis, for the classification and prognosis of multiple forms of cancer is now clearly established. For colon cancer, expression profiling can readily discriminate between normal and tumor tissue, and to some extent between tumors of different histopathological stage and prognosis. While a definitive in vivo study demonstrating the potential of this methodology for predicting response to chemotherapy is presently lacking, the ability of microarrays to distinguish other subtleties of colon cancer phenotype, as well as recent in vitro proof-of-principle experiments utilizing colon cancer cell lines, illustrate the potential of this methodology for predicting the probability of response to specific chemotherapeutic agents. This review discusses some of the recent advances in the use of microarray analysis for understanding and distinguishing colon cancer subtypes, and attempts to identify challenges that need to be overcome in order to achieve the goal of using gene expression profiling for customizing chemotherapy in colon cancer.

Alteration of gene expression in the colon of colorectal cancer model rat by dietary sodium gluconate.

PubMed

Kameue, Chiyoko; Tsukahara, Takamitsu; Ushida, Kazunari

2006-03-01

Butyrate induces apoptosis of various cancer cell lines in a p53-independent manner and inhibits the proliferation of cancer cells. In a previous report, we reported a significant reduction in tumor incidence in rat colon as a result of dietary sodium gluconate (GNA). The stimulation of apoptosis through enhanced butyrate production in the large intestine was involved in the antitumorigenic effect of GNA. In the present study, a cDNA microarray analysis was performed to investigate the particular mechanism involved in the antitumorigenic effect of GNA. Some up-regulated genes suggested by microarray analysis were further evaluated using real-time PCR. A microarray revealed that GNA regulates the expression of retinoic acid receptor (RAR) and retinoid X receptor (RXR), and several genes known as the target of retinoids in cancer cells. In other words, the antitumorigenic effect of GNA may involve the regulation of the retinoid signaling pathway by butyrate in a retinoid-independent manner.

A proposed metric for assessing the measurement quality of individual microarrays

PubMed Central

Kim, Kyoungmi; Page, Grier P; Beasley, T Mark; Barnes, Stephen; Scheirer, Katherine E; Allison, David B

2006-01-01

Background High-density microarray technology is increasingly applied to study gene expression levels on a large scale. Microarray experiments rely on several critical steps that may introduce error and uncertainty in analyses. These steps include mRNA sample extraction, amplification and labeling, hybridization, and scanning. In some cases this may be manifested as systematic spatial variation on the surface of microarray in which expression measurements within an individual array may vary as a function of geographic position on the array surface. Results We hypothesized that an index of the degree of spatiality of gene expression measurements associated with their physical geographic locations on an array could indicate the summary of the physical reliability of the microarray. We introduced a novel way to formulate this index using a statistical analysis tool. Our approach regressed gene expression intensity measurements on a polynomial response surface of the microarray's Cartesian coordinates. We demonstrated this method using a fixed model and presented results from real and simulated datasets. Conclusion We demonstrated the potential of such a quantitative metric for assessing the reliability of individual arrays. Moreover, we showed that this procedure can be incorporated into laboratory practice as a means to set quality control specifications and as a tool to determine whether an array has sufficient quality to be retained in terms of spatial correlation of gene expression measurements. PMID:16430768

Analysis of baseline gene expression levels from ...

EPA Pesticide Factsheets

The use of gene expression profiling to predict chemical mode of action would be enhanced by better characterization of variance due to individual, environmental, and technical factors. Meta-analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies has yielded useful information on baseline fluctuations in gene expression. A dataset of control animal microarray expression data was assembled by a working group of the Health and Environmental Sciences Institute's Technical Committee on the Application of Genomics in Mechanism Based Risk Assessment in order to provide a public resource for assessments of variability in baseline gene expression. Data from over 500 Affymetrix microarrays from control rat liver and kidney were collected from 16 different institutions. Thirty-five biological and technical factors were obtained for each animal, describing a wide range of study characteristics, and a subset were evaluated in detail for their contribution to total variability using multivariate statistical and graphical techniques. The study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state. These and other study factors were identified as key descriptors that should be included in the minimal information about a toxicogenomics study needed for interpretation of results by an independent source. Genes that are the most and least variable, gender-selectiv

AFM 4.0: a toolbox for DNA microarray analysis

PubMed Central

Breitkreutz, Bobby-Joe; Jorgensen, Paul; Breitkreutz, Ashton; Tyers, Mike

2001-01-01

We have developed a series of programs, collectively packaged as Array File Maker 4.0 (AFM), that manipulate and manage DNA microarray data. AFM 4.0 is simple to use, applicable to any organism or microarray, and operates within the familiar confines of Microsoft Excel. Given a database of expression ratios, AFM 4.0 generates input files for clustering, helps prepare colored figures and Venn diagrams, and can uncover aneuploidy in yeast microarray data. AFM 4.0 should be especially useful to laboratories that do not have access to specialized commercial or in-house software. PMID:11532221

Microarray analysis of gene expression patterns in the leaf during potato tuberization in the potato somatic hybrid Solanum tuberosum and Solanum etuberosum.

PubMed

Tiwari, Jagesh Kumar; Devi, Sapna; Sundaresha, S; Chandel, Poonam; Ali, Nilofer; Singh, Brajesh; Bhardwaj, Vinay; Singh, Bir Pal

2015-06-01

Genes involved in photoassimilate partitioning and changes in hormonal balance are important for potato tuberization. In the present study, we investigated gene expression patterns in the tuber-bearing potato somatic hybrid (E1-3) and control non-tuberous wild species Solanum etuberosum (Etb) by microarray. Plants were grown under controlled conditions and leaves were collected at eight tuber developmental stages for microarray analysis. A t-test analysis identified a total of 468 genes (94 up-regulated and 374 down-regulated) that were statistically significant (p ≤ 0.05) and differentially expressed in E1-3 and Etb. Gene Ontology (GO) characterization of the 468 genes revealed that 145 were annotated and 323 were of unknown function. Further, these 145 genes were grouped based on GO biological processes followed by molecular function and (or) PGSC description into 15 gene sets, namely (1) transport, (2) metabolic process, (3) biological process, (4) photosynthesis, (5) oxidation-reduction, (6) transcription, (7) translation, (8) binding, (9) protein phosphorylation, (10) protein folding, (11) ubiquitin-dependent protein catabolic process, (12) RNA processing, (13) negative regulation of protein, (14) methylation, and (15) mitosis. RT-PCR analysis of 10 selected highly significant genes (p ≤ 0.01) confirmed the microarray results. Overall, we show that candidate genes induced in leaves of E1-3 were implicated in tuberization processes such as transport, carbohydrate metabolism, phytohormones, and transcription/translation/binding functions. Hence, our results provide an insight into the candidate genes induced in leaf tissues during tuberization in E1-3.

Informatic selection of a neural crest-melanocyte cDNA set for microarray analysis

PubMed Central

Loftus, S. K.; Chen, Y.; Gooden, G.; Ryan, J. F.; Birznieks, G.; Hilliard, M.; Baxevanis, A. D.; Bittner, M.; Meltzer, P.; Trent, J.; Pavan, W.

1999-01-01

With cDNA microarrays, it is now possible to compare the expression of many genes simultaneously. To maximize the likelihood of finding genes whose expression is altered under the experimental conditions, it would be advantageous to be able to select clones for tissue-appropriate cDNA sets. We have taken advantage of the extensive sequence information in the dbEST expressed sequence tag (EST) database to identify a neural crest-derived melanocyte cDNA set for microarray analysis. Analysis of characterized genes with dbEST identified one library that contained ESTs representing 21 neural crest-expressed genes (library 198). The distribution of the ESTs corresponding to these genes was biased toward being derived from library 198. This is in contrast to the EST distribution profile for a set of control genes, characterized to be more ubiquitously expressed in multiple tissues (P < 1 × 10−9). From library 198, a subset of 852 clustered ESTs were selected that have a library distribution profile similar to that of the 21 neural crest-expressed genes. Microarray analysis demonstrated the majority of the neural crest-selected 852 ESTs (Mel1 array) were differentially expressed in melanoma cell lines compared with a non-neural crest kidney epithelial cell line (P < 1 × 10−8). This was not observed with an array of 1,238 ESTs that was selected without library origin bias (P = 0.204). This study presents an approach for selecting tissue-appropriate cDNAs that can be used to examine the expression profiles of developmental processes and diseases. PMID:10430933

APPLICATION OF CDNA MICROARRAY TECHNOLOGY TO IN VITRO TOXICOLOGY AND THE SELECTION OF GENES FOR A REAL TIME RT-PCR-BASED SCREEN FOR OXIDATIVE STRESS IN HEP-G2 CELLS

EPA Science Inventory

Large-scale analysis of gene expression using cDNA microarrays promises the
rapid detection of the mode of toxicity for drugs and other chemicals. cDNA
microarrays were used to examine chemically-induced alterations of gene
expression in HepG2 cells exposed to oxidative ...

Where statistics and molecular microarray experiments biology meet.

PubMed

Kelmansky, Diana M

2013-01-01

This review chapter presents a statistical point of view to microarray experiments with the purpose of understanding the apparent contradictions that often appear in relation to their results. We give a brief introduction of molecular biology for nonspecialists. We describe microarray experiments from their construction and the biological principles the experiments rely on, to data acquisition and analysis. The role of epidemiological approaches and sample size considerations are also discussed.

Self-directed student research through analysis of microarray datasets: a computer-based functional genomics practical class for masters-level students.

PubMed

Grenville-Briggs, Laura J; Stansfield, Ian

2011-01-01

This report describes a linked series of Masters-level computer practical workshops. They comprise an advanced functional genomics investigation, based upon analysis of a microarray dataset probing yeast DNA damage responses. The workshops require the students to analyse highly complex transcriptomics datasets, and were designed to stimulate active learning through experience of current research methods in bioinformatics and functional genomics. They seek to closely mimic a realistic research environment, and require the students first to propose research hypotheses, then test those hypotheses using specific sections of the microarray dataset. The complexity of the microarray data provides students with the freedom to propose their own unique hypotheses, tested using appropriate sections of the microarray data. This research latitude was highly regarded by students and is a strength of this practical. In addition, the focus on DNA damage by radiation and mutagenic chemicals allows them to place their results in a human medical context, and successfully sparks broad interest in the subject material. In evaluation, 79% of students scored the practical workshops on a five-point scale as 4 or 5 (totally effective) for student learning. More broadly, the general use of microarray data as a "student research playground" is also discussed. Copyright © 2011 Wiley Periodicals, Inc.

Clinical experience of laboratory follow-up with noninvasive prenatal testing using cell-free DNA and positive microdeletion results in 349 cases.

PubMed

Schwartz, S; Kohan, M; Pasion, R; Papenhausen, P R; Platt, L D

2018-02-01

Screening via noninvasive prenatal testing (NIPT) involving the analysis of cell-free DNA (cfDNA) from plasma has become readily available to screen for chromosomal and DNA aberrations through maternal blood. This report reviews a laboratory's experience with follow-up of positive NIPT screens for microdeletions. Patients that were screened positive by NIPT for a microdeletion involving 1p, 4p, 5p, 15q, or 22q who underwent diagnostic studies by either chorionic villus sampling or amniocentesis were evaluated. The overall positive predictive value for 349 patients was 9.2%. When a microdeletion was confirmed, 39.3% of the cases had additional abnormal microarray findings. Unrelated abnormal microarray findings were detected in 11.8% of the patients in whom the screen positive microdeletion was not confirmed. Stretches of homozygosity in the microdeletion were frequently associated with a false positive cfDNA microdeletion result. Overall, this report reveals that while cfDNA analysis will screen for microdeletions, the positive predictive value is low; in our series it is 9.2%. Therefore, the patient should be counseled accordingly. Confirmatory diagnostic microarray studies are imperative because of the high percentage of false positives and the frequent additional abnormalities not delineated by cfDNA analysis. © 2018 John Wiley & Sons, Ltd.

Analysis of Protein Expression in Cell Microarrays: A Tool for Antibody-based Proteomics

PubMed Central

Andersson, Ann-Catrin; Strömberg, Sara; Bäckvall, Helena; Kampf, Caroline; Uhlen, Mathias; Wester, Kenneth; Pontén, Fredrik

2006-01-01

Tissue microarray (TMA) technology provides a possibility to explore protein expression patterns in a multitude of normal and disease tissues in a high-throughput setting. Although TMAs have been used for analysis of tissue samples, robust methods for studying in vitro cultured cell lines and cell aspirates in a TMA format have been lacking. We have adopted a technique to homogeneously distribute cells in an agarose gel matrix, creating an artificial tissue. This enables simultaneous profiling of protein expression in suspension- and adherent-grown cell samples assembled in a microarray. In addition, the present study provides an optimized strategy for the basic laboratory steps to efficiently produce TMAs. Presented modifications resulted in an improved quality of specimens and a higher section yield compared with standard TMA production protocols. Sections from the generated cell TMAs were tested for immunohistochemical staining properties using 20 well-characterized antibodies. Comparison of immunoreactivity in cultured dispersed cells and corresponding cells in tissue samples showed congruent results for all tested antibodies. We conclude that a modified TMA technique, including cell samples, provides a valuable tool for high-throughput analysis of protein expression, and that this technique can be used for global approaches to explore the human proteome. PMID:16957166

Biologically relevant effects of mRNA amplification on gene expression profiles.

PubMed

van Haaften, Rachel I M; Schroen, Blanche; Janssen, Ben J A; van Erk, Arie; Debets, Jacques J M; Smeets, Hubert J M; Smits, Jos F M; van den Wijngaard, Arthur; Pinto, Yigal M; Evelo, Chris T A

2006-04-11

Gene expression microarray technology permits the analysis of global gene expression profiles. The amount of sample needed limits the use of small excision biopsies and/or needle biopsies from human or animal tissues. Linear amplification techniques have been developed to increase the amount of sample derived cDNA. These amplified samples can be hybridised on microarrays. However, little information is available whether microarrays based on amplified and unamplified material yield comparable results. In the present study we compared microarray data obtained from amplified mRNA derived from biopsies of rat cardiac left ventricle and non-amplified mRNA derived from the same organ. Biopsies were linearly amplified to acquire enough material for a microarray experiment. Both amplified and unamplified samples were hybridized to the Rat Expression Set 230 Array of Affymetrix. Analysis of the microarray data showed that unamplified material of two different left ventricles had 99.6% identical gene expression. Gene expression patterns of two biopsies obtained from the same parental organ were 96.3% identical. Similarly, gene expression pattern of two biopsies from dissimilar organs were 92.8% identical to each other.Twenty-one percent of reporters called present in parental left ventricular tissue disappeared after amplification in the biopsies. Those reporters were predominantly seen in the low intensity range. Sequence analysis showed that reporters that disappeared after amplification had a GC-content of 53.7+/-4.0%, while reporters called present in biopsy- and whole LV-samples had an average GC content of 47.8+/-5.5% (P <0.001). Those reporters were also predicted to form significantly more (0.76+/-0.07 versus 0.38+/-0.1) and longer (9.4+/-0.3 versus 8.4+/-0.4) hairpins as compared to representative control reporters present before and after amplification. This study establishes that the gene expression profile obtained after amplification of mRNA of left ventricular biopsies is representative for the whole left ventricle of the rat heart. However, specific gene transcripts present in parental tissues were undetectable in the minute left ventricular biopsies. Transcripts that were lost due to the amplification process were not randomly distributed, but had higher GC-content and hairpins in the sequence and were mainly found in the lower intensity range which includes many transcription factors from specific signalling pathways.

Biologically relevant effects of mRNA amplification on gene expression profiles

PubMed Central

van Haaften, Rachel IM; Schroen, Blanche; Janssen, Ben JA; van Erk, Arie; Debets, Jacques JM; Smeets, Hubert JM; Smits, Jos FM; van den Wijngaard, Arthur; Pinto, Yigal M; Evelo, Chris TA

2006-01-01

Background Gene expression microarray technology permits the analysis of global gene expression profiles. The amount of sample needed limits the use of small excision biopsies and/or needle biopsies from human or animal tissues. Linear amplification techniques have been developed to increase the amount of sample derived cDNA. These amplified samples can be hybridised on microarrays. However, little information is available whether microarrays based on amplified and unamplified material yield comparable results. In the present study we compared microarray data obtained from amplified mRNA derived from biopsies of rat cardiac left ventricle and non-amplified mRNA derived from the same organ. Biopsies were linearly amplified to acquire enough material for a microarray experiment. Both amplified and unamplified samples were hybridized to the Rat Expression Set 230 Array of Affymetrix. Results Analysis of the microarray data showed that unamplified material of two different left ventricles had 99.6% identical gene expression. Gene expression patterns of two biopsies obtained from the same parental organ were 96.3% identical. Similarly, gene expression pattern of two biopsies from dissimilar organs were 92.8% identical to each other. Twenty-one percent of reporters called present in parental left ventricular tissue disappeared after amplification in the biopsies. Those reporters were predominantly seen in the low intensity range. Sequence analysis showed that reporters that disappeared after amplification had a GC-content of 53.7+/-4.0%, while reporters called present in biopsy- and whole LV-samples had an average GC content of 47.8+/-5.5% (P <0.001). Those reporters were also predicted to form significantly more (0.76+/-0.07 versus 0.38+/-0.1) and longer (9.4+/-0.3 versus 8.4+/-0.4) hairpins as compared to representative control reporters present before and after amplification. Conclusion This study establishes that the gene expression profile obtained after amplification of mRNA of left ventricular biopsies is representative for the whole left ventricle of the rat heart. However, specific gene transcripts present in parental tissues were undetectable in the minute left ventricular biopsies. Transcripts that were lost due to the amplification process were not randomly distributed, but had higher GC-content and hairpins in the sequence and were mainly found in the lower intensity range which includes many transcription factors from specific signalling pathways. PMID:16608515

Transcriptional Changes in Canine Distemper Virus-Induced Demyelinating Leukoencephalitis Favor a Biphasic Mode of Demyelination

PubMed Central

Ulrich, Reiner; Puff, Christina; Wewetzer, Konstantin; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang

2014-01-01

Canine distemper virus (CDV)-induced demyelinating leukoencephalitis in dogs (Canis familiaris) is suggested to represent a naturally occurring translational model for subacute sclerosing panencephalitis and multiple sclerosis in humans. The aim of this study was a hypothesis-free microarray analysis of the transcriptional changes within cerebellar specimens of five cases of acute, six cases of subacute demyelinating, and three cases of chronic demyelinating and inflammatory CDV leukoencephalitis as compared to twelve non-infected control dogs. Frozen cerebellar specimens were used for analysis of histopathological changes including demyelination, transcriptional changes employing microarrays, and presence of CDV nucleoprotein RNA and protein using microarrays, RT-qPCR and immunohistochemistry. Microarray analysis revealed 780 differentially expressed probe sets. The dominating change was an up-regulation of genes related to the innate and the humoral immune response, and less distinct the cytotoxic T-cell-mediated immune response in all subtypes of CDV leukoencephalitis as compared to controls. Multiple myelin genes including myelin basic protein and proteolipid protein displayed a selective down-regulation in subacute CDV leukoencephalitis, suggestive of an oligodendrocyte dystrophy. In contrast, a marked up-regulation of multiple immunoglobulin-like expressed sequence tags and the delta polypeptide of the CD3 antigen was observed in chronic CDV leukoencephalitis, in agreement with the hypothesis of an immune-mediated demyelination in the late inflammatory phase of the disease. Analysis of pathways intimately linked to demyelination as determined by morphometry employing correlation-based Gene Set Enrichment Analysis highlighted the pathomechanistic importance of up-regulated genes comprised by the gene ontology terms “viral replication” and “humoral immune response” as well as down-regulated genes functionally related to “metabolite and energy generation”. PMID:24755553

Transcriptional changes in canine distemper virus-induced demyelinating leukoencephalitis favor a biphasic mode of demyelination.

PubMed

Ulrich, Reiner; Puff, Christina; Wewetzer, Konstantin; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang

2014-01-01

Canine distemper virus (CDV)-induced demyelinating leukoencephalitis in dogs (Canis familiaris) is suggested to represent a naturally occurring translational model for subacute sclerosing panencephalitis and multiple sclerosis in humans. The aim of this study was a hypothesis-free microarray analysis of the transcriptional changes within cerebellar specimens of five cases of acute, six cases of subacute demyelinating, and three cases of chronic demyelinating and inflammatory CDV leukoencephalitis as compared to twelve non-infected control dogs. Frozen cerebellar specimens were used for analysis of histopathological changes including demyelination, transcriptional changes employing microarrays, and presence of CDV nucleoprotein RNA and protein using microarrays, RT-qPCR and immunohistochemistry. Microarray analysis revealed 780 differentially expressed probe sets. The dominating change was an up-regulation of genes related to the innate and the humoral immune response, and less distinct the cytotoxic T-cell-mediated immune response in all subtypes of CDV leukoencephalitis as compared to controls. Multiple myelin genes including myelin basic protein and proteolipid protein displayed a selective down-regulation in subacute CDV leukoencephalitis, suggestive of an oligodendrocyte dystrophy. In contrast, a marked up-regulation of multiple immunoglobulin-like expressed sequence tags and the delta polypeptide of the CD3 antigen was observed in chronic CDV leukoencephalitis, in agreement with the hypothesis of an immune-mediated demyelination in the late inflammatory phase of the disease. Analysis of pathways intimately linked to demyelination as determined by morphometry employing correlation-based Gene Set Enrichment Analysis highlighted the pathomechanistic importance of up-regulated genes comprised by the gene ontology terms "viral replication" and "humoral immune response" as well as down-regulated genes functionally related to "metabolite and energy generation".

Microarray study of single nucleotide polymorphisms and expression of ATP-binding cassette genes in breast tumors

NASA Astrophysics Data System (ADS)

Tsyganov, M. M.; Ibragimova, M. K.; Karabut, I. V.; Freydin, M. B.; Choinzonov, E. L.; Litvyakov, N. V.

2015-11-01

Our previous research establishes that changes of expression of the ATP-binding cassette genes family is connected with the neoadjuvant chemotherapy effect. However, the mechanism of regulation of resistance gene expression remains unclear. As many researchers believe, single nucleotide polymorphisms can be involved in this process. Thereupon, microarray analysis is used to study polymorphisms in ATP-binding cassette genes. It is thus found that MDR gene expression is connected with 5 polymorphisms, i.e. rs241432, rs241429, rs241430, rs3784867, rs59409230, which participate in the regulation of expression of own genes.

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

PubMed Central

Brodsky, Leonid; Leontovich, Andrei; Shtutman, Michael; Feinstein, Elena

2004-01-01

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides’ areas characterized by an abnormal concentration of low/high differential expression values, which we define as ‘patterns of differentials’. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile’s quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis. PMID:14999086

Novel calibration tools and validation concepts for microarray-based platforms used in molecular diagnostics and food safety control.

PubMed

Brunner, C; Hoffmann, K; Thiele, T; Schedler, U; Jehle, H; Resch-Genger, U

2015-04-01

Commercial platforms consisting of ready-to-use microarrays printed with target-specific DNA probes, a microarray scanner, and software for data analysis are available for different applications in medical diagnostics and food analysis, detecting, e.g., viral and bacteriological DNA sequences. The transfer of these tools from basic research to routine analysis, their broad acceptance in regulated areas, and their use in medical practice requires suitable calibration tools for regular control of instrument performance in addition to internal assay controls. Here, we present the development of a novel assay-adapted calibration slide for a commercialized DNA-based assay platform, consisting of precisely arranged fluorescent areas of various intensities obtained by incorporating different concentrations of a "green" dye and a "red" dye in a polymer matrix. These dyes present "Cy3" and "Cy5" analogues with improved photostability, chosen based upon their spectroscopic properties closely matching those of common labels for the green and red channel of microarray scanners. This simple tool allows to efficiently and regularly assess and control the performance of the microarray scanner provided with the biochip platform and to compare different scanners. It will be eventually used as fluorescence intensity scale for referencing of assays results and to enhance the overall comparability of diagnostic tests.

The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

PubMed Central

Sioson, Allan A; Mane, Shrinivasrao P; Li, Pinghua; Sha, Wei; Heath, Lenwood S; Bohnert, Hans J; Grene, Ruth

2006-01-01

Background Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. Results The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. Conclusion The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In one experiment, MIV is a better choice than IIV as input to data normalization and statistical analysis methods, as it yields as greater number of statistically significant differentially expressed genes; TM4 does not support the choice of MIV input data. Overall, the more flexible and extensive statistical models of Expresso achieve more accurate analytical results, when judged by the yardstick of qRT-PCR data, in the context of an experimental design of modest complexity. PMID:16626497

Diagnostic Yield of Chromosomal Microarray Analysis in an Autism Primary Care Practice: Which Guidelines to Implement?

ERIC Educational Resources Information Center

McGrew, Susan G.; Peters, Brittany R.; Crittendon, Julie A.; Veenstra-VanderWeele, Jeremy

2012-01-01

Genetic testing is recommended for patients with ASD; however specific recommendations vary by specialty. American Academy of Pediatrics and American Academy of Neurology guidelines recommend G-banded karyotype and Fragile X DNA. The American College of Medical Genetics recommends Chromosomal Microarray Analysis (CMA). We determined the yield of…

Self-Directed Student Research through Analysis of Microarray Datasets: A Computer-Based Functional Genomics Practical Class for Masters-Level Students

ERIC Educational Resources Information Center

Grenville-Briggs, Laura J.; Stansfield, Ian

2011-01-01

This report describes a linked series of Masters-level computer practical workshops. They comprise an advanced functional genomics investigation, based upon analysis of a microarray dataset probing yeast DNA damage responses. The workshops require the students to analyse highly complex transcriptomics datasets, and were designed to stimulate…

Vaginal microbial flora analysis by next generation sequencing and microarrays; can microbes indicate vaginal origin in a forensic context?

PubMed

Benschop, Corina C G; Quaak, Frederike C A; Boon, Mathilde E; Sijen, Titia; Kuiper, Irene

2012-03-01

Forensic analysis of biological traces generally encompasses the investigation of both the person who contributed to the trace and the body site(s) from which the trace originates. For instance, for sexual assault cases, it can be beneficial to distinguish vaginal samples from skin or saliva samples. In this study, we explored the use of microbial flora to indicate vaginal origin. First, we explored the vaginal microbiome for a large set of clinical vaginal samples (n = 240) by next generation sequencing (n = 338,184 sequence reads) and found 1,619 different sequences. Next, we selected 389 candidate probes targeting genera or species and designed a microarray, with which we analysed a diverse set of samples; 43 DNA extracts from vaginal samples and 25 DNA extracts from samples from other body sites, including sites in close proximity of or in contact with the vagina. Finally, we used the microarray results and next generation sequencing dataset to assess the potential for a future approach that uses microbial markers to indicate vaginal origin. Since no candidate genera/species were found to positively identify all vaginal DNA extracts on their own, while excluding all non-vaginal DNA extracts, we deduce that a reliable statement about the cellular origin of a biological trace should be based on the detection of multiple species within various genera. Microarray analysis of a sample will then render a microbial flora pattern that is probably best analysed in a probabilistic approach.

16S rRNA based microarray analysis of ten periodontal bacteria in patients with different forms of periodontitis.

PubMed

Topcuoglu, Nursen; Kulekci, Guven

2015-10-01

DNA microarray analysis is a computer based technology, that a reverse capture, which targets 10 periodontal bacteria (ParoCheck) is available for rapid semi-quantitative determination. The aim of this three-year retrospective study was to display the microarray analysis results for the subgingival biofilm samples taken from patient cases diagnosed with different forms of periodontitis. A total of 84 patients with generalized aggressive periodontitis (GAP,n:29), generalized chronic periodontitis (GCP, n:25), peri-implantitis (PI,n:14), localized aggressive periodontitis (LAP,n:8) and refractory chronic periodontitis (RP,n:8) were consecutively selected from the archives of the Oral Microbiological Diagnostic Laboratory. The subgingival biofilm samples were analyzed by the microarray-based identification of 10 selected species. All the tested species were detected in the samples. The red complex bacteria were the most prevalent with very high levels in all groups. Fusobacterium nucleatum was detected in all samples at high levels. The green and blue complex bacteria were less prevalent compared with red and orange complex, except Aggregatibacter actinomycetemcomitas was detected in all LAP group. Positive correlations were found within all the red complex bacteria and between red and orange complex bacteria especially in GCP and GAP groups. Parocheck enables to monitoring of periodontal pathogens in all forms of periodontal disease and can be alternative to other guiding and reliable microbiologic tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

LS Bound based gene selection for DNA microarray data.

PubMed

Zhou, Xin; Mao, K Z

2005-04-15

One problem with discriminant analysis of DNA microarray data is that each sample is represented by quite a large number of genes, and many of them are irrelevant, insignificant or redundant to the discriminant problem at hand. Methods for selecting important genes are, therefore, of much significance in microarray data analysis. In the present study, a new criterion, called LS Bound measure, is proposed to address the gene selection problem. The LS Bound measure is derived from leave-one-out procedure of LS-SVMs (least squares support vector machines), and as the upper bound for leave-one-out classification results it reflects to some extent the generalization performance of gene subsets. We applied this LS Bound measure for gene selection on two benchmark microarray datasets: colon cancer and leukemia. We also compared the LS Bound measure with other evaluation criteria, including the well-known Fisher's ratio and Mahalanobis class separability measure, and other published gene selection algorithms, including Weighting factor and SVM Recursive Feature Elimination. The strength of the LS Bound measure is that it provides gene subsets leading to more accurate classification results than the filter method while its computational complexity is at the level of the filter method. A companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/. The website contains: (1) the source code of the gene selection algorithm; (2) the complete set of tables and figures regarding the experimental study; (3) proof of the inequality (9). ekzmao@ntu.edu.sg.

Rapid Characterization of Candidate Biomarkers for Pancreatic Cancer Using Cell Microarrays (CMAs)

PubMed Central

Kim, Min-Sik; Kuppireddy, Sarada V.; Sakamuri, Sruthi; Singal, Mukul; Getnet, Derese; Harsha, H. C.; Goel, Renu; Balakrishnan, Lavanya; Jacob, Harrys K. C.; Kashyap, Manoj K.; Tankala, Shantal G.; Maitra, Anirban; Iacobuzio-Donahue, Christine A.; Jaffee, Elizabeth; Goggins, Michael G.; Velculescu, Victor E.; Hruban, Ralph H.; Pandey, Akhilesh

2013-01-01

Tissue microarrays have become a valuable tool for high-throughput analysis using immunohistochemical labeling. However, the large majority of biochemical studies are carried out in cell lines to further characterize candidate biomarkers or therapeutic targets with subsequent studies in animals or using primary tissues. Thus, cell line-based microarrays could be a useful screening tool in some situations. Here, we constructed a cell microarray (CMA) containing a panel of 40 pancreatic cancer cell lines available from American Type Culture Collection in addition to those locally available at Johns Hopkins. As proof of principle, we performed immunocytochemical labeling of an epithelial cell adhesion molecule (Ep-CAM), a molecule generally expressed in the epithelium, on this pancreatic cancer CMA. In addition, selected molecules that have been previously shown to be differentially expressed in pancreatic cancer in the literature were validated. For example, we observed strong labeling of CA19-9 antigen, a prognostic and predictive marker for pancreatic cancer. We also carried out a bioinformatics analysis of a literature curated catalog of pancreatic cancer biomarkers developed previously by our group and identified two candidate biomarkers, HLA class I and transmembrane protease, serine 4 (TMPRSS4), and examined their expression in the cell lines represented on the pancreatic cancer CMAs. Our results demonstrate the utility of CMAs as a useful resource for rapid screening of molecules of interest and suggest that CMAs can become a universal standard platform in cancer research. PMID:22985314

CNV-ROC: A cost effective, computer-aided analytical performance evaluator of chromosomal microarrays.

PubMed

Goodman, Corey W; Major, Heather J; Walls, William D; Sheffield, Val C; Casavant, Thomas L; Darbro, Benjamin W

2015-04-01

Chromosomal microarrays (CMAs) are routinely used in both research and clinical laboratories; yet, little attention has been given to the estimation of genome-wide true and false negatives during the assessment of these assays and how such information could be used to calibrate various algorithmic metrics to improve performance. Low-throughput, locus-specific methods such as fluorescence in situ hybridization (FISH), quantitative PCR (qPCR), or multiplex ligation-dependent probe amplification (MLPA) preclude rigorous calibration of various metrics used by copy number variant (CNV) detection algorithms. To aid this task, we have established a comparative methodology, CNV-ROC, which is capable of performing a high throughput, low cost, analysis of CMAs that takes into consideration genome-wide true and false negatives. CNV-ROC uses a higher resolution microarray to confirm calls from a lower resolution microarray and provides for a true measure of genome-wide performance metrics at the resolution offered by microarray testing. CNV-ROC also provides for a very precise comparison of CNV calls between two microarray platforms without the need to establish an arbitrary degree of overlap. Comparison of CNVs across microarrays is done on a per-probe basis and receiver operator characteristic (ROC) analysis is used to calibrate algorithmic metrics, such as log2 ratio threshold, to enhance CNV calling performance. CNV-ROC addresses a critical and consistently overlooked aspect of analytical assessments of genome-wide techniques like CMAs which is the measurement and use of genome-wide true and false negative data for the calculation of performance metrics and comparison of CNV profiles between different microarray experiments. Copyright © 2015 Elsevier Inc. All rights reserved.

Molecular biological identification of Babesia, Theileria, and Anaplasma species in cattle in Egypt using PCR assays, gene sequence analysis and a novel DNA microarray.

PubMed

El-Ashker, Maged; Hotzel, Helmut; Gwida, Mayada; El-Beskawy, Mohamed; Silaghi, Cornelia; Tomaso, Herbert

2015-01-30

In this preliminary study, a novel DNA microarray system was tested for the diagnosis of bovine piroplasmosis and anaplasmosis in comparison with microscopy and PCR assay results. In the Dakahlia Governorate, Egypt, 164 cattle were investigated for the presence of piroplasms and Anaplasma species. All investigated cattle were clinically examined. Blood samples were screened for the presence of blood parasites using microscopy and PCR assays. Seventy-one animals were acutely ill, whereas 93 were apparently healthy. In acutely ill cattle, Babesia/Theileria species (n=11) and Anaplasma marginale (n=10) were detected. Mixed infections with Babesia/Theileria spp. and A. marginale were present in two further cases. A. marginale infections were also detected in apparently healthy subjects (n=23). The results of PCR assays were confirmed by DNA sequencing. All samples that were positive by PCR for Babesia/Theileria spp. gave also positive results in the microarray analysis. The microarray chips identified Babesia bovis (n=12) and Babesia bigemina (n=2). Cattle with babesiosis were likely to have hemoglobinuria and nervous signs when compared to those with anaplasmosis that frequently had bloody feces. We conclude that clinical examination in combination with microscopy are still very useful in diagnosing acute cases of babesiosis and anaplasmosis, but a combination of molecular biological diagnostic assays will detect even asymptomatic carriers. In perspective, parallel detection of Babesia/Theileria spp. and A. marginale infections using a single microarray system will be a valuable improvement. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Fabrication of high quality cDNA microarray using a small amount of cDNA.

PubMed

Park, Chan Hee; Jeong, Ha Jin; Jung, Jae Jun; Lee, Gui Yeon; Kim, Sang-Chul; Kim, Tae Soo; Yang, Sang Hwa; Chung, Hyun Cheol; Rha, Sun Young

2004-05-01

DNA microarray technology has become an essential part of biological research. It enables the genome-scale analysis of gene expression in various types of model systems. Manufacturing high quality cDNA microarrays of microdeposition type depends on some key factors including a printing device, spotting pins, glass slides, spotting solution, and humidity during spotting. UsingEthe Microgrid II TAS model printing device, this study defined the optimal conditions for producing high density, high quality cDNA microarrays with the least amount of cDNA product. It was observed that aminosilane-modified slides were superior to other types of surface modified-slides. A humidity of 30+/-3% in a closed environment and the overnight drying of the spotted slides gave the best conditions for arraying. In addition, the cDNA dissolved in 30% DMSO gave the optimal conditions for spotting compared to the 1X ArrayIt, 3X SSC and 50% DMSO. Lastly, cDNA in the concentration range of 100-300 ng/ micro l was determined to be best for arraying and post-processing. Currently, the printing system in this study yields reproducible 9000 spots with a spot size 150 mm diameter, and a 200 nm spot spacing.

Mining microarrays for metabolic meaning: nutritional regulation of hypothalamic gene expression.

PubMed

Mobbs, Charles V; Yen, Kelvin; Mastaitis, Jason; Nguyen, Ha; Watson, Elizabeth; Wurmbach, Elisa; Sealfon, Stuart C; Brooks, Andrew; Salton, Stephen R J

2004-06-01

DNA microarray analysis has been used to investigate relative changes in the level of gene expression in the CNS, including changes that are associated with disease, injury, psychiatric disorders, drug exposure or withdrawal, and memory formation. We have used oligonucleotide microarrays to identify hypothalamic genes that respond to nutritional manipulation. In addition to commonly used microarray analysis based on criteria such as fold-regulation, we have also found that simply carrying out multiple t tests then sorting by P value constitutes a highly reliable method to detect true regulation, as assessed by real-time polymerase chain reaction (PCR), even for relatively low abundance genes or relatively low magnitude of regulation. Such analyses directly suggested novel mechanisms that mediate effects of nutritional state on neuroendocrine function and are being used to identify regulated gene products that may elucidate the metabolic pathology of obese ob/ob, lean Vgf-/Vgf-, and other models with profound metabolic impairments.

Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.

PubMed Central

Schena, M; Shalon, D; Heller, R; Chai, A; Brown, P O; Davis, R W

1996-01-01

Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm2 DNA "chips" were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery. Images Fig. 1 Fig. 2 Fig. 3 PMID:8855227

Novel Biomarker Candidates for Colorectal Cancer Metastasis: A Meta-analysis of In Vitro Studies

PubMed Central

Long, Nguyen Phuoc; Lee, Wun Jun; Huy, Nguyen Truong; Lee, Seul Ji; Park, Jeong Hill; Kwon, Sung Won

2016-01-01

Colorectal cancer (CRC) is one of the most common and lethal cancers. Although numerous studies have evaluated potential biomarkers for early diagnosis, current biomarkers have failed to reach an acceptable level of accuracy for distant metastasis. In this paper, we performed a gene set meta-analysis of in vitro microarray studies and combined the results from this study with previously published proteomic data to validate and suggest prognostic candidates for CRC metastasis. Two microarray data sets included found 21 significant genes. Of these significant genes, ALDOA, IL8 (CXCL8), and PARP4 had strong potential as prognostic candidates. LAMB2, MCM7, CXCL23A, SERPINA3, ABCA3, ALDH3A2, and POLR2I also have potential. Other candidates were more controversial, possibly because of the biologic heterogeneity of tumor cells, which is a major obstacle to predicting metastasis. In conclusion, we demonstrated a meta-analysis approach and successfully suggested ten biomarker candidates for future investigation. PMID:27688707

Novel Biomarker Candidates for Colorectal Cancer Metastasis: A Meta-analysis of In Vitro Studies.

PubMed

Long, Nguyen Phuoc; Lee, Wun Jun; Huy, Nguyen Truong; Lee, Seul Ji; Park, Jeong Hill; Kwon, Sung Won

2016-01-01

Colorectal cancer (CRC) is one of the most common and lethal cancers. Although numerous studies have evaluated potential biomarkers for early diagnosis, current biomarkers have failed to reach an acceptable level of accuracy for distant metastasis. In this paper, we performed a gene set meta-analysis of in vitro microarray studies and combined the results from this study with previously published proteomic data to validate and suggest prognostic candidates for CRC metastasis. Two microarray data sets included found 21 significant genes. Of these significant genes, ALDOA, IL8 (CXCL8), and PARP4 had strong potential as prognostic candidates. LAMB2, MCM7, CXCL23A, SERPINA3, ABCA3, ALDH3A2, and POLR2I also have potential. Other candidates were more controversial, possibly because of the biologic heterogeneity of tumor cells, which is a major obstacle to predicting metastasis. In conclusion, we demonstrated a meta-analysis approach and successfully suggested ten biomarker candidates for future investigation.

Protein profiles associated with survival in lung adenocarcinoma

PubMed Central

Chen, Guoan; Gharib, Tarek G; Wang, Hong; Huang, Chiang-Ching; Kuick, Rork; Thomas, Dafydd G.; Shedden, Kerby A.; Misek, David E.; Taylor, Jeremy M. G.; Giordano, Thomas J.; Kardia, Sharon L. R.; Iannettoni, Mark D.; Yee, John; Hogg, Philip J.; Orringer, Mark B.; Hanash, Samir M.; Beer, David G.

2003-01-01

Morphologic assessment of lung tumors is informative but insufficient to adequately predict patient outcome. We previously identified transcriptional profiles that predict patient survival, and here we identify proteins associated with patient survival in lung adenocarcinoma. A total of 682 individual protein spots were quantified in 90 lung adenocarcinomas by using quantitative two-dimensional polyacrylamide gel electrophoresis analysis. A leave-one-out cross-validation procedure using the top 20 survival-associated proteins identified by Cox modeling indicated that protein profiles as a whole can predict survival in stage I tumor patients (P = 0.01). Thirty-three of 46 survival-associated proteins were identified by using mass spectrometry. Expression of 12 candidate proteins was confirmed as tumor-derived with immunohistochemical analysis and tissue microarrays. Oligonucleotide microarray results from both the same tumors and from an independent study showed mRNAs associated with survival for 11 of 27 encoded genes. Combined analysis of protein and mRNA data revealed 11 components of the glycolysis pathway as associated with poor survival. Among these candidates, phosphoglycerate kinase 1 was associated with survival in the protein study, in both mRNA studies and in an independent validation set of 117 adenocarcinomas and squamous lung tumors using tissue microarrays. Elevated levels of phosphoglycerate kinase 1 in the serum were also significantly correlated with poor outcome in a validation set of 107 patients with lung adenocarcinomas using ELISA analysis. These studies identify new prognostic biomarkers and indicate that protein expression profiles can predict the outcome of patients with early-stage lung cancer. PMID:14573703

Aberrant expression of long noncoding RNAs in cumulus cells isolated from PCOS patients.

PubMed

Huang, Xin; Hao, Cuifang; Bao, Hongchu; Wang, Meimei; Dai, Huangguan

2016-01-01

To describe the long noncoding RNA (lncRNA) profiles in cumulus cells isolated from polycystic ovary syndrome (PCOS) patients by employing a microarray and in-depth bioinformatics analysis. This information will help us understand the occurrence and development of PCOS. In this study, we used a microarray to describe lncRNA profiles in cumulus cells isolated from ten patients (five PCOS and five normal women). Several differentially expressed lncRNAs were chosen to validate the microarray results by quantitative RT-PCR (qRT-PCR). Then, the differentially expressed lncRNAs were classified into three subgroups (HOX loci lncRNA, enhancer-like lncRNA, and lincRNA) to deduce their potential features. Furthermore, a lncRNA/mRNA co-expression network was constructed by using the Cytoscape software (V2.8.3, http://www.cytoscape.org/ ). We observed that 623 lncRNAs and 260 messenger RNAs (mRNAs) were significantly up- or down-regulated (≥2-fold change), and these differences could be used to discriminate cumulus cells of PCOS from those of normal patients. Five differentially expressed lncRNAs (XLOC_011402, ENST00000454271, ENST00000433673, ENST00000450294, and ENST00000432431) were selected to validate the microarray results using quantitative RT-PCR (qRT-PCR). The qRT-PCR results were consistent with the microarray data. Further analysis indicated that many differentially expressed lncRNAs were transcribed from chromosome 2 and may act as enhancers to regulate their neighboring protein-coding genes. Forty-three lncRNAs and 29 mRNAs were used to construct the coding-non-coding gene co-expression network. Most pairs positively correlated, and one mRNA correlated with one or more lncRNAs. Our study is the first to determine genome-wide lncRNA expression patterns in cumulus cells isolated from PCOS patients by microarray. The results show that clusters of lncRNAs were aberrantly expressed in cumulus cells of PCOS patients compared with those of normal women, which revealed that lncRNAs differentially expressed in PCOS and normal women may contribute to the occurrence of PCOS and affect oocyte development.

Calibration and assessment of channel-specific biases in microarray data with extended dynamical range.

PubMed

Bengtsson, Henrik; Jönsson, Göran; Vallon-Christersson, Johan

2004-11-12

Non-linearities in observed log-ratios of gene expressions, also known as intensity dependent log-ratios, can often be accounted for by global biases in the two channels being compared. Any step in a microarray process may introduce such offsets and in this article we study the biases introduced by the microarray scanner and the image analysis software. By scanning the same spotted oligonucleotide microarray at different photomultiplier tube (PMT) gains, we have identified a channel-specific bias present in two-channel microarray data. For the scanners analyzed it was in the range of 15-25 (out of 65,535). The observed bias was very stable between subsequent scans of the same array although the PMT gain was greatly adjusted. This indicates that the bias does not originate from a step preceding the scanner detector parts. The bias varies slightly between arrays. When comparing estimates based on data from the same array, but from different scanners, we have found that different scanners introduce different amounts of bias. So do various image analysis methods. We propose a scanning protocol and a constrained affine model that allows us to identify and estimate the bias in each channel. Backward transformation removes the bias and brings the channels to the same scale. The result is that systematic effects such as intensity dependent log-ratios are removed, but also that signal densities become much more similar. The average scan, which has a larger dynamical range and greater signal-to-noise ratio than individual scans, can then be obtained. The study shows that microarray scanners may introduce a significant bias in each channel. Such biases have to be calibrated for, otherwise systematic effects such as intensity dependent log-ratios will be observed. The proposed scanning protocol and calibration method is simple to use and is useful for evaluating scanner biases or for obtaining calibrated measurements with extended dynamical range and better precision. The cross-platform R package aroma, which implements all described methods, is available for free from http://www.maths.lth.se/bioinformatics/.

DNA microarray analyses reveal a post-irradiation differential time-dependent gene expression profile in yeast cells exposed to X-rays and gamma-rays.

PubMed

Kimura, Shinzo; Ishidou, Emi; Kurita, Sakiko; Suzuki, Yoshiteru; Shibato, Junko; Rakwal, Randeep; Iwahashi, Hitoshi

2006-07-21

Ionizing radiation (IR) is the most enigmatic of genotoxic stress inducers in our environment that has been around from the eons of time. IR is generally considered harmful, and has been the subject of numerous studies, mostly looking at the DNA damaging effects in cells and the repair mechanisms therein. Moreover, few studies have focused on large-scale identification of cellular responses to IR, and to this end, we describe here an initial study on the transcriptional responses of the unicellular genome model, yeast (Saccharomyces cerevisiae strain S288C), by cDNA microarray. The effect of two different IR, X-rays, and gamma (gamma)-rays, was investigated by irradiating the yeast cells cultured in YPD medium with 50 Gy doses of X- and gamma-rays, followed by resuspension of the cells in YPD for time-course experiments. The samples were collected for microarray analysis at 20, 40, and 80 min after irradiation. Microarray analysis revealed a time-course transcriptional profile of changed gene expressions. Up-regulated genes belonged to the functional categories mainly related to cell cycle and DNA processing, cell rescue defense and virulence, protein and cell fate, and metabolism (X- and gamma-rays). Similarly, for X- and gamma-rays, the down-regulated genes belonged to mostly transcription and protein synthesis, cell cycle and DNA processing, control of cellular organization, cell fate, and C-compound and carbohydrate metabolism categories, respectively. This study provides for the first time a snapshot of the genome-wide mRNA expression profiles in X- and gamma-ray post-irradiated yeast cells and comparatively interprets/discusses the changed gene functional categories as effects of these two radiations vis-à-vis their energy levels.

A database for the analysis of immunity genes in Drosophila: PADMA database.

PubMed

Lee, Mark J; Mondal, Ariful; Small, Chiyedza; Paddibhatla, Indira; Kawaguchi, Akira; Govind, Shubha

2011-01-01

While microarray experiments generate voluminous data, discerning trends that support an existing or alternative paradigm is challenging. To synergize hypothesis building and testing, we designed the Pathogen Associated Drosophila MicroArray (PADMA) database for easy retrieval and comparison of microarray results from immunity-related experiments (www.padmadatabase.org). PADMA also allows biologists to upload their microarray-results and compare it with datasets housed within PADMA. We tested PADMA using a preliminary dataset from Ganaspis xanthopoda-infected fly larvae, and uncovered unexpected trends in gene expression, reshaping our hypothesis. Thus, the PADMA database will be a useful resource to fly researchers to evaluate, revise, and refine hypotheses.

Separate-channel analysis of two-channel microarrays: recovering inter-spot information.

PubMed

Smyth, Gordon K; Altman, Naomi S

2013-05-26

Two-channel (or two-color) microarrays are cost-effective platforms for comparative analysis of gene expression. They are traditionally analysed in terms of the log-ratios (M-values) of the two channel intensities at each spot, but this analysis does not use all the information available in the separate channel observations. Mixed models have been proposed to analyse intensities from the two channels as separate observations, but such models can be complex to use and the gain in efficiency over the log-ratio analysis is difficult to quantify. Mixed models yield test statistics for the null distributions can be specified only approximately, and some approaches do not borrow strength between genes. This article reformulates the mixed model to clarify the relationship with the traditional log-ratio analysis, to facilitate information borrowing between genes, and to obtain an exact distributional theory for the resulting test statistics. The mixed model is transformed to operate on the M-values and A-values (average log-expression for each spot) instead of on the log-expression values. The log-ratio analysis is shown to ignore information contained in the A-values. The relative efficiency of the log-ratio analysis is shown to depend on the size of the intraspot correlation. A new separate channel analysis method is proposed that assumes a constant intra-spot correlation coefficient across all genes. This approach permits the mixed model to be transformed into an ordinary linear model, allowing the data analysis to use a well-understood empirical Bayes analysis pipeline for linear modeling of microarray data. This yields statistically powerful test statistics that have an exact distributional theory. The log-ratio, mixed model and common correlation methods are compared using three case studies. The results show that separate channel analyses that borrow strength between genes are more powerful than log-ratio analyses. The common correlation analysis is the most powerful of all. The common correlation method proposed in this article for separate-channel analysis of two-channel microarray data is no more difficult to apply in practice than the traditional log-ratio analysis. It provides an intuitive and powerful means to conduct analyses and make comparisons that might otherwise not be possible.

Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

ERIC Educational Resources Information Center

Tra, Yolande V.; Evans, Irene M.

2010-01-01

"BIO2010" put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on…

Immunological Targeting of Tumor Initiating Prostate Cancer Cells

DTIC Science & Technology

2014-10-01

clinically using well-accepted immuno-competent animal models. 2) Keywords: Prostate Cancer, Lymphocyte, Vaccine, Antibody 3) Overall Project Summary...castrate animals . Task 1: Identify and verify antigenic targets from CAstrate Resistant Luminal Epithelial Cells (CRLEC) (months 1-16... animals per group will be processed to derive sufficient RNA for microarray analysis; the experiment will be repeated x 3. Microarray analysis will

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andersen, G.L.; He, Z.; DeSantis, T.Z.

Microarrays have proven to be a useful and high-throughput method to provide targeted DNA sequence information for up to many thousands of specific genetic regions in a single test. A microarray consists of multiple DNA oligonucleotide probes that, under high stringency conditions, hybridize only to specific complementary nucleic acid sequences (targets). A fluorescent signal indicates the presence and, in many cases, the abundance of genetic regions of interest. In this chapter we will look at how microarrays are used in microbial ecology, especially with the recent increase in microbial community DNA sequence data. Of particular interest to microbial ecologists, phylogeneticmore » microarrays are used for the analysis of phylotypes in a community and functional gene arrays are used for the analysis of functional genes, and, by inference, phylotypes in environmental samples. A phylogenetic microarray that has been developed by the Andersen laboratory, the PhyloChip, will be discussed as an example of a microarray that targets the known diversity within the 16S rRNA gene to determine microbial community composition. Using multiple, confirmatory probes to increase the confidence of detection and a mismatch probe for every perfect match probe to minimize the effect of cross-hybridization by non-target regions, the PhyloChip is able to simultaneously identify any of thousands of taxa present in an environmental sample. The PhyloChip is shown to reveal greater diversity within a community than rRNA gene sequencing due to the placement of the entire gene product on the microarray compared with the analysis of up to thousands of individual molecules by traditional sequencing methods. A functional gene array that has been developed by the Zhou laboratory, the GeoChip, will be discussed as an example of a microarray that dynamically identifies functional activities of multiple members within a community. The recent version of GeoChip contains more than 24,000 50mer oligonucleotide probes and covers more than 10,000 gene sequences in 150 gene categories involved in carbon, nitrogen, sulfur, and phosphorus cycling, metal resistance and reduction, and organic contaminant degradation. GeoChip can be used as a generic tool for microbial community analysis, and also link microbial community structure to ecosystem functioning. Examples of the application of both arrays in different environmental samples will be described in the two subsequent sections.« less

MAGMA: analysis of two-channel microarrays made easy.

PubMed

Rehrauer, Hubert; Zoller, Stefan; Schlapbach, Ralph

2007-07-01

The web application MAGMA provides a simple and intuitive interface to identify differentially expressed genes from two-channel microarray data. While the underlying algorithms are not superior to those of similar web applications, MAGMA is particularly user friendly and can be used without prior training. The user interface guides the novice user through the most typical microarray analysis workflow consisting of data upload, annotation, normalization and statistical analysis. It automatically generates R-scripts that document MAGMA's entire data processing steps, thereby allowing the user to regenerate all results in his local R installation. The implementation of MAGMA follows the model-view-controller design pattern that strictly separates the R-based statistical data processing, the web-representation and the application logic. This modular design makes the application flexible and easily extendible by experts in one of the fields: statistical microarray analysis, web design or software development. State-of-the-art Java Server Faces technology was used to generate the web interface and to perform user input processing. MAGMA's object-oriented modular framework makes it easily extendible and applicable to other fields and demonstrates that modern Java technology is also suitable for rather small and concise academic projects. MAGMA is freely available at www.magma-fgcz.uzh.ch.

Tomato Expression Database (TED): a suite of data presentation and analysis tools

PubMed Central

Fei, Zhangjun; Tang, Xuemei; Alba, Rob; Giovannoni, James

2006-01-01

The Tomato Expression Database (TED) includes three integrated components. The Tomato Microarray Data Warehouse serves as a central repository for raw gene expression data derived from the public tomato cDNA microarray. In addition to expression data, TED stores experimental design and array information in compliance with the MIAME guidelines and provides web interfaces for researchers to retrieve data for their own analysis and use. The Tomato Microarray Expression Database contains normalized and processed microarray data for ten time points with nine pair-wise comparisons during fruit development and ripening in a normal tomato variety and nearly isogenic single gene mutants impacting fruit development and ripening. Finally, the Tomato Digital Expression Database contains raw and normalized digital expression (EST abundance) data derived from analysis of the complete public tomato EST collection containing >150 000 ESTs derived from 27 different non-normalized EST libraries. This last component also includes tools for the comparison of tomato and Arabidopsis digital expression data. A set of query interfaces and analysis, and visualization tools have been developed and incorporated into TED, which aid users in identifying and deciphering biologically important information from our datasets. TED can be accessed at . PMID:16381976

Tomato Expression Database (TED): a suite of data presentation and analysis tools.

PubMed

Fei, Zhangjun; Tang, Xuemei; Alba, Rob; Giovannoni, James

2006-01-01

The Tomato Expression Database (TED) includes three integrated components. The Tomato Microarray Data Warehouse serves as a central repository for raw gene expression data derived from the public tomato cDNA microarray. In addition to expression data, TED stores experimental design and array information in compliance with the MIAME guidelines and provides web interfaces for researchers to retrieve data for their own analysis and use. The Tomato Microarray Expression Database contains normalized and processed microarray data for ten time points with nine pair-wise comparisons during fruit development and ripening in a normal tomato variety and nearly isogenic single gene mutants impacting fruit development and ripening. Finally, the Tomato Digital Expression Database contains raw and normalized digital expression (EST abundance) data derived from analysis of the complete public tomato EST collection containing >150,000 ESTs derived from 27 different non-normalized EST libraries. This last component also includes tools for the comparison of tomato and Arabidopsis digital expression data. A set of query interfaces and analysis, and visualization tools have been developed and incorporated into TED, which aid users in identifying and deciphering biologically important information from our datasets. TED can be accessed at http://ted.bti.cornell.edu.

mRNA expression profiling of laser microbeam microdissected cells from slender embryonic structures.

PubMed

Scheidl, Stefan J; Nilsson, Sven; Kalén, Mattias; Hellström, Mats; Takemoto, Minoru; Håkansson, Joakim; Lindahl, Per

2002-03-01

Microarray hybridization has rapidly evolved as an important tool for genomic studies and studies of gene regulation at the transcriptome level. Expression profiles from homogenous samples such as yeast and mammalian cell cultures are currently extending our understanding of biology, whereas analyses of multicellular organisms are more difficult because of tissue complexity. The combination of laser microdissection, RNA amplification, and microarray hybridization has the potential to provide expression profiles from selected populations of cells in vivo. In this article, we present and evaluate an experimental procedure for global gene expression analysis of slender embryonic structures using laser microbeam microdissection and laser pressure catapulting. As a proof of principle, expression profiles from 1000 cells in the mouse embryonic (E9.5) dorsal aorta were generated and compared with profiles for captured mesenchymal cells located one cell diameter further away from the aortic lumen. A number of genes were overexpressed in the aorta, including 11 previously known markers for blood vessels. Among the blood vessel markers were endoglin, tie-2, PDGFB, and integrin-beta1, that are important regulators of blood vessel formation. This demonstrates that microarray analysis of laser microbeam micro-dissected cells is sufficiently sensitive for identifying genes with regulative functions.

Development and application of a microarray meter tool to optimize microarray experiments

PubMed Central

Rouse, Richard JD; Field, Katrine; Lapira, Jennifer; Lee, Allen; Wick, Ivan; Eckhardt, Colleen; Bhasker, C Ramana; Soverchia, Laura; Hardiman, Gary

2008-01-01

Background Successful microarray experimentation requires a complex interplay between the slide chemistry, the printing pins, the nucleic acid probes and targets, and the hybridization milieu. Optimization of these parameters and a careful evaluation of emerging slide chemistries are a prerequisite to any large scale array fabrication effort. We have developed a 'microarray meter' tool which assesses the inherent variations associated with microarray measurement prior to embarking on large scale projects. Findings The microarray meter consists of nucleic acid targets (reference and dynamic range control) and probe components. Different plate designs containing identical probe material were formulated to accommodate different robotic and pin designs. We examined the variability in probe quality and quantity (as judged by the amount of DNA printed and remaining post-hybridization) using three robots equipped with capillary printing pins. Discussion The generation of microarray data with minimal variation requires consistent quality control of the (DNA microarray) manufacturing and experimental processes. Spot reproducibility is a measure primarily of the variations associated with printing. The microarray meter assesses array quality by measuring the DNA content for every feature. It provides a post-hybridization analysis of array quality by scoring probe performance using three metrics, a) a measure of variability in the signal intensities, b) a measure of the signal dynamic range and c) a measure of variability of the spot morphologies. PMID:18710498

Clonal diversity analysis using SNP microarray: a new prognostic tool for chronic lymphocytic leukemia.

PubMed

Zhang, Linsheng; Znoyko, Iya; Costa, Luciano J; Conlin, Laura K; Daber, Robert D; Self, Sally E; Wolff, Daynna J

2011-12-01

Chronic lymphocytic leukemia (CLL) is a clinically heterogeneous disease. The methods currently used for monitoring CLL and determining conditions for treatment are limited in their ability to predict disease progression, patient survival, and response to therapy. Although clonal diversity and the acquisition of new chromosomal abnormalities during the disease course (clonal evolution) have been associated with disease progression, their prognostic potential has been underappreciated because cytogenetic and fluorescence in situ hybridization (FISH) studies have a restricted ability to detect genomic abnormalities and clonal evolution. We hypothesized that whole genome analysis using high resolution single nucleotide polymorphism (SNP) microarrays would be useful to detect diversity and infer clonal evolution to offer prognostic information. In this study, we used the Infinium Omni1 BeadChip (Illumina, San Diego, CA) array for the analysis of genetic variation and percent mosaicism in 25 non-selected CLL patients to explore the prognostic value of the assessment of clonal diversity in patients with CLL. We calculated the percentage of mosaicism for each abnormality by applying a mathematical algorithm to the genotype frequency data and by manual determination using the Simulated DNA Copy Number (SiDCoN) tool, which was developed from a computer model of mosaicism. At least one genetic abnormality was identified in each case, and the SNP data was 98% concordant with FISH results. Clonal diversity, defined as the presence of two or more genetic abnormalities with differing percentages of mosaicism, was observed in 12 patients (48%), and the diversity correlated with the disease stage. Clonal diversity was present in most cases of advanced disease (Rai stages III and IV) or those with previous treatment, whereas 9 of 13 patients without detected clonal diversity were asymptomatic or clinically stable. In conclusion, SNP microarray studies with simultaneous evaluation of genomic alterations and mosaic distribution of clones can be used to assess apparent clonal evolution via analysis of clonal diversity. Since clonal evolution in CLL is strongly correlated with disease progression, whole genome SNP microarray analysis provides a new comprehensive and reliable prognostic tool for CLL patients. Copyright © 2011 Elsevier Inc. All rights reserved.

A detailed transcript-level probe annotation reveals alternative splicing based microarray platform differences

PubMed Central

Lee, Joseph C; Stiles, David; Lu, Jun; Cam, Margaret C

2007-01-01

Background Microarrays are a popular tool used in experiments to measure gene expression levels. Improving the reproducibility of microarray results produced by different chips from various manufacturers is important to create comparable and combinable experimental results. Alternative splicing has been cited as a possible cause of differences in expression measurements across platforms, though no study to this point has been conducted to show its influence in cross-platform differences. Results Using probe sequence data, a new microarray probe/transcript annotation was created based on the AceView Aug05 release that allowed for the categorization of genes based on their expression measurements' susceptibility to alternative splicing differences across microarray platforms. Examining gene expression data from multiple platforms in light of the new categorization, genes unsusceptible to alternative splicing differences showed higher signal agreement than those genes most susceptible to alternative splicing differences. The analysis gave rise to a different probe-level visualization method that can highlight probe differences according to transcript specificity. Conclusion The results highlight the need for detailed probe annotation at the transcriptome level. The presence of alternative splicing within a given sample can affect gene expression measurements and is a contributing factor to overall technical differences across platforms. PMID:17708771

Discrimination of Influenza Infection (A/2009 H1N1) from Prior Exposure by Antibody Protein Microarray Analysis

PubMed Central

te Beest, Dennis; de Bruin, Erwin; Imholz, Sandra; Wallinga, Jacco; Teunis, Peter; Koopmans, Marion; van Boven, Michiel

2014-01-01

Reliable discrimination of recent influenza A infection from previous exposure using hemagglutination inhibition (HI) or virus neutralization tests is currently not feasible. This is due to low sensitivity of the tests and the interference of antibody responses generated by previous infections. Here we investigate the diagnostic characteristics of a newly developed antibody (HA1) protein microarray using data from cross-sectional serological studies carried out before and after the pandemic of 2009. The data are analysed by mixture models, providing a probabilistic classification of sera (susceptible, prior-exposed, recently infected). Estimated sensitivity and specificity for identifying A/2009 infections are low using HI (66% and 51%), and high when using A/2009 microarray data alone or together with A/1918 microarray data (96% and 95%). As a heuristic, a high A/2009 to A/1918 antibody ratio (>1.05) is indicative of recent infection, while a low ratio is indicative of a pre-existing response, even if the A/2009 titer is high. We conclude that highly sensitive and specific classification of individual sera is possible using the protein microarray, thereby enabling precise estimation of age-specific infection attack rates in the population even if sample sizes are small. PMID:25405997

Analytical Protein Microarrays: Advancements Towards Clinical Applications

PubMed Central

Sauer, Ursula

2017-01-01

Protein microarrays represent a powerful technology with the potential to serve as tools for the detection of a broad range of analytes in numerous applications such as diagnostics, drug development, food safety, and environmental monitoring. Key features of analytical protein microarrays include high throughput and relatively low costs due to minimal reagent consumption, multiplexing, fast kinetics and hence measurements, and the possibility of functional integration. So far, especially fundamental studies in molecular and cell biology have been conducted using protein microarrays, while the potential for clinical, notably point-of-care applications is not yet fully utilized. The question arises what features have to be implemented and what improvements have to be made in order to fully exploit the technology. In the past we have identified various obstacles that have to be overcome in order to promote protein microarray technology in the diagnostic field. Issues that need significant improvement to make the technology more attractive for the diagnostic market are for instance: too low sensitivity and deficiency in reproducibility, inadequate analysis time, lack of high-quality antibodies and validated reagents, lack of automation and portable instruments, and cost of instruments necessary for chip production and read-out. The scope of the paper at hand is to review approaches to solve these problems. PMID:28146048

Profiling Humoral Immune Responses to Clostridium difficile-Specific Antigens by Protein Microarray Analysis.

PubMed

Negm, Ola H; Hamed, Mohamed R; Dilnot, Elizabeth M; Shone, Clifford C; Marszalowska, Izabela; Lynch, Mark; Loscher, Christine E; Edwards, Laura J; Tighe, Patrick J; Wilcox, Mark H; Monaghan, Tanya M

2015-09-01

Clostridium difficile is an anaerobic, Gram-positive, and spore-forming bacterium that is the leading worldwide infective cause of hospital-acquired and antibiotic-associated diarrhea. Several studies have reported associations between humoral immunity and the clinical course of C. difficile infection (CDI). Host humoral immune responses are determined using conventional enzyme-linked immunosorbent assay (ELISA) techniques. Herein, we report the first use of a novel protein microarray assay to determine systemic IgG antibody responses against a panel of highly purified C. difficile-specific antigens, including native toxins A and B (TcdA and TcdB, respectively), recombinant fragments of toxins A and B (TxA4 and TxB4, respectively), ribotype-specific surface layer proteins (SLPs; 001, 002, 027), and control proteins (tetanus toxoid and Candida albicans). Microarrays were probed with sera from a total of 327 individuals with CDI, cystic fibrosis without diarrhea, and healthy controls. For all antigens, precision profiles demonstrated <10% coefficient of variation (CV). Significant correlation was observed between microarray and ELISA in the quantification of antitoxin A and antitoxin B IgG. These results indicate that microarray is a suitable assay for defining humoral immune responses to C. difficile protein antigens and may have potential advantages in throughput, convenience, and cost. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Profiling Humoral Immune Responses to Clostridium difficile-Specific Antigens by Protein Microarray Analysis

PubMed Central

Negm, Ola H.; Hamed, Mohamed R.; Dilnot, Elizabeth M.; Shone, Clifford C.; Marszalowska, Izabela; Lynch, Mark; Loscher, Christine E.; Edwards, Laura J.; Tighe, Patrick J.; Wilcox, Mark H.

2015-01-01

Clostridium difficile is an anaerobic, Gram-positive, and spore-forming bacterium that is the leading worldwide infective cause of hospital-acquired and antibiotic-associated diarrhea. Several studies have reported associations between humoral immunity and the clinical course of C. difficile infection (CDI). Host humoral immune responses are determined using conventional enzyme-linked immunosorbent assay (ELISA) techniques. Herein, we report the first use of a novel protein microarray assay to determine systemic IgG antibody responses against a panel of highly purified C. difficile-specific antigens, including native toxins A and B (TcdA and TcdB, respectively), recombinant fragments of toxins A and B (TxA4 and TxB4, respectively), ribotype-specific surface layer proteins (SLPs; 001, 002, 027), and control proteins (tetanus toxoid and Candida albicans). Microarrays were probed with sera from a total of 327 individuals with CDI, cystic fibrosis without diarrhea, and healthy controls. For all antigens, precision profiles demonstrated <10% coefficient of variation (CV). Significant correlation was observed between microarray and ELISA in the quantification of antitoxin A and antitoxin B IgG. These results indicate that microarray is a suitable assay for defining humoral immune responses to C. difficile protein antigens and may have potential advantages in throughput, convenience, and cost. PMID:26178385

MASQOT: a method for cDNA microarray spot quality control

PubMed Central

Bylesjö, Max; Eriksson, Daniel; Sjödin, Andreas; Sjöström, Michael; Jansson, Stefan; Antti, Henrik; Trygg, Johan

2005-01-01

Background cDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more. Results A novel methodology for cDNA microarray spot quality control is outlined. Multivariate discriminant analysis was used to assess spot quality based on existing and novel descriptors. The presented methodology displays high reproducibility and was found superior in identifying unreliable data compared to other evaluated methodologies. Conclusion The proposed methodology for cDNA microarray spot quality control generates non-discrete values of spot quality which can be utilized as weights in subsequent analysis procedures as well as to discard spots of undesired quality using the suggested threshold values. The MASQOT approach provides a consistent assessment of spot quality and can be considered an alternative to the labor-intensive manual quality assessment process. PMID:16223442

mRNA-Seq and microarray development for the Grooved carpet shell clam, Ruditapes decussatus: a functional approach to unravel host -parasite interaction

PubMed Central

2013-01-01

Background The Grooved Carpet shell clam Ruditapes decussatus is the autochthonous European clam and the most appreciated from a gastronomic and economic point of view. The production is in decline due to several factors such as Perkinsiosis and habitat invasion and competition by the introduced exotic species, the manila clam Ruditapes philippinarum. After we sequenced R. decussatus transcriptome we have designed an oligo microarray capable of contributing to provide some clues on molecular response of the clam to Perkinsiosis. Results A database consisting of 41,119 unique transcripts was constructed, of which 12,479 (30.3%) were annotated by similarity. An oligo-DNA microarray platform was then designed and applied to profile gene expression in R. decussatus heavily infected by Perkinsus olseni. Functional annotation of differentially expressed genes between those two conditionswas performed by gene set enrichment analysis. As expected, microarrays unveil genes related with stress/infectious agents such as hydrolases, proteases and others. The extensive role of innate immune system was also analyzed and effect of parasitosis upon expression of important molecules such as lectins reviewed. Conclusions This study represents a first attempt to characterize Ruditapes decussatus transcriptome, an important marine resource for the European aquaculture. The trancriptome sequencing and consequent annotation will increase the available tools and resources for this specie, introducing the possibility of high throughput experiments such as microarrays analysis. In this specific case microarray approach was used to unveil some important aspects of host-parasite interaction between the Carpet shell clam and Perkinsus, two non-model species, highlighting some genes associated with this interaction. Ample information was obtained to identify biological processes significantly enriched among differentially expressed genes in Perkinsus infected versus non-infected gills. An overview on the genes related with the immune system on R. decussatus transcriptome is also reported. PMID:24168212

mRNA-Seq and microarray development for the Grooved Carpet shell clam, Ruditapes decussatus: a functional approach to unravel host-parasite interaction.

PubMed

Leite, Ricardo B; Milan, Massimo; Coppe, Alessandro; Bortoluzzi, Stefania; dos Anjos, António; Reinhardt, Richard; Saavedra, Carlos; Patarnello, Tomaso; Cancela, M Leonor; Bargelloni, Luca

2013-10-29

The Grooved Carpet shell clam Ruditapes decussatus is the autochthonous European clam and the most appreciated from a gastronomic and economic point of view. The production is in decline due to several factors such as Perkinsiosis and habitat invasion and competition by the introduced exotic species, the manila clam Ruditapes philippinarum. After we sequenced R. decussatus transcriptome we have designed an oligo microarray capable of contributing to provide some clues on molecular response of the clam to Perkinsiosis. A database consisting of 41,119 unique transcripts was constructed, of which 12,479 (30.3%) were annotated by similarity. An oligo-DNA microarray platform was then designed and applied to profile gene expression in R. decussatus heavily infected by Perkinsus olseni. Functional annotation of differentially expressed genes between those two conditionswas performed by gene set enrichment analysis. As expected, microarrays unveil genes related with stress/infectious agents such as hydrolases, proteases and others. The extensive role of innate immune system was also analyzed and effect of parasitosis upon expression of important molecules such as lectins reviewed. This study represents a first attempt to characterize Ruditapes decussatus transcriptome, an important marine resource for the European aquaculture. The trancriptome sequencing and consequent annotation will increase the available tools and resources for this specie, introducing the possibility of high throughput experiments such as microarrays analysis. In this specific case microarray approach was used to unveil some important aspects of host-parasite interaction between the Carpet shell clam and Perkinsus, two non-model species, highlighting some genes associated with this interaction. Ample information was obtained to identify biological processes significantly enriched among differentially expressed genes in Perkinsus infected versus non-infected gills. An overview on the genes related with the immune system on R. decussatus transcriptome is also reported.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Intra-Platform Repeatability and Inter-Platform Comparability of MicroRNA Microarray Technology

PubMed Central

Sato, Fumiaki; Tsuchiya, Soken; Terasawa, Kazuya; Tsujimoto, Gozoh

2009-01-01

Over the last decade, DNA microarray technology has provided a great contribution to the life sciences. The MicroArray Quality Control (MAQC) project demonstrated the way to analyze the expression microarray. Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. Thus, we compared repeatability and comparability of five different microRNA microarray platforms (Agilent, Ambion, Exiqon, Invitrogen and Toray) using 309 microRNAs probes, and the Taqman microRNA system using 142 microRNA probes. This study demonstrated that microRNA microarray has high intra-platform repeatability and comparability to quantitative RT-PCR of microRNA. Among the five platforms, Agilent and Toray array showed relatively better performances than the others. However, the current lineup of commercially available microRNA microarray systems fails to show good inter-platform concordance, probably because of lack of an adequate normalization method and severe divergence in stringency of detection call criteria between different platforms. This study provided the basic information about the performance and the problems specific to the current microRNA microarray systems. PMID:19436744

Addressable droplet microarrays for single cell protein analysis.

PubMed

Salehi-Reyhani, Ali; Burgin, Edward; Ces, Oscar; Willison, Keith R; Klug, David R

2014-11-07

Addressable droplet microarrays are potentially attractive as a way to achieve miniaturised, reduced volume, high sensitivity analyses without the need to fabricate microfluidic devices or small volume chambers. We report a practical method for producing oil-encapsulated addressable droplet microarrays which can be used for such analyses. To demonstrate their utility, we undertake a series of single cell analyses, to determine the variation in copy number of p53 proteins in cells of a human cancer cell line.

Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset

PubMed Central

2012-01-01

Background We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these “differentially expressed GO terms” and have named the algorithm “matrix-assisted identification method of differentially expressed GO terms” (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms. Findings We combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson’s correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. Conclusions MIMGO is a reliable method to identify differentially expressed GO terms comprehensively. PMID:23232071

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline

PubMed Central

2013-01-01

Background As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. Results We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS A : DE genes with non-zero effect sizes in all studies, (2) HS B : DE genes with non-zero effect sizes in one or more studies and (3) HS r : DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. Conclusions The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS A , HS B , and HS r ). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author’s publication website. PMID:24359104

Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline.

PubMed

Chang, Lun-Ching; Lin, Hui-Min; Sibille, Etienne; Tseng, George C

2013-12-21

As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS(A): DE genes with non-zero effect sizes in all studies, (2) HS(B): DE genes with non-zero effect sizes in one or more studies and (3) HS(r): DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS(A), HS(B), and HS(r)). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website.

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Gene expression analysis of rheumatoid arthritis synovial lining regions by cDNA microarray combined with laser microdissection: up-regulation of inflammation-associated STAT1, IRF1, CXCL9, CXCL10, and CCL5

PubMed Central

Yoshida, S; Arakawa, F; Higuchi, F; Ishibashi, Y; Goto, M; Sugita, Y; Nomura, Y; Niino, D; Shimizu, K; Aoki, R; Hashikawa, K; Kimura, Y; Yasuda, K; Tashiro, K; Kuhara, S; Nagata, K; Ohshima, K

2012-01-01

Objectives The main histological change in rheumatoid arthritis (RA) is the villous proliferation of synovial lining cells, an important source of cytokines and chemokines, which are associated with inflammation. The aim of this study was to evaluate gene expression in the microdissected synovial lining cells of RA patients, using those of osteoarthritis (OA) patients as the control. Methods Samples were obtained during total joint replacement from 11 RA and five OA patients. Total RNA from the synovial lining cells was derived from selected specimens by laser microdissection (LMD) for subsequent cDNA microarray analysis. In addition, the expression of significant genes was confirmed immunohistochemically. Results The 14 519 genes detected by cDNA microarray were used to compare gene expression levels in synovial lining cells from RA with those from OA patients. Cluster analysis indicated that RA cells, including low- and high-expression subgroups, and OA cells were stored in two main clusters. The molecular activity of RA was statistically consistent with its clinical and histological activity. Expression levels of signal transducer and activator of transcription 1 (STAT1), interferon regulatory factor 1 (IRF1), and the chemokines CXCL9, CXCL10, and CCL5 were statistically significantly higher in the synovium of RA than in that of OA. Immunohistochemically, the lining synovium of RA, but not that of OA, clearly expressed STAT1, IRF1, and chemokines, as was seen in microarray analysis combined with LMD. Conclusions Our findings indicate an important role for lining synovial cells in the inflammatory and proliferative processes of RA. Further understanding of the local signalling in structural components is important in rheumatology. PMID:22401175

Microarray-based analysis of gene expression in lycopersicon esculentum seedling roots in response to cadmium, chromium, mercury, and lead.

PubMed

Hou, Jing; Liu, Xinhui; Wang, Juan; Zhao, Shengnan; Cui, Baoshan

2015-02-03

The effects of heavy metals in agricultural soils have received special attention due to their potential for accumulation in crops, which can affect species at all trophic levels. Therefore, there is a critical need for reliable bioassays for assessing risk levels due to heavy metals in agricultural soil. In the present study, we used microarrays to investigate changes in gene expression of Lycopersicon esculentum in response to Cd-, Cr-, Hg-, or Pb-spiked soil. Exposure to (1)/10 median lethal concentrations (LC50) of Cd, Cr, Hg, or Pb for 7 days resulted in expression changes in 29 Cd-specific, 58 Cr-specific, 192 Hg-specific and 864 Pb-specific genes as determined by microarray analysis, whereas conventional morphological and physiological bioassays did not reveal any toxicant stresses. Hierarchical clustering analysis showed that the characteristic gene expression profiles induced by Cd, Cr, Hg, and Pb were distinct from not only the control but also one another. Furthermore, a total of three genes related to "ion transport" for Cd, 14 genes related to "external encapsulating structure organization", "reproductive developmental process", "lipid metabolic process" and "response to stimulus" for Cr, 11 genes related to "cellular metabolic process" and "cellular response to stimulus" for Hg, 78 genes related to 20 biological processes (e.g., DNA metabolic process, monosaccharide catabolic process, cell division) for Pb were identified and selected as their potential biomarkers. These findings demonstrated that microarray-based analysis of Lycopersicon esculentum was a sensitive tool for the early detection of potential toxicity of heavy metals in agricultural soil, as well as an effective tool for identifying the heavy metal-specific genes, which should be useful for assessing risk levels due to heavy metals in agricultural soil.

Histological staining methods preparatory to laser capture microdissection significantly affect the integrity of the cellular RNA.

PubMed

Wang, Hongyang; Owens, James D; Shih, Joanna H; Li, Ming-Chung; Bonner, Robert F; Mushinski, J Frederic

2006-04-27

Gene expression profiling by microarray analysis of cells enriched by laser capture microdissection (LCM) faces several technical challenges. Frozen sections yield higher quality RNA than paraffin-imbedded sections, but even with frozen sections, the staining methods used for histological identification of cells of interest could still damage the mRNA in the cells. To study the contribution of staining methods to degradation of results from gene expression profiling of LCM samples, we subjected pellets of the mouse plasma cell tumor cell line TEPC 1165 to direct RNA extraction and to parallel frozen sectioning for LCM and subsequent RNA extraction. We used microarray hybridization analysis to compare gene expression profiles of RNA from cell pellets with gene expression profiles of RNA from frozen sections that had been stained with hematoxylin and eosin (H&E), Nissl Stain (NS), and for immunofluorescence (IF) as well as with the plasma cell-revealing methyl green pyronin (MGP) stain. All RNAs were amplified with two rounds of T7-based in vitro transcription and analyzed by two-color expression analysis on 10-K cDNA microarrays. The MGP-stained samples showed the least introduction of mRNA loss, followed by H&E and immunofluorescence. Nissl staining was significantly more detrimental to gene expression profiles, presumably owing to an aqueous step in which RNA may have been damaged by endogenous or exogenous RNAases. RNA damage can occur during the staining steps preparatory to laser capture microdissection, with the consequence of loss of representation of certain genes in microarray hybridization analysis. Inclusion of RNAase inhibitor in aqueous staining solutions appears to be important in protecting RNA from loss of gene transcripts.

Histological staining methods preparatory to laser capture microdissection significantly affect the integrity of the cellular RNA

PubMed Central

Wang, Hongyang; Owens, James D; Shih, Joanna H; Li, Ming-Chung; Bonner, Robert F; Mushinski, J Frederic

2006-01-01

Background Gene expression profiling by microarray analysis of cells enriched by laser capture microdissection (LCM) faces several technical challenges. Frozen sections yield higher quality RNA than paraffin-imbedded sections, but even with frozen sections, the staining methods used for histological identification of cells of interest could still damage the mRNA in the cells. To study the contribution of staining methods to degradation of results from gene expression profiling of LCM samples, we subjected pellets of the mouse plasma cell tumor cell line TEPC 1165 to direct RNA extraction and to parallel frozen sectioning for LCM and subsequent RNA extraction. We used microarray hybridization analysis to compare gene expression profiles of RNA from cell pellets with gene expression profiles of RNA from frozen sections that had been stained with hematoxylin and eosin (H&E), Nissl Stain (NS), and for immunofluorescence (IF) as well as with the plasma cell-revealing methyl green pyronin (MGP) stain. All RNAs were amplified with two rounds of T7-based in vitro transcription and analyzed by two-color expression analysis on 10-K cDNA microarrays. Results The MGP-stained samples showed the least introduction of mRNA loss, followed by H&E and immunofluorescence. Nissl staining was significantly more detrimental to gene expression profiles, presumably owing to an aqueous step in which RNA may have been damaged by endogenous or exogenous RNAases. Conclusion RNA damage can occur during the staining steps preparatory to laser capture microdissection, with the consequence of loss of representation of certain genes in microarray hybridization analysis. Inclusion of RNAase inhibitor in aqueous staining solutions appears to be important in protecting RNA from loss of gene transcripts. PMID:16643667

Performance analysis of clustering techniques over microarray data: A case study

NASA Astrophysics Data System (ADS)

Dash, Rasmita; Misra, Bijan Bihari

2018-03-01

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

Comparison of innovative molecular approaches and standard spore assays for assessment of surface cleanliness.

PubMed

Cooper, Moogega; La Duc, Myron T; Probst, Alexander; Vaishampayan, Parag; Stam, Christina; Benardini, James N; Piceno, Yvette M; Andersen, Gary L; Venkateswaran, Kasthuri

2011-08-01

A bacterial spore assay and a molecular DNA microarray method were compared for their ability to assess relative cleanliness in the context of bacterial abundance and diversity on spacecraft surfaces. Colony counts derived from the NASA standard spore assay were extremely low for spacecraft surfaces. However, the PhyloChip generation 3 (G3) DNA microarray resolved the genetic signatures of a highly diverse suite of microorganisms in the very same sample set. Samples completely devoid of cultivable spores were shown to harbor the DNA of more than 100 distinct microbial phylotypes. Furthermore, samples with higher numbers of cultivable spores did not necessarily give rise to a greater microbial diversity upon analysis with the DNA microarray. The findings of this study clearly demonstrated that there is not a statistically significant correlation between the cultivable spore counts obtained from a sample and the degree of bacterial diversity present. Based on these results, it can be stated that validated state-of-the-art molecular techniques, such as DNA microarrays, can be utilized in parallel with classical culture-based methods to further describe the cleanliness of spacecraft surfaces.

Simplified Microarray Technique for Identifying mRNA in Rare Samples

NASA Technical Reports Server (NTRS)

Almeida, Eduardo; Kadambi, Geeta

2007-01-01

Two simplified methods of identifying messenger ribonucleic acid (mRNA), and compact, low-power apparatuses to implement the methods, are at the proof-of-concept stage of development. These methods are related to traditional methods based on hybridization of nucleic acid, but whereas the traditional methods must be practiced in laboratory settings, these methods could be practiced in field settings. Hybridization of nucleic acid is a powerful technique for detection of specific complementary nucleic acid sequences, and is increasingly being used for detection of changes in gene expression in microarrays containing thousands of gene probes. A traditional microarray study entails at least the following six steps: 1. Purification of cellular RNA, 2. Amplification of complementary deoxyribonucleic acid [cDNA] by polymerase chain reaction (PCR), 3. Labeling of cDNA with fluorophores of Cy3 (a green cyanine dye) and Cy5 (a red cyanine dye), 4. Hybridization to a microarray chip, 5. Fluorescence scanning the array(s) with dual excitation wavelengths, and 6. Analysis of the resulting images. This six-step procedure must be performed in a laboratory because it requires bulky equipment.

RNAi targeting GPR4 influences HMEC-1 gene expression by microarray analysis

PubMed Central

Ren, Juan; Zhang, Yuelang; Cai, Hui; Ma, Hongbing; Zhao, Dongli; Zhang, Xiaozhi; Li, Zongfang; Wang, Shufeng; Wang, Jiangsheng; Liu, Rui; Li, Yi; Qian, Jiansheng; Wei, Hongxia; Niu, Liying; Liu, Yan; Xiao, Lisha; Ding, Muyang; Jiang, Shiwen

2014-01-01

G-protein coupled receptor 4 (GPR4) belongs to a protein family comprised of 3 closely related G protein-coupled receptors. Recent studies have shown that GPR4 plays important roles in angiogenesis, proton sensing, and regulating tumor cells as an oncogenic gene. How GPR4 conducts its functions? Rare has been known. In order to detect the genes related to GPR4, microarray technology was employed. GPR4 is highly expressed in human vascular endothelial cell HMEC-1. Small interfering RNA against GPR4 was used to knockdown GPR4 expression in HMEC-1. Then RNA from the GPR4 knockdown cells and control cells were analyzed through genome microarray. Microarray results shown that among the whole genes and expressed sequence tags, 447 differentially expressed genes were identified, containing 318 up-regulated genes and 129 down-regulated genes. These genes whose expression dramatically changed may be involved in the GPR4 functions. These genes were related to cell apoptosis, cytoskeleton and signal transduction, cell proliferation, differentiation and cell-cycle regulation, gene transcription and translation and cell material and energy metabolism. PMID:24753754

Spotting and validation of a genome wide oligonucleotide chip with duplicate measurement of each gene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomassen, Mads; Skov, Vibe; Eiriksdottir, Freyja

2006-06-16

The quality of DNA microarray based gene expression data relies on the reproducibility of several steps in a microarray experiment. We have developed a spotted genome wide microarray chip with oligonucleotides printed in duplicate in order to minimise undesirable biases, thereby optimising detection of true differential expression. The validation study design consisted of an assessment of the microarray chip performance using the MessageAmp and FairPlay labelling kits. Intraclass correlation coefficient (ICC) was used to demonstrate that MessageAmp was significantly more reproducible than FairPlay. Further examinations with MessageAmp revealed the applicability of the system. The linear range of the chips wasmore » three orders of magnitude, the precision was high, as 95% of measurements deviated less than 1.24-fold from the expected value, and the coefficient of variation for relative expression was 13.6%. Relative quantitation was more reproducible than absolute quantitation and substantial reduction of variance was attained with duplicate spotting. An analysis of variance (ANOVA) demonstrated no significant day-to-day variation.« less

Bioinformatic analysis of the effects and mechanisms of decitabine and cytarabine on acute myeloid leukemia

PubMed Central

Zhou, Shiyong; Liu, Pengfei; Zhang, Huilai

2017-01-01

Acute myeloid leukemia (AML) is a frequently occurring malignant disease of the blood and may result from a variety of genetic disorders. The present study aimed to identify the underlying mechanisms associated with the therapeutic effects of decitabine and cytarabine on AML, using microarray analysis. The microarray datasets GSE40442 and GSE40870 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially methylated sites were identified in AML cells treated with decitabine compared with those treated with cytarabine via the Linear Models for Microarray Data package, following data pre-processing. Gene Ontology (GO) analysis of DEGs was performed using the Database for Annotation, Visualization and Integrated Analysis Discovery. Genes corresponding to the differentially methylated sites were obtained using the annotation package of the methylation microarray platform. The overlapping genes were identified, which exhibited the opposite variation trend between gene expression and DNA methylation. Important transcription factor (TF)-gene pairs were screened out, and a regulated network subsequently constructed. A total of 190 DEGs and 540 differentially methylated sites were identified in AML cells treated with decitabine compared with those treated with cytarabine. A total of 36 GO terms of DEGs were enriched, including nucleosomes, protein-DNA complexes and the nucleosome assembly. The 540 differentially methylated sites were located on 240 genes, including the acid-repeat containing protein (ACRC) gene that was additionally differentially expressed. In addition, 60 TF pairs and overlapped methylated sites, and 140 TF-pairs and DEGs were screened out. The regulated network included 68 nodes and 140 TF-gene pairs. The present study identified various genes including ACRC and proliferating cell nuclear antigen, in addition to various TFs, including TATA-box binding protein associated factor 1 and CCCTC-binding factor, which may be potential therapeutic targets of AML. PMID:28498449

Bioinformatic analysis of the effects and mechanisms of decitabine and cytarabine on acute myeloid leukemia.

PubMed

Zhou, Shiyong; Liu, Pengfei; Zhang, Huilai

2017-07-01

Acute myeloid leukemia (AML) is a frequently occurring malignant disease of the blood and may result from a variety of genetic disorders. The present study aimed to identify the underlying mechanisms associated with the therapeutic effects of decitabine and cytarabine on AML, using microarray analysis. The microarray datasets GSE40442 and GSE40870 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially methylated sites were identified in AML cells treated with decitabine compared with those treated with cytarabine via the Linear Models for Microarray Data package, following data pre‑processing. Gene Ontology (GO) analysis of DEGs was performed using the Database for Annotation, Visualization and Integrated Analysis Discovery. Genes corresponding to the differentially methylated sites were obtained using the annotation package of the methylation microarray platform. The overlapping genes were identified, which exhibited the opposite variation trend between gene expression and DNA methylation. Important transcription factor (TF)‑gene pairs were screened out, and a regulated network subsequently constructed. A total of 190 DEGs and 540 differentially methylated sites were identified in AML cells treated with decitabine compared with those treated with cytarabine. A total of 36 GO terms of DEGs were enriched, including nucleosomes, protein‑DNA complexes and the nucleosome assembly. The 540 differentially methylated sites were located on 240 genes, including the acid‑repeat containing protein (ACRC) gene that was additionally differentially expressed. In addition, 60 TF pairs and overlapped methylated sites, and 140 TF‑pairs and DEGs were screened out. The regulated network included 68 nodes and 140 TF‑gene pairs. The present study identified various genes including ACRC and proliferating cell nuclear antigen, in addition to various TFs, including TATA‑box binding protein associated factor 1 and CCCTC‑binding factor, which may be potential therapeutic targets of AML.

Gene expression profile in cardiovascular disease and preeclampsia: a meta-analysis of the transcriptome based on raw data from human studies deposited in Gene Expression Omnibus.

PubMed

Sitras, V; Fenton, C; Acharya, G

2015-02-01

Cardiovascular disease (CVD) and preeclampsia (PE) share common clinical features. We aimed to identify common transcriptomic signatures involved in CVD and PE in humans. Meta-analysis of individual raw microarray data deposited in GEO, obtained from blood samples of patients with CVD versus controls and placental samples from women with PE versus healthy women with uncomplicated pregnancies. Annotation of cases versus control samples was taken directly from the microarray documentation. Genes that showed a significant differential expression in the majority of experiments were selected for subsequent analysis. Hypergeometric gene list analysis was performed using Bioconductor GOstats package. Bioinformatic analysis was performed in PANTHER. Seven studies in CVD and 5 studies in PE were eligible for meta-analysis. A total of 181 genes were found to be differentially expressed in microarray studies investigating gene expression in blood samples obtained from patients with CVD compared to controls and 925 genes were differentially expressed between preeclamptic and healthy placentas. Among these differentially expressed genes, 22 were common between CVD and PE. Bioinformatic analysis of these genes revealed oxidative stress, p-53 pathway feedback, inflammation mediated by chemokines and cytokines, interleukin signaling, B-cell activation, PDGF signaling, Wnt signaling, integrin signaling and Alzheimer disease pathways to be involved in the pathophysiology of both CVD and PE. Metabolism, development, response to stimulus, immune response and cell communication were the associated biologic processes in both conditions. Gene set enrichment analysis showed the following overlapping pathways between CVD and PE: TGF-β-signaling, apoptosis, graft-versus-host disease, allograft rejection, chemokine signaling, steroid hormone synthesis, type I and II diabetes mellitus, VEGF signaling, pathways in cancer, GNRH signaling, Huntingtons disease and Notch signaling. CVD and PE share same common traits in their gene expression profile indicating common pathways in their pathophysiology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Systematic validation and atomic force microscopy of non-covalent short oligonucleotide barcode microarrays.

PubMed

Cook, Michael A; Chan, Chi-Kin; Jorgensen, Paul; Ketela, Troy; So, Daniel; Tyers, Mike; Ho, Chi-Yip

2008-02-06

Molecular barcode arrays provide a powerful means to analyze cellular phenotypes in parallel through detection of short (20-60 base) unique sequence tags, or "barcodes", associated with each strain or clone in a collection. However, costs of current methods for microarray construction, whether by in situ oligonucleotide synthesis or ex situ coupling of modified oligonucleotides to the slide surface are often prohibitive to large-scale analyses. Here we demonstrate that unmodified 20mer oligonucleotide probes printed on conventional surfaces show comparable hybridization signals to covalently linked 5'-amino-modified probes. As a test case, we undertook systematic cell size analysis of the budding yeast Saccharomyces cerevisiae genome-wide deletion collection by size separation of the deletion pool followed by determination of strain abundance in size fractions by barcode arrays. We demonstrate that the properties of a 13K unique feature spotted 20 mer oligonucleotide barcode microarray compare favorably with an analogous covalently-linked oligonucleotide array. Further, cell size profiles obtained with the size selection/barcode array approach recapitulate previous cell size measurements of individual deletion strains. Finally, through atomic force microscopy (AFM), we characterize the mechanism of hybridization to unmodified barcode probes on the slide surface. These studies push the lower limit of probe size in genome-scale unmodified oligonucleotide microarray construction and demonstrate a versatile, cost-effective and reliable method for molecular barcode analysis.

Insights into the degradation capacities of Amycolatopsis tucumanensis DSM 45259 guided by microarray data.

PubMed

Bourguignon, Natalia; Bargiela, Rafael; Rojo, David; Chernikova, Tatyana N; de Rodas, Sara A López; García-Cantalejo, Jesús; Näther, Daniela J; Golyshin, Peter N; Barbas, Coral; Ferrero, Marcela; Ferrer, Manuel

2016-12-01

The analysis of catabolic capacities of microorganisms is currently often achieved by cultivation approaches and by the analysis of genomic or metagenomic datasets. Recently, a microarray system designed from curated key aromatic catabolic gene families and key alkane degradation genes was designed. The collection of genes in the microarray can be exploited to indicate whether a given microbe or microbial community is likely to be functionally connected with certain degradative phenotypes, without previous knowledge of genome data. Herein, this microarray was applied to capture new insights into the catabolic capacities of copper-resistant actinomycete Amycolatopsis tucumanensis DSM 45259. The array data support the presumptive ability of the DSM 45259 strain to utilize single alkanes (n-decane and n-tetradecane) and aromatics such as benzoate, phthalate and phenol as sole carbon sources, which was experimentally validated by cultivation and mass spectrometry. Interestingly, while in strain DSM 45259 alkB gene encoding an alkane hydroxylase is most likely highly similar to that found in other actinomycetes, the genes encoding benzoate 1,2-dioxygenase, phthalate 4,5-dioxygenase and phenol hydroxylase were homologous to proteobacterial genes. This suggests that strain DSM 45259 contains catabolic genes distantly related to those found in other actinomycetes. Together, this study not only provided new insight into the catabolic abilities of strain DSM 45259, but also suggests that this strain contains genes uncommon within actinomycetes.

Improved analytical methods for microarray-based genome-composition analysis

PubMed Central

Kim, Charles C; Joyce, Elizabeth A; Chan, Kaman; Falkow, Stanley

2002-01-01

Background Whereas genome sequencing has given us high-resolution pictures of many different species of bacteria, microarrays provide a means of obtaining information on genome composition for many strains of a given species. Genome-composition analysis using microarrays, or 'genomotyping', can be used to categorize genes into 'present' and 'divergent' categories based on the level of hybridization signal. This typically involves selecting a signal value that is used as a cutoff to discriminate present (high signal) and divergent (low signal) genes. Current methodology uses empirical determination of cutoffs for classification into these categories, but this methodology is subject to several problems that can result in the misclassification of many genes. Results We describe a method that depends on the shape of the signal-ratio distribution and does not require empirical determination of a cutoff. Moreover, the cutoff is determined on an array-to-array basis, accounting for variation in strain composition and hybridization quality. The algorithm also provides an estimate of the probability that any given gene is present, which provides a measure of confidence in the categorical assignments. Conclusions Many genes previously classified as present using static methods are in fact divergent on the basis of microarray signal; this is corrected by our algorithm. We have reassigned hundreds of genes from previous genomotyping studies of Helicobacter pylori and Campylobacter jejuni strains, and expect that the algorithm should be widely applicable to genomotyping data. PMID:12429064

Chromosomal Microarray Analysis of Consecutive Individuals with Autism Spectrum Disorders Using an Ultra-High Resolution Chromosomal Microarray Optimized for Neurodevelopmental Disorders.

PubMed

Ho, Karen S; Wassman, E Robert; Baxter, Adrianne L; Hensel, Charles H; Martin, Megan M; Prasad, Aparna; Twede, Hope; Vanzo, Rena J; Butler, Merlin G

2016-12-09

Copy number variants (CNVs) detected by chromosomal microarray analysis (CMA) significantly contribute to understanding the etiology of autism spectrum disorder (ASD) and other related conditions. In recognition of the value of CMA testing and its impact on medical management, CMA is in medical guidelines as a first-tier test in the evaluation of children with these disorders. As CMA becomes adopted into routine care for these patients, it becomes increasingly important to report these clinical findings. This study summarizes the results of over 4 years of CMA testing by a CLIA-certified clinical testing laboratory. Using a 2.8 million probe microarray optimized for the detection of CNVs associated with neurodevelopmental disorders, we report an overall CNV detection rate of 28.1% in 10,351 consecutive patients, which rises to nearly 33% in cases without ASD, with only developmental delay/intellectual disability (DD/ID) and/or multiple congenital anomalies (MCA). The overall detection rate for individuals with ASD is also significant at 24.4%. The detection rate and pathogenic yield of CMA vary significantly with the indications for testing, age, and gender, as well as the specialty of the ordering doctor. We note discrete differences in the most common recurrent CNVs found in individuals with or without a diagnosis of ASD.

Bioinformatics analysis of differentially expressed gene profiles associated with systemic lupus erythematosus

PubMed Central

Wu, Chengjiang; Zhao, Yangjing; Lin, Yu; Yang, Xinxin; Yan, Meina; Min, Yujiao; Pan, Zihui; Xia, Sheng; Shao, Qixiang

2018-01-01

DNA microarray and high-throughput sequencing have been widely used to identify the differentially expressed genes (DEGs) in systemic lupus erythematosus (SLE). However, the big data from gene microarrays are also challenging to work with in terms of analysis and processing. The presents study combined data from the microarray expression profile (GSE65391) and bioinformatics analysis to identify the key genes and cellular pathways in SLE. Gene ontology (GO) and cellular pathway enrichment analyses of DEGs were performed to investigate significantly enriched pathways. A protein-protein interaction network was constructed to determine the key genes in the occurrence and development of SLE. A total of 310 DEGs were identified in SLE, including 193 upregulated genes and 117 downregulated genes. GO analysis revealed that the most significant biological process of DEGs was immune system process. Kyoto Encyclopedia of Genes and Genome pathway analysis showed that these DEGs were enriched in signaling pathways associated with the immune system, including the RIG-I-like receptor signaling pathway, intestinal immune network for IgA production, antigen processing and presentation and the toll-like receptor signaling pathway. The current study screened the top 10 genes with higher degrees as hub genes, which included 2′-5′-oligoadenylate synthetase 1, MX dynamin like GTPase 2, interferon induced protein with tetratricopeptide repeats 1, interferon regulatory factor 7, interferon induced with helicase C domain 1, signal transducer and activator of transcription 1, ISG15 ubiquitin-like modifier, DExD/H-box helicase 58, interferon induced protein with tetratricopeptide repeats 3 and 2′-5′-oligoadenylate synthetase 2. Module analysis revealed that these hub genes were also involved in the RIG-I-like receptor signaling, cytosolic DNA-sensing, toll-like receptor signaling and ribosome biogenesis pathways. In addition, these hub genes, from different probe sets, exhibited significant co-expressed tendency in multi-experiment microarray datasets (P<0.01). In conclusion, these key genes and cellular pathways may improve the current understanding of the underlying mechanism of development of SLE. These key genes may be potential biomarkers of diagnosis, therapy and prognosis for SLE. PMID:29257335

Computational synchronization of microarray data with application to Plasmodium falciparum.

PubMed

Zhao, Wei; Dauwels, Justin; Niles, Jacquin C; Cao, Jianshu

2012-06-21

Microarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intra-erythrocytic cycle (IDC). However, the cell population gradually loses synchrony during the experiment. As a result, the microarray measurements are blurred. In this paper, we propose a generalized deconvolution approach to reconstruct the intrinsic expression pattern, and apply it to P. falciparum IDC microarray data. We develop a statistical model for the decay of synchrony among cells, and reconstruct the expression pattern through statistical inference. The proposed method can handle microarray measurements with noise and missing data. The original gene expression patterns become more apparent in the reconstructed profiles, making it easier to analyze and interpret the data. We hypothesize that reconstructed gene expression patterns represent better temporally resolved expression profiles that can be probabilistically modeled to match changes in expression level to IDC transitions. In particular, we identify transcriptionally regulated protein kinases putatively involved in regulating the P. falciparum IDC. By analyzing publicly available microarray data sets for the P. falciparum IDC, protein kinases are ranked in terms of their likelihood to be involved in regulating transitions between the ring, trophozoite and schizont developmental stages of the P. falciparum IDC. In our theoretical framework, a few protein kinases have high probability rankings, and could potentially be involved in regulating these developmental transitions. This study proposes a new methodology for extracting intrinsic expression patterns from microarray data. By applying this method to P. falciparum microarray data, several protein kinases are predicted to play a significant role in the P. falciparum IDC. Earlier experiments have indeed confirmed that several of these kinases are involved in this process. Overall, these results indicate that further functional analysis of these additional putative protein kinases may reveal new insights into how the P. falciparum IDC is regulated.

Effect of simulated transport stress on the rat small intestine: A morphological and gene expression study.

PubMed

Wan, Changrong; Yin, Peng; Xu, Xiaolong; Liu, Mingjiang; He, Shasha; Song, Shixiu; Liu, Fenghua; Xu, Jianqin

2014-04-01

The present study investigated the effects of simulated transport stress on morphology and gene expression in the small intestine of laboratory rats. Sprague Dawley rats were subjected to 35°C and 0.1×g on a constant temperature shaker for physiological, biochemical, morphological and microarray analysis before and after treatment. The treatment induced obvious stress responses with significant decreases in body weight (P<0.01), increases in rectal temperature, serum corticosterone (CORT), serum glucose (GLU), creatine kinase (CK) and lactate dehydrogenase (LDH) levels (P<0.01), as well as expression of Hsp27/70/90 mRNA (P<0.05; P<0.01). The rat jejunum was severely damaged and apoptotic after mimicking transport stress, which may mainly be related to cell death, oxidation reduction and hormone imbalance determined by microarray analysis. The bioinformatics analysis from the present study would provide insight into the potential mechanisms underlying transport stress-induced injury in the rat small intestine. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characteristic Markers of the WNT Signaling Pathways Are Differentially Expressed in Osteoarthritic Cartilage

PubMed Central

Dehne, T.; Lindahl, A.; Brittberg, M.; Pruss, A.; Ringe, J.; Sittinger, M.; Karlsson, C.

2012-01-01

Objective: It is well known that expression of markers for WNT signaling is dysregulated in osteoarthritic (OA) bone. However, it is still not fully known if the expression of these markers also is affected in OA cartilage. The aim of this study was therefore to examine this issue. Methods: Human cartilage biopsies from OA and control donors were subjected to genome-wide oligonucleotide microarrays. Genes involved in WNT signaling were selected using the BioRetis database, KEGG pathway analysis was searched using DAVID software tools, and cluster analysis was performed using Genesis software. Results from the microarray analysis were verified using quantitative real-time PCR and immunohistochemistry. In order to study the impact of cytokines for the dysregulated WNT signaling, OA and control chondrocytes were stimulated with interleukin-1 and analyzed with real-time PCR for their expression of WNT-related genes. Results: Several WNT markers displayed a significantly altered expression in OA compared to normal cartilage. Interestingly, inhibitors of the canonical and planar cell polarity WNT signaling pathways displayed significantly increased expression in OA cartilage, while the Ca2+/WNT signaling pathway was activated. Both real-time PCR and immunohistochemistry verified the microarray results. Real-time PCR analysis demonstrated that interleukin-1 upregulated expression of important WNT markers. Conclusions: WNT signaling is significantly affected in OA cartilage. The result suggests that both the canonical and planar cell polarity WNT signaling pathways were partly inhibited while the Ca2+/WNT pathway was activated in OA cartilage. PMID:26069618

Initiation of follicular atresia: gene networks during early atresia in pig ovaries.

PubMed

Zhang, Jinbi; Liu, Yang; Yao, Wang; Li, Qifa; Liu, Hong-Lin; Pan, Zengxiang

2018-05-09

In mammals, more than 99% of ovarian follicles undergo a degenerative process known as atresia. The molecular events involve in atresia initiation remain incompletely understood. The objective of this study was to analyze differential gene expression profiles of medium antral ovarian follicles during early atresia in pig. The transcriptome evaluation was performed on cDNA microarrays using healthy and early atretic follicle samples and was validated by quantitative PCR. Annotation analysis applying current database (sus scrofa 11.1) revealed 450 significantly differential expressed genes between healthy and early atretic follicles. Among them, 142 were significantly up-regulated in early atretic with respect to healthy group and 308 were down-regulated. Similar expression trends were observed between microarray data and qRT-PCR confirmation, which indicated the reliability of the microarray analysis. Further analysis of the differential expressed genes revealed the most significantly affected biological functions during early atresia including blood vessel development, regulation of DNA-templated transcription in response to stress and negative regulation of cell adhesion. The pathway and interaction analysis suggested that atresia initiation associates with 1) a crosstalk of cell apoptosis, autophagy, and ferroptosis rather than change of typical apoptosis markers, 2) dramatic shift of steroidogenic enzymes, 3) deficient glutathione metabolism, and 4) vascular degeneration. The novel gene candidates and pathways identified in the current study will lead to a comprehensive view of the molecular regulation of ovarian follicular atresia and a new understanding of atresia initiation.

Methods to study legionella transcriptome in vitro and in vivo.

PubMed

Faucher, Sebastien P; Shuman, Howard A

2013-01-01

The study of transcriptome responses can provide insight into the regulatory pathways and genetic factors that contribute to a specific phenotype. For bacterial pathogens, it can identify putative new virulence systems and shed light on the mechanisms underlying the regulation of virulence factors. Microarrays have been previously used to study gene regulation in Legionella pneumophila. In the past few years a sharp reduction of the costs associated with microarray experiments together with the availability of relatively inexpensive custom-designed commercial microarrays has made microarray technology an accessible tool for the majority of researchers. Here we describe the methodologies to conduct microarray experiments from in vitro and in vivo samples.

Use of homologous and heterologous gene expression profiling tools to characterize transcription dynamics during apple fruit maturation and ripening.

PubMed

Costa, Fabrizio; Alba, Rob; Schouten, Henk; Soglio, Valeria; Gianfranceschi, Luca; Serra, Sara; Musacchi, Stefano; Sansavini, Silviero; Costa, Guglielmo; Fei, Zhangjun; Giovannoni, James

2010-10-25

Fruit development, maturation and ripening consists of a complex series of biochemical and physiological changes that in climacteric fruits, including apple and tomato, are coordinated by the gaseous hormone ethylene. These changes lead to final fruit quality and understanding of the functional machinery underlying these processes is of both biological and practical importance. To date many reports have been made on the analysis of gene expression in apple. In this study we focused our investigation on the role of ethylene during apple maturation, specifically comparing transcriptomics of normal ripening with changes resulting from application of the hormone receptor competitor 1-methylcyclopropene. To gain insight into the molecular process regulating ripening in apple, and to compare to tomato (model species for ripening studies), we utilized both homologous and heterologous (tomato) microarray to profile transcriptome dynamics of genes involved in fruit development and ripening, emphasizing those which are ethylene regulated.The use of both types of microarrays facilitated transcriptome comparison between apple and tomato (for the later using data previously published and available at the TED: tomato expression database) and highlighted genes conserved during ripening of both species, which in turn represent a foundation for further comparative genomic studies. The cross-species analysis had the secondary aim of examining the efficiency of heterologous (specifically tomato) microarray hybridization for candidate gene identification as related to the ripening process. The resulting transcriptomics data revealed coordinated gene expression during fruit ripening of a subset of ripening-related and ethylene responsive genes, further facilitating the analysis of ethylene response during fruit maturation and ripening. Our combined strategy based on microarray hybridization enabled transcriptome characterization during normal climacteric apple ripening, as well as definition of ethylene-dependent transcriptome changes. Comparison with tomato fruit maturation and ethylene responsive transcriptome activity facilitated identification of putative conserved orthologous ripening-related genes, which serve as an initial set of candidates for assessing conservation of gene activity across genomes of fruit bearing plant species.

Gene expression profiling of two distinct neuronal populations in the rodent spinal cord.

PubMed

Ryge, Jesper; Westerdahl, Ann-Charlotte; Alstrøm, Preben; Kiehn, Ole

2008-01-01

In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50-250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord.

Gene Expression Profiling of Two Distinct Neuronal Populations in the Rodent Spinal Cord

PubMed Central

Alstrøm, Preben; Kiehn, Ole

2008-01-01

Background In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. Methodology/Principal Findings We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50–250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. Conclusions/Significance We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord. PMID:18923679

A High Phosphorus Diet Affects Lipid Metabolism in Rat Liver: A DNA Microarray Analysis

PubMed Central

Chun, Sunwoo; Bamba, Takeshi; Suyama, Tatsuya; Ishijima, Tomoko; Fukusaki, Eiichiro; Abe, Keiko; Nakai, Yuji

2016-01-01

A high phosphorus (HP) diet causes disorders of renal function, bone metabolism, and vascular function. We previously demonstrated that DNA microarray analysis is an appropriate method to comprehensively evaluate the effects of a HP diet on kidney dysfunction such as calcification, fibrillization, and inflammation. We reported that type IIb sodium-dependent phosphate transporter is significantly up-regulated in this context. In the present study, we performed DNA microarray analysis to investigate the effects of a HP diet on the liver, which plays a pivotal role in energy metabolism. DNA microarray analysis was performed with total RNA isolated from the livers of rats fed a control diet (containing 0.3% phosphorus) or a HP diet (containing 1.2% phosphorus). Gene Ontology analysis of differentially expressed genes (DEGs) revealed that the HP diet induced down-regulation of genes involved in hepatic amino acid catabolism and lipogenesis, while genes related to fatty acid β-oxidation process were up-regulated. Although genes related to fatty acid biosynthesis were down-regulated in HP diet-fed rats, genes important for the elongation and desaturation reactions of omega-3 and -6 fatty acids were up-regulated. Concentrations of hepatic arachidonic acid and eicosapentaenoic acid were increased in HP diet-fed rats. These essential fatty acids activate peroxisome proliferator-activated receptor alpha (PPARα), a transcription factor for fatty acid β-oxidation. Evaluation of the upstream regulators of DEGs using Ingenuity Pathway Analysis indicated that PPARα was activated in the livers of HP diet-fed rats. Furthermore, the serum concentration of fibroblast growth factor 21, a hormone secreted from the liver that promotes fatty acid utilization in adipose tissue as a PPARα target gene, was higher (p = 0.054) in HP diet-fed rats than in control diet-fed rats. These data suggest that a HP diet enhances energy expenditure through the utilization of free fatty acids released via lipolysis of white adipose tissue. PMID:27187182

Transcriptomic markers meet the real world: finding diagnostic signatures of corticosteroid treatment in commercial beef samples

PubMed Central

2012-01-01

Background The use of growth-promoters in beef cattle, despite the EU ban, remains a frequent practice. The use of transcriptomic markers has already proposed to identify indirect evidence of anabolic hormone treatment. So far, such approach has been tested in experimentally treated animals. Here, for the first time commercial samples were analyzed. Results Quantitative determination of Dexamethasone (DEX) residues in the urine collected at the slaughterhouse was performed by Liquid Chromatography-Mass Spectrometry (LC-MS). DNA-microarray technology was used to obtain transcriptomic profiles of skeletal muscle in commercial samples and negative controls. LC-MS confirmed the presence of low level of DEX residues in the urine of the commercial samples suspect for histological classification. Principal Component Analysis (PCA) on microarray data identified two clusters of samples. One cluster included negative controls and a subset of commercial samples, while a second cluster included part of the specimens collected at the slaughterhouse together with positives for corticosteroid treatment based on thymus histology and LC-MS. Functional analysis of the differentially expressed genes (3961) between the two groups provided further evidence that animals clustering with positive samples might have been treated with corticosteroids. These suspect samples could be reliably classified with a specific classification tool (Prediction Analysis of Microarray) using just two genes. Conclusions Despite broad variation observed in gene expression profiles, the present study showed that DNA-microarrays can be used to find transcriptomic signatures of putative anabolic treatments and that gene expression markers could represent a useful screening tool. PMID:23110699

Using microarray analysis to evaluate genetic polymorphisms involved in the metabolism of environmental chemicals.

PubMed

Ban, Susumu; Kondo, Tomoko; Ishizuka, Mayumi; Sasaki, Seiko; Konishi, Kanae; Washino, Noriaki; Fujita, Syoichi; Kishi, Reiko

2007-05-01

The field of molecular biology currently faces the need for a comprehensive method of evaluating individual differences derived from genetic variation in the form of single nucleotide polymorphisms (SNPs). SNPs in human genes are generally considered to be very useful in determining inherited genetic disorders, susceptibility to certain diseases, and cancer predisposition. Quick and accurate discrimination of SNPs is the key characteristic of technology used in DNA diagnostics. For this study, we first developed a DNA microarray and then evaluated its efficacy by determining the detection ability and validity of this method. Using DNA obtained from 380 pregnant Japanese women, we examined 13 polymorphisms of 9 genes, which are associated with the metabolism of environmental chemical compounds found in high frequency among Japanese populations. The ability to detect CYP1A1 I462V, CYP1B1 L432V, GSTP1 I105V and AhR R554K gene polymorphisms was above 98%, and agreement rates when compared with real time PCR analysis methods (kappa values) showed high validity: 0.98 (0.96), 0.97 (0.93), 0.90 (0.81), 0.90 (0.91), respectively. While this DNA microarray analysis should prove important as a method for initial screening, it is still necessary that we find better methods for improving the detection of other gene polymorphisms not part of this study.

Reverse engineering biological networks :applications in immune responses to bio-toxins.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineermore » regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.« less

Microarray analysis reveals overlapping and specific transcriptional responses to different plant hormones in rice

PubMed Central

Garg, Rohini; Tyagi, Akhilesh K.; Jain, Mukesh

2012-01-01

Hormones exert pleiotropic effects on plant growth and development throughout the life cycle. Many of these effects are mediated at molecular level via altering gene expression. In this study, we investigated the exogenous effect of plant hormones, including auxin, cytokinin, abscisic acid, ethylene, salicylic acid and jasmonic acid, on the transcription of rice genes at whole genome level using microarray. Our analysis identified a total of 4171 genes involved in several biological processes, whose expression was altered significantly in the presence of different hormones. Further, 28% of these genes exhibited overlapping transcriptional responses in the presence of any two hormones, indicating crosstalk among plant hormones. In addition, we identified genes showing only a particular hormone-specific response, which can be used as hormone-specific markers. The results of this study will facilitate further studies in hormone biology in rice. PMID:22827941

Gene expression profiling in gill tissues of White spot syndrome virus infected black tiger shrimp Penaeus monodon by DNA microarray.

PubMed

Shekhar, M S; Gomathi, A; Gopikrishna, G; Ponniah, A G

2015-06-01

White spot syndrome virus (WSSV) continues to be the most devastating viral pathogen infecting penaeid shrimp the world over. The genome of WSSV has been deciphered and characterized from three geographical isolates and significant progress has been made in developing various molecular diagnostic methods to detect the virus. However, the information on host immune gene response to WSSV pathogenesis is limited. Microarray analysis was carried out as an approach to analyse the gene expression in black tiger shrimp Penaeus monodon in response to WSSV infection. Gill tissues collected from the WSSV infected shrimp at 6, 24, 48 h and moribund stage were analysed for differential gene expression. Shrimp cDNAs of 40,059 unique sequences were considered for designing the microarray chip. The Cy3-labeled cRNA derived from healthy and WSSV-infected shrimp was subjected to hybridization with all the DNA spots in the microarray which revealed 8,633 and 11,147 as up- and down-regulated genes respectively at different time intervals post infection. The altered expression of these numerous genes represented diverse functions such as immune response, osmoregulation, apoptosis, nucleic acid binding, energy and metabolism, signal transduction, stress response and molting. The changes in gene expression profiles observed by microarray analysis provides molecular insights and framework of genes which are up- and down-regulated at different time intervals during WSSV infection in shrimp. The microarray data was validated by Real Time analysis of four differentially expressed genes involved in apoptosis (translationally controlled tumor protein, inhibitor of apoptosis protein, ubiquitin conjugated enzyme E2 and caspase) for gene expression levels. The role of apoptosis related genes in WSSV infected shrimp is discussed herein.

Reuse of imputed data in microarray analysis increases imputation efficiency

PubMed Central

Kim, Ki-Yeol; Kim, Byoung-Jin; Yi, Gwan-Su

2004-01-01

Background The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. Results We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. Conclusions Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data. PMID:15504240

Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis.

PubMed

Ma, Chuang; Wang, Xiangfeng

2012-09-01

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey's biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.

A robust two-way semi-linear model for normalization of cDNA microarray data

PubMed Central

Wang, Deli; Huang, Jian; Xie, Hehuang; Manzella, Liliana; Soares, Marcelo Bento

2005-01-01

Background Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values. Methods We propose a robust semiparametric method in a two-way semi-linear model (TW-SLM) for normalization of cDNA microarray data. This method does not make the usual assumptions underlying some of the existing methods. For example, it does not assume that: (i) the percentage of differentially expressed genes is small; or (ii) the numbers of up- and down-regulated genes are about the same, as required in the LOWESS normalization method. We conduct simulation studies to evaluate the proposed method and use a real data set from a specially designed microarray experiment to compare the performance of the proposed method with that of the LOWESS normalization approach. Results The simulation results show that the proposed method performs better than the LOWESS normalization method in terms of mean square errors for estimated gene effects. The results of analysis of the real data set also show that the proposed method yields more consistent results between the direct and the indirect comparisons and also can detect more differentially expressed genes than the LOWESS method. Conclusions Our simulation studies and the real data example indicate that the proposed robust TW-SLM method works at least as well as the LOWESS method and works better when the underlying assumptions for the LOWESS method are not satisfied. Therefore, it is a powerful alternative to the existing normalization methods. PMID:15663789

Application of the Gini Correlation Coefficient to Infer Regulatory Relationships in Transcriptome Analysis[W][OA

PubMed Central

Ma, Chuang; Wang, Xiangfeng

2012-01-01

One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey’s biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses. PMID:22797655

Immune and inflammatory gene signature in rat cerebrum in subarachnoid hemorrhage with microarray analysis.

PubMed

Lee, Chu-I; Chou, An-Kuo; Lin, Ching-Chih; Chou, Chia-Hua; Loh, Joon-Khim; Lieu, Ann-Shung; Wang, Chih-Jen; Huang, Chi-Ying F; Howng, Shen-Long; Hong, Yi-Ren

2012-01-01

Cerebral vasospasm following subarachnoid hemorrhage (SAH) has been studied in terms of a contraction of the major cerebral arteries, but the effect of cerebrum tissue in SAH is not yet well understood. To gain insight into the biology of SAH-expressing cerebrum, we employed oligonucleotide microarrays to characterize the gene expression profiles of cerebrum tissue at the early stage of SAH. Functional gene expression in the cerebrum was analyzed 2 h following stage 1-hemorrhage in Sprague-Dawley rats. mRNA was investigated by performing microarray and quantitative real-time PCR analyses, and protein expression was determined by Western blot analysis. In this study, 18 upregulated and 18 downregulated genes displayed at least a 1.5-fold change. Five genes were verified by real-time PCR, including three upregulated genes [prostaglandin E synthase (PGES), CD14 antigen, and tissue inhibitor of metalloproteinase 1 (TIMP1)] as well as two downregulated genes [KRAB-zinc finger protein-2 (KZF-2) and γ-aminobutyric acid B receptor 1 (GABA B receptor)]. Notably, there were functional implications for the three upregulated genes involved in the inflammatory SAH process. However, the mechanisms leading to decreased KZF-2 and GABA B receptor expression in SAH have never been characterized. We conclude that oligonucleotide microarrays have the potential for use as a method to identify candidate genes associated with SAH and to provide novel investigational targets, including genes involved in the immune and inflammatory response. Furthermore, understanding the regulation of MMP9/TIMP1 during the early stages of SAH may elucidate the pathophysiological mechanisms in SAH rats.

Ewing's Sarcoma: An Analysis of miRNA Expression Profiles and Target Genes in Paraffin-Embedded Primary Tumor Tissue.

PubMed

Parafioriti, Antonina; Bason, Caterina; Armiraglio, Elisabetta; Calciano, Lucia; Daolio, Primo Andrea; Berardocco, Martina; Di Bernardo, Andrea; Colosimo, Alessia; Luksch, Roberto; Berardi, Anna C

2016-04-30

The molecular mechanism responsible for Ewing's Sarcoma (ES) remains largely unknown. MicroRNAs (miRNAs), a class of small non-coding RNAs able to regulate gene expression, are deregulated in tumors and may serve as a tool for diagnosis and prediction. However, the status of miRNAs in ES has not yet been thoroughly investigated. This study compared global miRNAs expression in paraffin-embedded tumor tissue samples from 20 ES patients, affected by primary untreated tumors, with miRNAs expressed in normal human mesenchymal stromal cells (MSCs) by microarray analysis. A miRTarBase database was used to identify the predicted target genes for differentially expressed miRNAs. The miRNAs microarray analysis revealed distinct patterns of miRNAs expression between ES samples and normal MSCs. 58 of the 954 analyzed miRNAs were significantly differentially expressed in ES samples compared to MSCs. Moreover, the qRT-PCR analysis carried out on three selected miRNAs showed that miR-181b, miR-1915 and miR-1275 were significantly aberrantly regulated, confirming the microarray results. Bio-database analysis identified BCL-2 as a bona fide target gene of the miR-21, miR-181a, miR-181b, miR-29a, miR-29b, miR-497, miR-195, miR-let-7a, miR-34a and miR-1915. Using paraffin-embedded tissues from ES patients, this study has identified several potential target miRNAs and one gene that might be considered a novel critical biomarker for ES pathogenesis.

Molecular characterization of endocarditis-associated Staphylococcus aureus.

PubMed

Nethercott, Cara; Mabbett, Amanda N; Totsika, Makrina; Peters, Paul; Ortiz, Juan C; Nimmo, Graeme R; Coombs, Geoffrey W; Walker, Mark J; Schembri, Mark A

2013-07-01

Infective endocarditis (IE) is a life-threatening infection of the heart endothelium and valves. Staphylococcus aureus is a predominant cause of severe IE and is frequently associated with infections in health care settings and device-related infections. Multilocus sequence typing (MLST), spa typing, and virulence gene microarrays are frequently used to classify S. aureus clinical isolates. This study examined the utility of these typing tools to investigate S. aureus epidemiology associated with IE. Ninety-seven S. aureus isolates were collected from patients diagnosed with (i) IE, (ii) bloodstream infection related to medical devices, (iii) bloodstream infection not related to medical devices, and (iv) skin or soft-tissue infections. The MLST clonal complex (CC) for each isolate was determined and compared to the CCs of members of the S. aureus population by eBURST analysis. The spa type of all isolates was also determined. A null model was used to determine correlations of IE with CC and spa type. DNA microarray analysis was performed, and a permutational analysis of multivariate variance (PERMANOVA) and principal coordinates analysis were conducted to identify genotypic differences between IE and non-IE strains. CC12, CC20, and spa type t160 were significantly associated with IE S. aureus. A subset of virulence-associated genes and alleles, including genes encoding staphylococcal superantigen-like proteins, fibrinogen-binding protein, and a leukocidin subunit, also significantly correlated with IE isolates. MLST, spa typing, and microarray analysis are promising tools for monitoring S. aureus epidemiology associated with IE. Further research to determine a role for the S. aureus IE-associated virulence genes identified in this study is warranted.

Molecular Characterization of Endocarditis-Associated Staphylococcus aureus

PubMed Central

Nethercott, Cara; Mabbett, Amanda N.; Totsika, Makrina; Peters, Paul; Ortiz, Juan C.; Nimmo, Graeme R.; Coombs, Geoffrey W.

2013-01-01

Infective endocarditis (IE) is a life-threatening infection of the heart endothelium and valves. Staphylococcus aureus is a predominant cause of severe IE and is frequently associated with infections in health care settings and device-related infections. Multilocus sequence typing (MLST), spa typing, and virulence gene microarrays are frequently used to classify S. aureus clinical isolates. This study examined the utility of these typing tools to investigate S. aureus epidemiology associated with IE. Ninety-seven S. aureus isolates were collected from patients diagnosed with (i) IE, (ii) bloodstream infection related to medical devices, (iii) bloodstream infection not related to medical devices, and (iv) skin or soft-tissue infections. The MLST clonal complex (CC) for each isolate was determined and compared to the CCs of members of the S. aureus population by eBURST analysis. The spa type of all isolates was also determined. A null model was used to determine correlations of IE with CC and spa type. DNA microarray analysis was performed, and a permutational analysis of multivariate variance (PERMANOVA) and principal coordinates analysis were conducted to identify genotypic differences between IE and non-IE strains. CC12, CC20, and spa type t160 were significantly associated with IE S. aureus. A subset of virulence-associated genes and alleles, including genes encoding staphylococcal superantigen-like proteins, fibrinogen-binding protein, and a leukocidin subunit, also significantly correlated with IE isolates. MLST, spa typing, and microarray analysis are promising tools for monitoring S. aureus epidemiology associated with IE. Further research to determine a role for the S. aureus IE-associated virulence genes identified in this study is warranted. PMID:23616460

Analysis and modelling of septic shock microarray data using Singular Value Decomposition.

PubMed

Allanki, Srinivas; Dixit, Madhulika; Thangaraj, Paul; Sinha, Nandan Kumar

2017-06-01

Being a high throughput technique, enormous amounts of microarray data has been generated and there arises a need for more efficient techniques of analysis, in terms of speed and accuracy. Finding the differentially expressed genes based on just fold change and p-value might not extract all the vital biological signals that occur at a lower gene expression level. Besides this, numerous mathematical models have been generated to predict the clinical outcome from microarray data, while very few, if not none, aim at predicting the vital genes that are important in a disease progression. Such models help a basic researcher narrow down and concentrate on a promising set of genes which leads to the discovery of gene-based therapies. In this article, as a first objective, we have used the lesser known and used Singular Value Decomposition (SVD) technique to build a microarray data analysis tool that works with gene expression patterns and intrinsic structure of the data in an unsupervised manner. We have re-analysed a microarray data over the clinical course of Septic shock from Cazalis et al. (2014) and have shown that our proposed analysis provides additional information compared to the conventional method. As a second objective, we developed a novel mathematical model that predicts a set of vital genes in the disease progression that works by generating samples in the continuum between health and disease, using a simple normal-distribution-based random number generator. We also verify that most of the predicted genes are indeed related to septic shock. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

PubMed Central

Sysoev, Victor V.; Kiselev, Ilya; Frietsch, Markus; Goschnick, Joachim

2004-01-01

The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 °C/mm and 6.7 °C/mm, applied across the sensor elements (segments) of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis) coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.

Chondrocyte channel transcriptomics

PubMed Central

Lewis, Rebecca; May, Hannah; Mobasheri, Ali; Barrett-Jolley, Richard

2013-01-01

To date, a range of ion channels have been identified in chondrocytes using a number of different techniques, predominantly electrophysiological and/or biomolecular; each of these has its advantages and disadvantages. Here we aim to compare and contrast the data available from biophysical and microarray experiments. This letter analyses recent transcriptomics datasets from chondrocytes, accessible from the European Bioinformatics Institute (EBI). We discuss whether such bioinformatic analysis of microarray datasets can potentially accelerate identification and discovery of ion channels in chondrocytes. The ion channels which appear most frequently across these microarray datasets are discussed, along with their possible functions. We discuss whether functional or protein data exist which support the microarray data. A microarray experiment comparing gene expression in osteoarthritis and healthy cartilage is also discussed and we verify the differential expression of 2 of these genes, namely the genes encoding large calcium-activated potassium (BK) and aquaporin channels. PMID:23995703

Clustering gene expression data based on predicted differential effects of GV interaction.

PubMed

Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

2005-02-01

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

GenePublisher: Automated analysis of DNA microarray data.

PubMed

Knudsen, Steen; Workman, Christopher; Sicheritz-Ponten, Thomas; Friis, Carsten

2003-07-01

GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with a specification of the data. The server performs normalization, statistical analysis and visualization of the data. The results are run against databases of signal transduction pathways, metabolic pathways and promoter sequences in order to extract more information. The results of the entire analysis are summarized in report form and returned to the user.

Seasonal dynamics of freshwater pathogens as measured by microarray at Lake Sapanca, a drinking water source in the north-eastern part of Turkey.

PubMed

Akçaalan, Reyhan; Albay, Meric; Koker, Latife; Baudart, Julia; Guillebault, Delphine; Fischer, Sabine; Weigel, Wilfried; Medlin, Linda K

2017-12-22

Monitoring drinking water quality is an important public health issue. Two objectives from the 4 years, six nations, EU Project μAqua were to develop hierarchically specific probes to detect and quantify pathogens in drinking water using a PCR-free microarray platform and to design a standardised water sampling program from different sources in Europe to obtain sufficient material for downstream analysis. Our phylochip contains barcodes (probes) that specifically identify freshwater pathogens that are human health risks in a taxonomic hierarchical fashion such that if species is present, the entire taxonomic hierarchy (genus, family, order, phylum, kingdom) leading to it must also be present, which avoids false positives. Molecular tools are more rapid, accurate and reliable than traditional methods, which means faster mitigation strategies with less harm to humans and the community. We present microarray results for the presence of freshwater pathogens from a Turkish lake used drinking water and inferred cyanobacterial cell equivalents from samples concentrated from 40 into 1 L in 45 min using hollow fibre filters. In two companion studies from the same samples, cyanobacterial toxins were analysed using chemical methods and those dates with highest toxin values also had highest cell equivalents as inferred from this microarray study.

eSensor: an electrochemical detection-based DNA microarray technology enabling sample-to-answer molecular diagnostics

NASA Astrophysics Data System (ADS)

Liu, Robin H.; Longiaru, Mathew

2009-05-01

DNA microarrays are becoming a widespread tool used in life science and drug screening due to its many benefits of miniaturization and integration. Microarrays permit a highly multiplexed DNA analysis. Recently, the development of new detection methods and simplified methodologies has rapidly expanded the use of microarray technologies from predominantly gene expression analysis into the arena of diagnostics. Osmetech's eSensor® is an electrochemical detection platform based on a low-to- medium density DNA hybridization array on a cost-effective printed circuit board substrate. eSensor® has been cleared by FDA for Warfarin sensitivity test and Cystic Fibrosis Carrier Detection. Other genetic-based diagnostic and infectious disease detection tests are under development. The eSensor® platform eliminates the need for an expensive laser-based optical system and fluorescent reagents. It allows one to perform hybridization and detection in a single and small instrument without any fluidic processing and handling. Furthermore, the eSensor® platform is readily adaptable to on-chip sample-to-answer genetic analyses using microfluidics technology. The eSensor® platform provides a cost-effective solution to direct sample-to-answer genetic analysis, and thus have a potential impact in the fields of point-of-care genetic analysis, environmental testing, and biological warfare agent detection.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards.

PubMed

Bruland, Torunn; Anderssen, Endre; Doseth, Berit; Bergum, Hallgeir; Beisvag, Vidar; Laegreid, Astrid

2007-10-18

The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards. We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real. The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards

PubMed Central

Bruland, Torunn; Anderssen, Endre; Doseth, Berit; Bergum, Hallgeir; Beisvag, Vidar; Lægreid, Astrid

2007-01-01

Background The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards. Results We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real. Conclusion The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish. PMID:17949480

HOXD-AS1 is a novel lncRNA encoded in HOXD cluster and a marker of neuroblastoma progression revealed via integrative analysis of noncoding transcriptome

PubMed Central

2014-01-01

Background Long noncoding RNAs (lncRNAs) constitute a major, but poorly characterized part of human transcriptome. Recent evidence indicates that many lncRNAs are involved in cancer and can be used as predictive and prognostic biomarkers. Significant fraction of lncRNAs is represented on widely used microarray platforms, however they have usually been ignored in cancer studies. Results We developed a computational pipeline to annotate lncRNAs on popular Affymetrix U133 microarrays, creating a resource allowing measurement of expression of 1581 lncRNAs. This resource can be utilized to interrogate existing microarray datasets for various lncRNA studies. We found that these lncRNAs fall into three distinct classes according to their statistical distribution by length. Remarkably, these three classes of lncRNAs were co-localized with protein coding genes exhibiting distinct gene ontology groups. This annotation was applied to microarray analysis which identified a 159 lncRNA signature that discriminates between localized and metastatic stages of neuroblastoma. Analysis of an independent patient cohort revealed that this signature differentiates also relapsing from non-relapsing primary tumors. This is the first example of the signature developed via the analysis of expression of lncRNAs solely. One of these lncRNAs, termed HOXD-AS1, is encoded in HOXD cluster. HOXD-AS1 is evolutionary conserved among hominids and has all bona fide features of a gene. Studying retinoid acid (RA) response of SH-SY5Y cell line, a model of human metastatic neuroblastoma, we found that HOXD-AS1 is a subject to morphogenic regulation, is activated by PI3K/Akt pathway and itself is involved in control of RA-induced cell differentiation. Knock-down experiments revealed that HOXD-AS1 controls expression levels of clinically significant protein-coding genes involved in angiogenesis and inflammation, the hallmarks of metastatic cancer. Conclusions Our findings greatly extend the number of noncoding RNAs functionally implicated in tumor development and patient treatment and highlight their role as potential prognostic biomarkers of neuroblastomas. PMID:25522241

DNA microarray-based PCR ribotyping of Clostridium difficile.

PubMed

Schneeberg, Alexander; Ehricht, Ralf; Slickers, Peter; Baier, Vico; Neubauer, Heinrich; Zimmermann, Stefan; Rabold, Denise; Lübke-Becker, Antina; Seyboldt, Christian

2015-02-01

This study presents a DNA microarray-based assay for fast and simple PCR ribotyping of Clostridium difficile strains. Hybridization probes were designed to query the modularly structured intergenic spacer region (ISR), which is also the template for conventional and PCR ribotyping with subsequent capillary gel electrophoresis (seq-PCR) ribotyping. The probes were derived from sequences available in GenBank as well as from theoretical ISR module combinations. A database of reference hybridization patterns was set up from a collection of 142 well-characterized C. difficile isolates representing 48 seq-PCR ribotypes. The reference hybridization patterns calculated by the arithmetic mean were compared using a similarity matrix analysis. The 48 investigated seq-PCR ribotypes revealed 27 array profiles that were clearly distinguishable. The most frequent human-pathogenic ribotypes 001, 014/020, 027, and 078/126 were discriminated by the microarray. C. difficile strains related to 078/126 (033, 045/FLI01, 078, 126, 126/FLI01, 413, 413/FLI01, 598, 620, 652, and 660) and 014/020 (014, 020, and 449) showed similar hybridization patterns, confirming their genetic relatedness, which was previously reported. A panel of 50 C. difficile field isolates was tested by seq-PCR ribotyping and the DNA microarray-based assay in parallel. Taking into account that the current version of the microarray does not discriminate some closely related seq-PCR ribotypes, all isolates were typed correctly. Moreover, seq-PCR ribotypes without reference profiles available in the database (ribotype 009 and 5 new types) were correctly recognized as new ribotypes, confirming the performance and expansion potential of the microarray. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

RECOVERING FILTER-BASED MICROARRAY DATA FOR PATHWAYS ANALYSIS USING A MULTIPOINT ALIGNMENT STRATEGY

EPA Science Inventory

The use of commercial microarrays are rapidly becoming the method of choice for profiling gene expression and assessing various disease states. Research Genetics has provided a series of well defined biological and software tools to the research community for these analyses. Th...

Estimating gene function with least squares nonnegative matrix factorization.

PubMed

Wang, Guoli; Ochs, Michael F

2007-01-01

Nonnegative matrix factorization is a machine learning algorithm that has extracted information from data in a number of fields, including imaging and spectral analysis, text mining, and microarray data analysis. One limitation with the method for linking genes through microarray data in order to estimate gene function is the high variance observed in transcription levels between different genes. Least squares nonnegative matrix factorization uses estimates of the uncertainties on the mRNA levels for each gene in each condition, to guide the algorithm to a local minimum in normalized chi2, rather than a Euclidean distance or divergence between the reconstructed data and the data itself. Herein, application of this method to microarray data is demonstrated in order to predict gene function.

Microarray expression technology: from start to finish.

PubMed

Elvidge, Gareth

2006-01-01

The recent introduction of new microarray expression technologies and the further development of established platforms ensure that the researcher is presented with a range of options for performing an experiment. Whilst this has opened up the possibilities for future applications, such as exon-specific arrays, increased sample throughput and 'chromatin immunoprecipitation (ChIP) on chip' experiments, the initial decision processes and experiment planning are made more difficult. This review will give an overview of the various technologies that are available to perform a microarray expression experiment, from the initial planning stages through to the final data analysis. Both practical aspects and data analysis options will be considered. The relative advantages and disadvantages will be discussed with insights provided for future directions of the technology.

Single molecule fluorescence microscopy for ultra-sensitive RNA expression profiling

NASA Astrophysics Data System (ADS)

Hesse, Jan; Jacak, Jaroslaw; Regl, Gerhard; Eichberger, Thomas; Aberger, Fritz; Schlapak, Robert; Howorka, Stefan; Muresan, Leila; Frischauf, Anna-Maria; Schütz, Gerhard J.

2007-02-01

We developed a microarray analysis platform for ultra-sensitive RNA expression profiling of minute samples. It utilizes a novel scanning system for single molecule fluorescence detection on cm2 size samples in combination with specialized biochips, optimized for low autofluorescence and weak unspecific adsorption. 20 μg total RNA was extracted from 10 6 cells of a human keratinocyte cell line (HaCaT) and reversely transcribed in the presence of Alexa647-aha-dUTP. 1% of the resulting labeled cDNA was used for complex hybridization to a custom-made oligonucleotide microarray representing a set of 125 different genes. For low abundant genes, individual cDNA molecules hybridized to the microarray spots could be resolved. Single cDNA molecules hybridized to the chip surface appeared as diffraction limited features in the fluorescence images. The à trous wavelet method was utilized for localization and counting of the separated cDNA signals. Subsequently, the degree of labeling of the localized cDNA molecules was determined by brightness analysis for the different genes. Variations by factors up to 6 were found, which in conventional microarray analysis would result in a misrepresentation of the relative abundance of mRNAs.

Estimating differential expression from multiple indicators

PubMed Central

Ilmjärv, Sten; Hundahl, Christian Ansgar; Reimets, Riin; Niitsoo, Margus; Kolde, Raivo; Vilo, Jaak; Vasar, Eero; Luuk, Hendrik

2014-01-01

Regardless of the advent of high-throughput sequencing, microarrays remain central in current biomedical research. Conventional microarray analysis pipelines apply data reduction before the estimation of differential expression, which is likely to render the estimates susceptible to noise from signal summarization and reduce statistical power. We present a probe-level framework, which capitalizes on the high number of concurrent measurements to provide more robust differential expression estimates. The framework naturally extends to various experimental designs and target categories (e.g. transcripts, genes, genomic regions) as well as small sample sizes. Benchmarking in relation to popular microarray and RNA-sequencing data-analysis pipelines indicated high and stable performance on the Microarray Quality Control dataset and in a cell-culture model of hypoxia. Experimental-data-exhibiting long-range epigenetic silencing of gene expression was used to demonstrate the efficacy of detecting differential expression of genomic regions, a level of analysis not embraced by conventional workflows. Finally, we designed and conducted an experiment to identify hypothermia-responsive genes in terms of monotonic time-response. As a novel insight, hypothermia-dependent up-regulation of multiple genes of two major antioxidant pathways was identified and verified by quantitative real-time PCR. PMID:24586062

Custom microarray construction and analysis for determining potential biomarkers of subchronic androgen exposure in the Eastern Mosquitofish (Gambusia holbrooki)

USGS Publications Warehouse

Brockmeier, Erica K.; Yu, Fahong; Amador, David Moraga; Bargar, Timothy A.; Denslow, Nancy D.

2013-01-01

Coupling microarray data with phenotypic changes driven by androgen exposure in mosquitofish is key for developing this organism into a bioindicator for EDCs. Future studies using this array will enhance knowledge of the biology and toxicological response of this species. This work provides a foundation of molecular knowledge and tools that can be used to delve further into understanding the biology of G. holbrooki and how this organism can be used as a bioindicator organism for endocrine disrupting pollutants in the environment.

Functional interaction analysis of GM1-related carbohydrates and Vibrio cholerae toxins using carbohydrate microarray.

PubMed

Kim, Chang Sup; Seo, Jeong Hyun; Cha, Hyung Joon

2012-08-07

The development of analytical tools is important for understanding the infection mechanisms of pathogenic bacteria or viruses. In the present work, a functional carbohydrate microarray combined with a fluorescence immunoassay was developed to analyze the interactions of Vibrio cholerae toxin (ctx) proteins and GM1-related carbohydrates. Ctx proteins were loaded onto the surface-immobilized GM1 pentasaccharide and six related carbohydrates, and their binding affinities were detected immunologically. The analysis of the ctx-carbohydrate interactions revealed that the intrinsic selectivity of ctx was GM1 pentasaccharide ≫ GM2 tetrasaccharide > asialo GM1 tetrasaccharide ≥ GM3trisaccharide, indicating that a two-finger grip formation and the terminal monosaccharides play important roles in the ctx-GM1 interaction. In addition, whole cholera toxin (ctxAB(5)) had a stricter substrate specificity and a stronger binding affinity than only the cholera toxin B subunit (ctxB). On the basis of the quantitative analysis, the carbohydrate microarray showed the sensitivity of detection of the ctxAB(5)-GM1 interaction with a limit-of-detection (LOD) of 2 ng mL(-1) (23 pM), which is comparable to other reported high sensitivity assay tools. In addition, the carbohydrate microarray successfully detected the actual toxin directly secreted from V. cholerae, without showing cross-reactivity to other bacteria. Collectively, these results demonstrate that the functional carbohydrate microarray is suitable for analyzing toxin protein-carbohydrate interactions and can be applied as a biosensor for toxin detection.

Bacterial identification and subtyping using DNA microarray and DNA sequencing.

PubMed

Al-Khaldi, Sufian F; Mossoba, Magdi M; Allard, Marc M; Lienau, E Kurt; Brown, Eric D

2012-01-01

The era of fast and accurate discovery of biological sequence motifs in prokaryotic and eukaryotic cells is here. The co-evolution of direct genome sequencing and DNA microarray strategies not only will identify, isotype, and serotype pathogenic bacteria, but also it will aid in the discovery of new gene functions by detecting gene expressions in different diseases and environmental conditions. Microarray bacterial identification has made great advances in working with pure and mixed bacterial samples. The technological advances have moved beyond bacterial gene expression to include bacterial identification and isotyping. Application of new tools such as mid-infrared chemical imaging improves detection of hybridization in DNA microarrays. The research in this field is promising and future work will reveal the potential of infrared technology in bacterial identification. On the other hand, DNA sequencing by using 454 pyrosequencing is so cost effective that the promise of $1,000 per bacterial genome sequence is becoming a reality. Pyrosequencing technology is a simple to use technique that can produce accurate and quantitative analysis of DNA sequences with a great speed. The deposition of massive amounts of bacterial genomic information in databanks is creating fingerprint phylogenetic analysis that will ultimately replace several technologies such as Pulsed Field Gel Electrophoresis. In this chapter, we will review (1) the use of DNA microarray using fluorescence and infrared imaging detection for identification of pathogenic bacteria, and (2) use of pyrosequencing in DNA cluster analysis to fingerprint bacterial phylogenetic trees.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

EPA Science Inventory

The second phase of the MicroArray Quality Control (MAQC-II) project evaluated common practices for developing and validating microarray-based models aimed at predicting toxicological and clinical endpoints. Thirty-six teams developed classifiers for 13 endpoints - some easy, som...

Microarray Analysis Reveals Increased Expression of Matrix Metalloproteases and Cytokines of Interleukin-20 Subfamily in the Kidneys of Neonate Rats Underwent Unilateral Ureteral Obstruction: A Potential Role of IL-24 in the Regulation of Inflammation and Tissue Remodeling.

PubMed

Pap, Domonkos; Sziksz, Erna; Kiss, Zoltán; Rokonay, Réka; Veres-Székely, Apor; Lippai, Rita; Takács, István Márton; Kis, Éva; Fekete, Andrea; Reusz, György; Szabó, Attila J; Vannay, Adam

2017-01-01

Congenital obstructive nephropathy (CON) is the main cause of pediatric chronic kidney diseases leading to renal fibrosis. High morbidity and limited treatment opportunities of CON urge the better understanding of the underlying molecular mechanisms. To identify the differentially expressed genes, microarray analysis was performed on the kidney samples of neonatal rats underwent unilateral ureteral obstruction (UUO). Microarray results were then validated by real-time RT-PCR and bioinformatics analysis was carried out to identify the relevant genes, functional groups and pathways involved in the pathomechanism of CON. Renal expression of matrix metalloproteinase (MMP)-12 and interleukin (IL)-24 were evaluated by real-time RT-PCR, flow cytometry and immunohistochemical analysis. Effect of the main profibrotic factors on the expression of MMP-12 and IL-24 was investigated on HK-2 and HEK-293 cell lines. Finally, the effect of IL-24 treatment on the expression of pro-inflammatory cytokines and MMPs were tested in vitro. Microarray analysis revealed 880 transcripts showing >2.0-fold change following UUO, enriched mainly in immune response related processes. The most up-regulated genes were MMPs and members of IL-20 cytokine subfamily, including MMP-3, MMP-7, MMP-12, IL-19 and IL-24. We found that while TGF-β treatment inhibits the expression of MMP-12 and IL-24, H2O2 or PDGF-B treatment induce the epithelial expression of MMP-12. We demonstrated that IL-24 treatment decreases the expression of IL-6 and MMP-3 in the renal epithelial cells. This study provides an extensive view of UUO induced changes in the gene expression profile of the developing kidney and describes novel molecules, which may play significant role in the pathomechanism of CON. © 2017 The Author(s)Published by S. Karger AG, Basel.

Surface Glycosylation Profiles of Urine Extracellular Vesicles

PubMed Central

Gerlach, Jared Q.; Krüger, Anja; Gallogly, Susan; Hanley, Shirley A.; Hogan, Marie C.; Ward, Christopher J.

2013-01-01

Urinary extracellular vesicles (uEVs) are released by cells throughout the nephron and contain biomolecules from their cells of origin. Although uEV-associated proteins and RNA have been studied in detail, little information exists regarding uEV glycosylation characteristics. Surface glycosylation profiling by flow cytometry and lectin microarray was applied to uEVs enriched from urine of healthy adults by ultracentrifugation and centrifugal filtration. The carbohydrate specificity of lectin microarray profiles was confirmed by competitive sugar inhibition and carbohydrate-specific enzyme hydrolysis. Glycosylation profiles of uEVs and purified Tamm Horsfall protein were compared. In both flow cytometry and lectin microarray assays, uEVs demonstrated surface binding, at low to moderate intensities, of a broad range of lectins whether prepared by ultracentrifugation or centrifugal filtration. In general, ultracentrifugation-prepared uEVs demonstrated higher lectin binding intensities than centrifugal filtration-prepared uEVs consistent with lesser amounts of co-purified non-vesicular proteins. The surface glycosylation profiles of uEVs showed little inter-individual variation and were distinct from those of Tamm Horsfall protein, which bound a limited number of lectins. In a pilot study, lectin microarray was used to compare uEVs from individuals with autosomal dominant polycystic kidney disease to those of age-matched controls. The lectin microarray profiles of polycystic kidney disease and healthy uEVs showed differences in binding intensity of 6/43 lectins. Our results reveal a complex surface glycosylation profile of uEVs that is accessible to lectin-based analysis following multiple uEV enrichment techniques, is distinct from co-purified Tamm Horsfall protein and may demonstrate disease-specific modifications. PMID:24069349

ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

PubMed Central

2014-01-01

Background Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures. Results In this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process. Conclusions This study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally. PMID:24739361

Development of a fluorescence-activated cell sorting method coupled with whole genome amplification to analyze minority and trace Dehalococcoides genomes in microbial communities.

PubMed

Lee, Patrick K H; Men, Yujie; Wang, Shanquan; He, Jianzhong; Alvarez-Cohen, Lisa

2015-02-03

Dehalococcoides mccartyi are functionally important bacteria that catalyze the reductive dechlorination of chlorinated ethenes. However, these anaerobic bacteria are fastidious to isolate, making downstream genomic characterization challenging. In order to facilitate genomic analysis, a fluorescence-activated cell sorting (FACS) method was developed in this study to separate D. mccartyi cells from a microbial community, and the DNA of the isolated cells was processed by whole genome amplification (WGA) and hybridized onto a D. mccartyi microarray for comparative genomics against four sequenced strains. First, FACS was successfully applied to a D. mccartyi isolate as positive control, and then microarray results verified that WGA from 10(6) cells or ∼1 ng of genomic DNA yielded high-quality coverage detecting nearly all genes across the genome. As expected, some inter- and intrasample variability in WGA was observed, but these biases were minimized by performing multiple parallel amplifications. Subsequent application of the FACS and WGA protocols to two enrichment cultures containing ∼10% and ∼1% D. mccartyi cells successfully enabled genomic analysis. As proof of concept, this study demonstrates that coupling FACS with WGA and microarrays is a promising tool to expedite genomic characterization of target strains in environmental communities where the relative concentrations are low.

mRNA Expression Profiling of Laser Microbeam Microdissected Cells from Slender Embryonic Structures

PubMed Central

Scheidl, Stefan J.; Nilsson, Sven; Kalén, Mattias; Hellström, Mats; Takemoto, Minoru; Håkansson, Joakim; Lindahl, Per

2002-01-01

Microarray hybridization has rapidly evolved as an important tool for genomic studies and studies of gene regulation at the transcriptome level. Expression profiles from homogenous samples such as yeast and mammalian cell cultures are currently extending our understanding of biology, whereas analyses of multicellular organisms are more difficult because of tissue complexity. The combination of laser microdissection, RNA amplification, and microarray hybridization has the potential to provide expression profiles from selected populations of cells in vivo. In this article, we present and evaluate an experimental procedure for global gene expression analysis of slender embryonic structures using laser microbeam microdissection and laser pressure catapulting. As a proof of principle, expression profiles from 1000 cells in the mouse embryonic (E9.5) dorsal aorta were generated and compared with profiles for captured mesenchymal cells located one cell diameter further away from the aortic lumen. A number of genes were overexpressed in the aorta, including 11 previously known markers for blood vessels. Among the blood vessel markers were endoglin, tie-2, PDGFB, and integrin-β1, that are important regulators of blood vessel formation. This demonstrates that microarray analysis of laser microbeam micro-dissected cells is sufficiently sensitive for identifying genes with regulative functions. PMID:11891179

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

[Differential gene expression in incompatible interaction between Lilium regale Wilson and Fusarium oxysporum f. sp. lilii revealed by combined SSH and microarray analysis].

PubMed

Rao, J; Liu, D; Zhang, N; He, H; Ge, F; Chen, C

2014-01-01

Fusarium wilt, caused by a soilborne pathogen Fusarium oxysporum f. sp. lilii, is the major disease of lily (Lilium L.). In order to isolate the genes differentially expressed in a resistant reaction to F. oxysporum in L. regale Wilson, a cDNA library was constructed with L. regale root during F. oxysporum infection using the suppression subtractive hybridization (SSH), and a total of 585 unique expressed sequence tags (ESTs) were obtained. Furthermore, the gene expression profiles in the incompatible interaction between L. regale and F. oxysporum were revealed by oligonucleotide microarray analysis of 585 unique ESTs comparison to the compatible interaction between a susceptible Lilium Oriental Hybrid 'Siberia' and F. oxysporum. The result of expression profile analysis indicated that the genes encoding pathogenesis-related proteins (PRs), antioxidative stress enzymes, secondary metabolism enzymes, transcription factors, signal transduction proteins as well as a large number of unknown genes were involved in early defense response of L. regale to F. oxysporum infection. Moreover, the following quantitative reverse transcription PCR (QRT-PCR) analysis confirmed reliability of the oligonucleotide microarray data. In the present study, isolation of differentially expressed genes in L. regale during response to F. oxysporum helped to uncover the molecular mechanism associated with the resistance of L. regale against F. oxysporum.

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

APPLICATION OF DNA MICROARRAYS TO REPRODUCTIVE TOXICOLOGY AND THE DEVELOPMENT OF A TESTIS ARRAY

EPA Science Inventory

With the advent of sequence information for entire mammalian genomes, it is now possible to analyze gene expression and gene polymorphisms on a genomic scale. The primary tool for analysis of gene expression is the DNA microarray. We have used commercially available cDNA micro...

Microarrays for Undergraduate Classes

ERIC Educational Resources Information Center

Hancock, Dale; Nguyen, Lisa L.; Denyer, Gareth S.; Johnston, Jill M.

2006-01-01

A microarray experiment is presented that, in six laboratory sessions, takes undergraduate students from the tissue sample right through to data analysis. The model chosen, the murine erythroleukemia cell line, can be easily cultured in sufficient quantities for class use. Large changes in gene expression can be induced in these cells by…

BIOMONITORING THE TOXICOGENOMIC RESPONSE TO ENDOCRINE DISRUPTING CHEMICALS IN HUMANS, LABORATORY SPECIES AND WILDLIFE

EPA Science Inventory

With the advent of sequence information for entire eukaryotic genomes, it is now possible to analyze gene expression on a genomic scale. The primary tool for genomic analysis of gene expression is the gene microarray. We have used commercially available and custom cDNA microarray...

CLOFIBRATE-INDUCED GENE EXPRESSION CHANGES IN RAT LIVER: A CROSS-LABORATORY ANALYSIS USING MEMBRANE CDNA ARRAYS

EPA Science Inventory

Microarrays have the potential to significantly impact our ability to identify toxic hazards by the identification of mechanistically-relevant markers of toxicity. To be useful for risk assessment however, microarray data must be challenged to determine its reliability and inter...

Comparative analysis of microarray data in Arabidopsis transcriptome during compatible interactions with plant viruses

USDA-ARS?s Scientific Manuscript database

To analyze transcriptome response to virus infection, we have assembled currently available microarray data on changes in gene expression levels in compatible Arabidopsis-virus interactions. We used the mean r (Pearson’s correlation coefficient) for neighboring pairs to estimate pairwise local simil...

Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis.

PubMed

Tylee, Daniel S; Hess, Jonathan L; Quinn, Thomas P; Barve, Rahul; Huang, Hailiang; Zhang-James, Yanli; Chang, Jeffrey; Stamova, Boryana S; Sharp, Frank R; Hertz-Picciotto, Irva; Faraone, Stephen V; Kong, Sek Won; Glatt, Stephen J

2017-04-01

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Blood Transcriptomic Comparison of Individuals with and without Autism Spectrum Disorder: A Combined-Samples Mega-Analysis

PubMed Central

Tylee, Daniel S.; Hess, Jonathan L.; Quinn, Thomas P.; Barve, Rahul; Huang, Hailiang; Zhang-James, Yanli; Chang, Jeffrey; Stamova, Boryana S.; Sharp, Frank R.; Hertz-Picciotto, Irva; Faraone, Stephen V.; Kong, Sek Won; Glatt, Stephen J.

2017-01-01

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex-vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. PMID:27862943

Building biochips: a protein production pipeline

NASA Astrophysics Data System (ADS)

de Carvalho-Kavanagh, Marianne G. S.; Albala, Joanna S.

2004-06-01

Protein arrays are emerging as a practical format in which to study proteins in high-throughput using many of the same techniques as that of the DNA microarray. The key advantage to array-based methods for protein study is the potential for parallel analysis of thousands of samples in an automated, high-throughput fashion. Building protein arrays capable of this analysis capacity requires a robust expression and purification system capable of generating hundreds to thousands of purified recombinant proteins. We have developed a method to utilize LLNL-I.M.A.G.E. cDNAs to generate recombinant protein libraries using a baculovirus-insect cell expression system. We have used this strategy to produce proteins for analysis of protein/DNA and protein/protein interactions using protein microarrays in order to understand the complex interactions of proteins involved in homologous recombination and DNA repair. Using protein array techniques, a novel interaction between the DNA repair protein, Rad51B, and histones has been identified.

Bioinformatics and Microarray Data Analysis on the Cloud.

PubMed

Calabrese, Barbara; Cannataro, Mario

2016-01-01

High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

Reproducibility-optimized test statistic for ranking genes in microarray studies.

PubMed

Elo, Laura L; Filén, Sanna; Lahesmaa, Riitta; Aittokallio, Tero

2008-01-01

A principal goal of microarray studies is to identify the genes showing differential expression under distinct conditions. In such studies, the selection of an optimal test statistic is a crucial challenge, which depends on the type and amount of data under analysis. While previous studies on simulated or spike-in datasets do not provide practical guidance on how to choose the best method for a given real dataset, we introduce an enhanced reproducibility-optimization procedure, which enables the selection of a suitable gene- anking statistic directly from the data. In comparison with existing ranking methods, the reproducibilityoptimized statistic shows good performance consistently under various simulated conditions and on Affymetrix spike-in dataset. Further, the feasibility of the novel statistic is confirmed in a practical research setting using data from an in-house cDNA microarray study of asthma-related gene expression changes. These results suggest that the procedure facilitates the selection of an appropriate test statistic for a given dataset without relying on a priori assumptions, which may bias the findings and their interpretation. Moreover, the general reproducibilityoptimization procedure is not limited to detecting differential expression only but could be extended to a wide range of other applications as well.

Systematic Validation and Atomic Force Microscopy of Non-Covalent Short Oligonucleotide Barcode Microarrays

PubMed Central

Cook, Michael A.; Chan, Chi-Kin; Jorgensen, Paul; Ketela, Troy; So, Daniel; Tyers, Mike; Ho, Chi-Yip

2008-01-01

Background Molecular barcode arrays provide a powerful means to analyze cellular phenotypes in parallel through detection of short (20–60 base) unique sequence tags, or “barcodes”, associated with each strain or clone in a collection. However, costs of current methods for microarray construction, whether by in situ oligonucleotide synthesis or ex situ coupling of modified oligonucleotides to the slide surface are often prohibitive to large-scale analyses. Methodology/Principal Findings Here we demonstrate that unmodified 20mer oligonucleotide probes printed on conventional surfaces show comparable hybridization signals to covalently linked 5′-amino-modified probes. As a test case, we undertook systematic cell size analysis of the budding yeast Saccharomyces cerevisiae genome-wide deletion collection by size separation of the deletion pool followed by determination of strain abundance in size fractions by barcode arrays. We demonstrate that the properties of a 13K unique feature spotted 20 mer oligonucleotide barcode microarray compare favorably with an analogous covalently-linked oligonucleotide array. Further, cell size profiles obtained with the size selection/barcode array approach recapitulate previous cell size measurements of individual deletion strains. Finally, through atomic force microscopy (AFM), we characterize the mechanism of hybridization to unmodified barcode probes on the slide surface. Conclusions/Significance These studies push the lower limit of probe size in genome-scale unmodified oligonucleotide microarray construction and demonstrate a versatile, cost-effective and reliable method for molecular barcode analysis. PMID:18253494

Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears.

PubMed

Rai, Muhammad Farooq; Tycksen, Eric D; Sandell, Linda J; Brophy, Robert H

2018-01-01

Microarrays and RNA-seq are at the forefront of high throughput transcriptome analyses. Since these methodologies are based on different principles, there are concerns about the concordance of data between the two techniques. The concordance of RNA-seq and microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed in clinically derived ligament tissues. To demonstrate the concordance between RNA-seq and microarrays and to assess potential benefits of RNA-seq over microarrays, we assessed differences in transcript expression in anterior cruciate ligament (ACL) tissues based on time-from-injury. ACL remnants were collected from patients with an ACL tear at the time of ACL reconstruction. RNA prepared from torn ACL remnants was subjected to Agilent microarrays (N = 24) and RNA-seq (N = 8). The correlation of biological replicates in RNA-seq and microarrays data was similar (0.98 vs. 0.97), demonstrating that each platform has high internal reproducibility. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarrays values were moderate. The cross-platform concordance for differentially expressed transcripts or enriched pathways was linearly correlated (r = 0.64). RNA-Seq was superior in detecting low abundance transcripts and differentiating biologically critical isoforms. Additional independent validation of transcript expression was undertaken using microfluidic PCR for selected genes. PCR data showed 100% concordance (in expression pattern) with RNA-seq and microarrays data. These findings demonstrate that RNA-seq has advantages over microarrays for transcriptome profiling of ligament tissues when available and affordable. Furthermore, these findings are likely transferable to other musculoskeletal tissues where tissue collection is challenging and cells are in low abundance. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:484-497, 2018. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Genotyping microarray: Mutation screening in Spanish families with autosomal dominant retinitis pigmentosa

PubMed Central

García-Hoyos, María; Cortón, Marta; Ávila-Fernández, Almudena; Riveiro-Álvarez, Rosa; Giménez, Ascensión; Hernan, Inma; Carballo, Miguel; Ayuso, Carmen

2012-01-01

Purpose Presently, 22 genes have been described in association with autosomal dominant retinitis pigmentosa (adRP); however, they explain only 50% of all cases, making genetic diagnosis of this disease difficult and costly. The aim of this study was to evaluate a specific genotyping microarray for its application to the molecular diagnosis of adRP in Spanish patients. Methods We analyzed 139 unrelated Spanish families with adRP. Samples were studied by using a genotyping microarray (adRP). All mutations found were further confirmed with automatic sequencing. Rhodopsin (RHO) sequencing was performed in all negative samples for the genotyping microarray. Results The adRP genotyping microarray detected the mutation associated with the disease in 20 of the 139 families with adRP. As in other populations, RHO was found to be the most frequently mutated gene in these families (7.9% of the microarray genotyped families). The rate of false positives (microarray results not confirmed with sequencing) and false negatives (mutations in RHO detected with sequencing but not with the genotyping microarray) were established, and high levels of analytical sensitivity (95%) and specificity (100%) were found. Diagnostic accuracy was 15.1%. Conclusions The adRP genotyping microarray is a quick, cost-efficient first step in the molecular diagnosis of Spanish patients with adRP. PMID:22736939

Importance of the efficiency of double-stranded DNA formation in cDNA synthesis for the imprecision of microarray expression analysis.

PubMed

Thormar, Hans G; Gudmundsson, Bjarki; Eiriksdottir, Freyja; Kil, Siyoen; Gunnarsson, Gudmundur H; Magnusson, Magnus Karl; Hsu, Jason C; Jonsson, Jon J

2013-04-01

The causes of imprecision in microarray expression analysis are poorly understood, limiting the use of this technology in molecular diagnostics. Two-dimensional strandness-dependent electrophoresis (2D-SDE) separates nucleic acid molecules on the basis of length and strandness, i.e., double-stranded DNA (dsDNA), single-stranded DNA (ssDNA), and RNA·DNA hybrids. We used 2D-SDE to measure the efficiency of cDNA synthesis and its importance for the imprecision of an in vitro transcription-based microarray expression analysis. The relative amount of double-stranded cDNA formed in replicate experiments that used the same RNA sample template was highly variable, ranging between 0% and 72% of the total DNA. Microarray experiments showed an inverse relationship between the difference between sample pairs in probe variance and the relative amount of dsDNA. Approximately 15% of probes showed between-sample variation (P < 0.05) when the dsDNA percentage was between 12% and 35%. In contrast, only 3% of probes showed between-sample variation when the dsDNA percentage was 69% and 72%. Replication experiments of the 35% dsDNA and 72% dsDNA samples were used to separate sample variation from probe replication variation. The estimated SD of the sample-to-sample variation and of the probe replicates was lower in 72% dsDNA samples than in 35% dsDNA samples. Variation in the relative amount of double-stranded cDNA synthesized can be an important component of the imprecision in T7 RNA polymerase-based microarray expression analysis. © 2013 American Association for Clinical Chemistry

Quantitative Proteomic and Microarray Analysis of the Archaeon Methanosarcina Acetivorans Grown with Acetate Versus Methanol*

PubMed Central

Li, Lingyun; Li, Qingbo; Rohlin, Lars; Kim, UnMi; Salmon, Kirsty; Rejtar, Tomas; Gunsalus, Robert P.; Karger, Barry L.; Ferry, James G.

2008-01-01

Summary Methanosarcina acetivorans strain C2A is an acetate- and methanol-utilizing methane-producing organism for which the genome, the largest yet sequenced among the Archaea, reveals extensive physiological diversity. LC linear ion trap-FTICR mass spectrometry was employed to analyze acetate- vs. methanol-grown cells metabolically labeled with 14N vs. 15N, respectively, to obtain quantitative protein abundance ratios. DNA microarray analyses of acetate- vs. methanol-grown cells was also performed to determine gene expression ratios. The combined approaches were highly complementary, extending the physiological understanding of growth and methanogenesis. Of the 1081 proteins detected, 255 were ≥ 3-fold differentially abundant. DNA microarray analysis revealed 410 genes that were ≥ 2.5-fold differentially expressed of 1972 genes with detected expression. The ratios of differentially abundant proteins were in good agreement with expression ratios of the encoding genes. Taken together, the results suggest several novel roles for electron transport components specific to acetate-grown cells, including two flavodoxins each specific for growth on acetate or methanol. Protein abundance ratios indicated that duplicate CO dehydrogenase/acetyl-CoA complexes function in the conversion of acetate to methane. Surprisingly, the protein abundance and gene expression ratios indicated a general stress response in acetate- vs. methanol-grown cells that included enzymes specific for polyphosphate accumulation and oxidative stress. The microarray analysis identified transcripts of several genes encoding regulatory proteins with identity to the PhoU, MarR, GlnK, and TetR families commonly found in the Bacteria domain. An analysis of neighboring genes suggested roles in controlling phosphate metabolism (PhoU), ammonia assimilation (GlnK), and molybdopterin cofactor biosynthesis (TetR). Finally, the proteomic and microarray results suggested roles for two-component regulatory systems specific for each growth substrate. PMID:17269732

BioconductorBuntu: a Linux distribution that implements a web-based DNA microarray analysis server.

PubMed

Geeleher, Paul; Morris, Dermot; Hinde, John P; Golden, Aaron

2009-06-01

BioconductorBuntu is a custom distribution of Ubuntu Linux that automatically installs a server-side microarray processing environment, providing a user-friendly web-based GUI to many of the tools developed by the Bioconductor Project, accessible locally or across a network. System installation is via booting off a CD image or by using a Debian package provided to upgrade an existing Ubuntu installation. In its current version, several microarray analysis pipelines are supported including oligonucleotide, dual-or single-dye experiments, including post-processing with Gene Set Enrichment Analysis. BioconductorBuntu is designed to be extensible, by server-side integration of further relevant Bioconductor modules as required, facilitated by its straightforward underlying Python-based infrastructure. BioconductorBuntu offers an ideal environment for the development of processing procedures to facilitate the analysis of next-generation sequencing datasets. BioconductorBuntu is available for download under a creative commons license along with additional documentation and a tutorial from (http://bioinf.nuigalway.ie).

A benchmark for statistical microarray data analysis that preserves actual biological and technical variance.

PubMed

De Hertogh, Benoît; De Meulder, Bertrand; Berger, Fabrice; Pierre, Michael; Bareke, Eric; Gaigneaux, Anthoula; Depiereux, Eric

2010-01-11

Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods. Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality. Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

Assessing the cleanliness of surfaces: Innovative molecular approaches vs. standard spore assays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cooper, M.; Duc, M.T. La; Probst, A.

2011-04-01

A bacterial spore assay and a molecular DNA microarray method were compared for their ability to assess relative cleanliness in the context of bacterial abundance and diversity on spacecraft surfaces. Colony counts derived from the NASA standard spore assay were extremely low for spacecraft surfaces. However, the PhyloChip generation 3 (G3) DNA microarray resolved the genetic signatures of a highly diverse suite of microorganisms in the very same sample set. Samples completely devoid of cultivable spores were shown to harbor the DNA of more than 100 distinct microbial phylotypes. Furthermore, samples with higher numbers of cultivable spores did not necessarilymore » give rise to a greater microbial diversity upon analysis with the DNA microarray. The findings of this study clearly demonstrated that there is not a statistically significant correlation between the cultivable spore counts obtained from a sample and the degree of bacterial diversity present. Based on these results, it can be stated that validated state-of-the-art molecular techniques, such as DNA microarrays, can be utilized in parallel with classical culture-based methods to further describe the cleanliness of spacecraft surfaces.« less

Identification of Novel Tissue-Specific Genes by Analysis of Microarray Databases: A Human and Mouse Model

PubMed Central

Suh, Yeunsu; Davis, Michael E.; Lee, Kichoon

2013-01-01

Understanding the tissue-specific pattern of gene expression is critical in elucidating the molecular mechanisms of tissue development, gene function, and transcriptional regulations of biological processes. Although tissue-specific gene expression information is available in several databases, follow-up strategies to integrate and use these data are limited. The objective of the current study was to identify and evaluate novel tissue-specific genes in human and mouse tissues by performing comparative microarray database analysis and semi-quantitative PCR analysis. We developed a powerful approach to predict tissue-specific genes by analyzing existing microarray data from the NCBI′s Gene Expression Omnibus (GEO) public repository. We investigated and confirmed tissue-specific gene expression in the human and mouse kidney, liver, lung, heart, muscle, and adipose tissue. Applying our novel comparative microarray approach, we confirmed 10 kidney, 11 liver, 11 lung, 11 heart, 8 muscle, and 8 adipose specific genes. The accuracy of this approach was further verified by employing semi-quantitative PCR reaction and by searching for gene function information in existing publications. Three novel tissue-specific genes were discovered by this approach including AMDHD1 (amidohydrolase domain containing 1) in the liver, PRUNE2 (prune homolog 2) in the heart, and ACVR1C (activin A receptor, type IC) in adipose tissue. We further confirmed the tissue-specific expression of these 3 novel genes by real-time PCR. Among them, ACVR1C is adipose tissue-specific and adipocyte-specific in adipose tissue, and can be used as an adipocyte developmental marker. From GEO profiles, we predicted the processes in which AMDHD1 and PRUNE2 may participate. Our approach provides a novel way to identify new sets of tissue-specific genes and to predict functions in which they may be involved. PMID:23741331

Controlled hydrostatic pressure stress downregulates the expression of ribosomal genes in preimplantation embryos: a possible protection mechanism?

PubMed

Bock, I; Raveh-Amit, H; Losonczi, E; Carstea, A C; Feher, A; Mashayekhi, K; Matyas, S; Dinnyes, A; Pribenszky, C

2016-04-01

The efficiency of various assisted reproductive techniques can be improved by preconditioning the gametes and embryos with sublethal hydrostatic pressure treatment. However, the underlying molecular mechanism responsible for this protective effect remains unknown and requires further investigation. Here, we studied the effect of optimised hydrostatic pressure treatment on the global gene expression of mouse oocytes after embryonic genome activation. Based on a gene expression microarray analysis, a significant effect of treatment was observed in 4-cell embryos derived from treated oocytes, revealing a transcriptional footprint of hydrostatic pressure-affected genes. Functional analysis identified numerous genes involved in protein synthesis that were downregulated in 4-cell embryos in response to hydrostatic pressure treatment, suggesting that regulation of translation has a major role in optimised hydrostatic pressure-induced stress tolerance. We present a comprehensive microarray analysis and further delineate a potential mechanism responsible for the protective effect of hydrostatic pressure treatment.

Interpretation of Genomic Data Questions and Answers

PubMed Central

Simon, Richard

2008-01-01

Using a question and answer format we describe important aspects of using genomic technologies in cancer research. The main challenges are not managing the mass of data, but rather the design, analysis and accurate reporting of studies that result in increased biological knowledge and medical utility. Many analysis issues address the use of expression microarrays but are also applicable to other whole genome assays. Microarray based clinical investigations have generated both unrealistic hyperbole and excessive skepticism. Genomic technologies are tremendously powerful and will play instrumental roles in elucidating the mechanisms of oncogenesis and in devlopingan era of predictive medicine in which treatments are tailored to individual tumors. Achieving these goals involves challenges in re-thinking many paradigms for the conduct of basic and clinical cancer research and for the organization of interdisciplinary collaboration. PMID:18582627

Bifidobacterium breve B-3 exerts metabolic syndrome-suppressing effects in the liver of diet-induced obese mice: a DNA microarray analysis.

PubMed

Kondo, S; Kamei, A; Xiao, J Z; Iwatsuki, K; Abe, K

2013-09-01

We previously reported that supplementation with Bifidobacterium breve B-3 reduced body weight gain and accumulation of visceral fat in a dose-dependent manner, and improved serum levels of total cholesterol, glucose and insulin in a mouse model of diet-induced obesity. In this study, we investigated the expression of genes in the liver using DNA microarray analysis and q-PCR to reveal the mechanism of these anti-obesity effects in this mouse model. Administration of B. breve B-3 led to regulated gene expression of pathways involved in lipid metabolism and response to stress. The results indicate that these regulations in the liver are related to the anti-metabolic syndrome effects of B. breve B-3.

DNA microarrays of baculovirus genomes: differential expression of viral genes in two susceptible insect cell lines.

PubMed

Yamagishi, J; Isobe, R; Takebuchi, T; Bando, H

2003-03-01

We describe, for the first time, the generation of a viral DNA chip for simultaneous expression measurements of nearly all known open reading frames (ORFs) in the best-studied members of the family Baculoviridae, Autographa californica multiple nucleopolyhedrovirus (AcMNPV) and Bombyx mori nucleopolyhedrovirus (BmNPV). In this study, a viral DNA chip (Ac-BmNPV chip) was fabricated and used to characterize the viral gene expression profile for AcMNPV in different cell types. The viral chip is composed of microarrays of viral DNA prepared by robotic deposition of PCR-amplified viral DNA fragments on glass for ORFs in the NPV genome. Viral gene expression was monitored by hybridization to the DNA fragment microarrays with fluorescently labeled cDNAs prepared from infected Spodoptera frugiperda, Sf9 cells and Trichoplusia ni, TnHigh-Five cells, the latter a major producer of baculovirus and recombinant proteins. A comparison of expression profiles of known ORFs in AcMNPV elucidated six genes (ORF150, p10, pk2, and three late gene expression factor genes lef-3, p35 and lef- 6) the expression of each of which was regulated differently in the two cell lines. Most of these genes are known to be closely involved in the viral life cycle such as in DNA replication, late gene expression and the release of polyhedra from infected cells. These results imply that the differential expression of these viral genes accounts for the differences in viral replication between these two cell lines. Thus, these fabricated microarrays of NPV DNA which allow a rapid analysis of gene expression at the viral genome level should greatly speed the functional analysis of large genomes of NPV.

Isolation of RNA From Peripheral Blood Cells: A Validation Study for Molecular Diagnostics by Microarray and Kinetic RT-PCR Assays - Application in Aerospace Medicine

DTIC Science & Technology

2004-01-01

of RNA From Peripheral Blood Cells: A Validation Study for Molecular Diagnostics by Microarray and Kinetic RT-PCR Assays  Application in...VALIDATION STUDY FOR MOLECULAR DIAGNOSTICS BY MICROARRAY AND KINETIC RT-PCR ASSAYS  APPLICATION IN AEROSPACE MEDICINE INTRODUCTION Extraction of cellular

Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway

PubMed Central

2011-01-01

Background Sporadic amyotrophic lateral sclerosis (sALS) is a motor neuron disease with poorly understood etiology. Results of gene expression profiling studies of whole blood from ALS patients have not been validated and are difficult to relate to ALS pathogenesis because gene expression profiles depend on the relative abundance of the different cell types present in whole blood. We conducted microarray analyses using Agilent Human Whole Genome 4 × 44k Arrays on a more homogeneous cell population, namely purified peripheral blood lymphocytes (PBLs), from ALS patients and healthy controls to identify molecular signatures possibly relevant to ALS pathogenesis. Methods Differentially expressed genes were determined by LIMMA (Linear Models for MicroArray) and SAM (Significance Analysis of Microarrays) analyses. The SAFE (Significance Analysis of Function and Expression) procedure was used to identify molecular pathway perturbations. Proteasome inhibition assays were conducted on cultured peripheral blood mononuclear cells (PBMCs) from ALS patients to confirm alteration of the Ubiquitin/Proteasome System (UPS). Results For the first time, using SAFE in a global gene ontology analysis (gene set size 5-100), we show significant perturbation of the KEGG (Kyoto Encyclopedia of Genes and Genomes) ALS pathway of motor neuron degeneration in PBLs from ALS patients. This was the only KEGG disease pathway significantly upregulated among 25, and contributing genes, including SOD1, represented 54% of the encoded proteins or protein complexes of the KEGG ALS pathway. Further SAFE analysis, including gene set sizes >100, showed that only neurodegenerative diseases (4 out of 34 disease pathways) including ALS were significantly upregulated. Changes in UBR2 expression correlated inversely with time since onset of disease and directly with ALSFRS-R, implying that UBR2 was increased early in the course of ALS. Cultured PBMCs from ALS patients accumulated more ubiquitinated proteins than PBMCs from healthy controls in a serum-dependent manner confirming changes in this pathway. Conclusions Our study indicates that PBLs from sALS patients are strong responders to systemic signals or local signals acquired by cell trafficking, representing changes in gene expression similar to those present in brain and spinal cord of sALS patients. PBLs may provide a useful means to study ALS pathogenesis. PMID:22027401

Comparisons of serum miRNA expression profiles in patients with diabetic retinopathy and type 2 diabetes mellitus.

PubMed

Ma, Jianping; Wang, Jufang; Liu, Yanfen; Wang, Changyi; Duan, Donghui; Lu, Nanjia; Wang, Kaiyue; Zhang, Lu; Gu, Kaibo; Chen, Sihan; Zhang, Tao; You, Dingyun; Han, Liyuan

2017-02-01

The aim of this study was to compare the expression levels of serum miRNAs in diabetic retinopathy and type 2 diabetes mellitus. Serum miRNA expression profiles from diabetic retinopathy cases (type 2 diabetes mellitus patients with diabetic retinopathy) and type 2 diabetes mellitus controls (type 2 diabetes mellitus patients without diabetic retinopathy) were examined by miRNA-specific microarray analysis. Quantitative real-time polymerase chain reaction was used to validate the significantly differentially expressed serum miRNAs from the microarray analysis of 45 diabetic retinopathy cases and 45 age-, sex-, body mass index- and duration-of-diabetes-matched type 2 diabetes mellitus controls. The relative changes in serum miRNA expression levels were analyzed using the 2-ΔΔCt method. A total of 5 diabetic retinopathy cases and 5 type 2 diabetes mellitus controls were included in the miRNA-specific microarray analysis. The serum levels of miR-3939 and miR-1910-3p differed significantly between the two groups in the screening stage; however, quantitative real-time polymerase chain reaction did not reveal significant differences in miRNA expression for 45 diabetic retinopathy cases and their matched type 2 diabetes mellitus controls. Our findings indicate that miR-3939 and miR-1910-3p may not play important roles in the development of diabetic retinopathy; however, studies with a larger sample size are needed to confirm our findings.

Analysis of baseline gene expression levels from toxicogenomics study control animals to identify sources of variation and predict responses to chemicals

EPA Science Inventory

The use of gene expression profiling to predict chemical mode of action would be enhanced by better characterization of variance due to individual, environmental, and technical factors. Meta-analysis of microarray data from untreated or vehicle-treated animals within the control ...

Association of HADHA expression with the risk of breast cancer: targeted subset analysis and meta-analysis of microarray data

PubMed Central

2012-01-01

Background The role of n-3 fatty acids in prevention of breast cancer is well recognized, but the underlying molecular mechanisms are still unclear. In view of the growing need for early detection of breast cancer, Graham et al. (2010) studied the microarray gene expression in histologically normal epithelium of subjects with or without breast cancer. We conducted a secondary analysis of this dataset with a focus on the genes (n = 47) involved in fat and lipid metabolism. We used stepwise multivariate logistic regression analyses, volcano plots and false discovery rates for association analyses. We also conducted meta-analyses of other microarray studies using random effects models for three outcomes--risk of breast cancer (380 breast cancer patients and 240 normal subjects), risk of metastasis (430 metastatic compared to 1104 non-metastatic breast cancers) and risk of recurrence (484 recurring versus 890 non-recurring breast cancers). Results The HADHA gene [hydroxyacyl-CoA dehydrogenase/3-ketoacyl-CoA thiolase/enoyl-CoA hydratase (trifunctional protein), alpha subunit] was significantly under-expressed in breast cancer; more so in those with estrogen receptor-negative status. Our meta-analysis showed an 18.4%-26% reduction in HADHA expression in breast cancer. Also, there was an inconclusive but consistent under-expression of HADHA in subjects with metastatic and recurring breast cancers. Conclusions Involvement of mitochondria and the mitochondrial trifunctional protein (encoded by HADHA gene) in breast carcinogenesis is known. Our results lend additional support to the possibility of this involvement. Further, our results suggest that targeted subset analysis of large genome-based datasets can provide interesting association signals. PMID:22240105

The pleiotropic effects of fisetin and hesperetin on human acute promyelocytic leukemia cells are mediated through apoptosis, cell cycle arrest, and alterations in signaling networks.

PubMed

Adan, Aysun; Baran, Yusuf

2015-11-01

Fisetin and hesperetin, flavonoids from various plants, have several pharmaceutical activities including antioxidative, anti-inflammatory, and anticancer effects. However, studies elucidating the role and the mechanism(s) of action of fisetin and hesperetin in acute promyelocytic leukemia are absent. In this study, we investigated the mechanism of the antiproliferative and apoptotic actions exerted by fisetin and hesperetin on human HL60 acute promyelocytic leukemia cells. The viability of HL60 cells was evaluated using the MTT assay, apoptosis by annexin V/propidium iodide (PI) staining and cell cycle distribution using flow cytometry, and changes in caspase-3 enzyme activity and mitochondrial transmembrane potential. Moreover, we performed whole-genome microarray gene expression analysis to reveal genes affected by fisetin and hesperetin that can be important for developing of future targeted therapy. Based on data obtained from microarray analysis, we also described biological networks modulated after fisetin and hesperetin treatment by KEGG and IPA analysis. Fisetin and hesperetin treatment showed a concentration- and time-dependent inhibition of proliferation and induced G2/M arrest for both agents and G0/G1 arrest for hesperetin at only the highest concentrations. There was a disruption of mitochondrial membrane potential together with increased caspase-3 activity. Furthermore, fisetin- and hesperetin-triggered apoptosis was confirmed by annexin V/PI analysis. The microarray gene profiling analysis revealed some important biological pathways including mitogen-activated protein kinases (MAPK) and inhibitor of DNA binding (ID) signaling pathways altered by fisetin and hesperetin treatment as well as gave a list of genes modulated ≥2-fold involved in cell proliferation, cell division, and apoptosis. Altogether, data suggested that fisetin and hesperetin have anticancer properties and deserve further investigation.

High-Throughput Quantification of SH2 Domain-Phosphopeptide Interactions with Cellulose-Peptide Conjugate Microarrays.

PubMed

Engelmann, Brett W

2017-01-01

The Src Homology 2 (SH2) domain family primarily recognizes phosphorylated tyrosine (pY) containing peptide motifs. The relative affinity preferences among competing SH2 domains for phosphopeptide ligands define "specificity space," and underpins many functional pY mediated interactions within signaling networks. The degree of promiscuity exhibited and the dynamic range of affinities supported by individual domains or phosphopeptides is best resolved by a carefully executed and controlled quantitative high-throughput experiment. Here, I describe the fabrication and application of a cellulose-peptide conjugate microarray (CPCMA) platform to the quantitative analysis of SH2 domain specificity space. Included herein are instructions for optimal experimental design with special attention paid to common sources of systematic error, phosphopeptide SPOT synthesis, microarray fabrication, analyte titrations, data capture, and analysis.

Microbial Diagnostic Microarrays for the Detection and Typing of Food- and Water-Borne (Bacterial) Pathogens

PubMed Central

Kostić, Tanja; Sessitsch, Angela

2011-01-01

Reliable and sensitive pathogen detection in clinical and environmental (including food and water) samples is of greatest importance for public health. Standard microbiological methods have several limitations and improved alternatives are needed. Most important requirements for reliable analysis include: (i) specificity; (ii) sensitivity; (iii) multiplexing potential; (iv) robustness; (v) speed; (vi) automation potential; and (vii) low cost. Microarray technology can, through its very nature, fulfill many of these requirements directly and the remaining challenges have been tackled. In this review, we attempt to compare performance characteristics of the microbial diagnostic microarrays developed for the detection and typing of food and water pathogens, and discuss limitations, points still to be addressed and issues specific for the analysis of food, water and environmental samples. PMID:27605332

A remark on copy number variation detection methods.

PubMed

Li, Shuo; Dou, Xialiang; Gao, Ruiqi; Ge, Xinzhou; Qian, Minping; Wan, Lin

2018-01-01

Copy number variations (CNVs) are gain and loss of DNA sequence of a genome. High throughput platforms such as microarrays and next generation sequencing technologies (NGS) have been applied for genome wide copy number losses. Although progress has been made in both approaches, the accuracy and consistency of CNV calling from the two platforms remain in dispute. In this study, we perform a deep analysis on copy number losses on 254 human DNA samples, which have both SNP microarray data and NGS data publicly available from Hapmap Project and 1000 Genomes Project respectively. We show that the copy number losses reported from Hapmap Project and 1000 Genome Project only have < 30% overlap, while these reports are required to have cross-platform (e.g. PCR, microarray and high-throughput sequencing) experimental supporting by their corresponding projects, even though state-of-art calling methods were employed. On the other hand, copy number losses are found directly from HapMap microarray data by an accurate algorithm, i.e. CNVhac, almost all of which have lower read mapping depth in NGS data; furthermore, 88% of which can be supported by the sequences with breakpoint in NGS data. Our results suggest the ability of microarray calling CNVs and the possible introduction of false negatives from the unessential requirement of the additional cross-platform supporting. The inconsistency of CNV reports from Hapmap Project and 1000 Genomes Project might result from the inadequate information containing in microarray data, the inconsistent detection criteria, or the filtration effect of cross-platform supporting. The statistical test on CNVs called from CNVhac show that the microarray data can offer reliable CNV reports, and majority of CNV candidates can be confirmed by raw sequences. Therefore, the CNV candidates given by a good caller could be highly reliable without cross-platform supporting, so additional experimental information should be applied in need instead of necessarily.

Advances in cell-free protein array methods.

PubMed

Yu, Xiaobo; Petritis, Brianne; Duan, Hu; Xu, Danke; LaBaer, Joshua

2018-01-01

Cell-free protein microarrays represent a special form of protein microarray which display proteins made fresh at the time of the experiment, avoiding storage and denaturation. They have been used increasingly in basic and translational research over the past decade to study protein-protein interactions, the pathogen-host relationship, post-translational modifications, and antibody biomarkers of different human diseases. Their role in the first blood-based diagnostic test for early stage breast cancer highlights their value in managing human health. Cell-free protein microarrays will continue to evolve to become widespread tools for research and clinical management. Areas covered: We review the advantages and disadvantages of different cell-free protein arrays, with an emphasis on the methods that have been studied in the last five years. We also discuss the applications of each microarray method. Expert commentary: Given the growing roles and impact of cell-free protein microarrays in research and medicine, we discuss: 1) the current technical and practical limitations of cell-free protein microarrays; 2) the biomarker discovery and verification pipeline using protein microarrays; and 3) how cell-free protein microarrays will advance over the next five years, both in their technology and applications.

A comprehensive simulation study on classification of RNA-Seq data.

PubMed

Zararsız, Gökmen; Goksuluk, Dincer; Korkmaz, Selcuk; Eldem, Vahap; Zararsiz, Gozde Erturk; Duru, Izzet Parug; Ozturk, Ahmet

2017-01-01

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNA-Seq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www.bioconductor.org/packages/release/bioc/html/MLSeq.html.

A High Throughput Protein Microarray Approach to Classify HIV Monoclonal Antibodies and Variant Antigens

PubMed Central

Dotsey, Emmanuel Y.; Gorlani, Andrea; Ingale, Sampat; Achenbach, Chad J.; Forthal, Donald N.; Felgner, Philip L.; Gach, Johannes S.

2015-01-01

In recent years, high throughput discovery of human recombinant monoclonal antibodies (mAbs) has been applied to greatly advance our understanding of the specificity, and functional activity of antibodies against HIV. Thousands of antibodies have been generated and screened in functional neutralization assays, and antibodies associated with cross-strain neutralization and passive protection in primates, have been identified. To facilitate this type of discovery, a high throughput-screening tool is needed to accurately classify mAbs, and their antigen targets. In this study, we analyzed and evaluated a prototype microarray chip comprised of the HIV-1 recombinant proteins gp140, gp120, gp41, and several membrane proximal external region peptides. The protein microarray analysis of 11 HIV-1 envelope-specific mAbs revealed diverse binding affinities and specificities across clades. Half maximal effective concentrations, generated by our chip analysis, correlated significantly (P<0.0001) with concentrations from ELISA binding measurements. Polyclonal immune responses in plasma samples from HIV-1 infected subjects exhibited different binding patterns, and reactivity against printed proteins. Examining the totality of the specificity of the humoral response in this way reveals the exquisite diversity, and specificity of the humoral response to HIV. PMID:25938510

Cloud-scale genomic signals processing classification analysis for gene expression microarray data.

PubMed

Harvey, Benjamin; Soo-Yeon Ji

2014-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring inference though analysis of DNA/mRNA sequence data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological inference by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale classification analysis of microarray data using Wavelet thresholding in a Cloud environment to identify significantly expressed features. This paper proposes a novel methodology that uses Wavelet based Denoising to initialize a threshold for determination of significantly expressed genes for classification. Additionally, this research was implemented and encompassed within cloud-based distributed processing environment. The utilization of Cloud computing and Wavelet thresholding was used for the classification 14 tumor classes from the Global Cancer Map (GCM). The results proved to be more accurate than using a predefined p-value for differential expression classification. This novel methodology analyzed Wavelet based threshold features of gene expression in a Cloud environment, furthermore classifying the expression of samples by analyzing gene patterns, which inform us of biological processes. Moreover, enabling researchers to face the present and forthcoming challenges that may arise in the analysis of data in functional genomics of large microarray datasets.

[Differentially expressed genes of cell signal transduction associated with benzene poisoning by cDNA microarray].

PubMed

Wang, Hong; Bi, Yongyi; Tao, Ning; Wang, Chunhong

2005-08-01

To detect the differential expression of cell signal transduction genes associated with benzene poisoning, and to explore the pathogenic mechanisms of blood system damage induced by benzene. Peripheral white blood cell gene expression profile of 7 benzene poisoning patients, including one aplastic anemia, was determined by cDNA microarray. Seven chips from normal workers were served as controls. Cluster analysis of gene expression profile was performed. Among the 4265 target genes, 176 genes associated with cell signal transduction were differentially expressed. 35 up-regulated genes including PTPRC, STAT4, IFITM1 etc were found in at least 6 pieces of microarray; 45 down-regulated genes including ARHB, PPP3CB, CDC37 etc were found in at least 5 pieces of microarray. cDNA microarray technology is an effective technique for screening the differentially expressed genes of cell signal transduction. Disorder in cell signal transduction may play certain role in the pathogenic mechanism of benzene poisoning.

Microarray analysis identified Puccinia striiformis f. sp. tritici genes involved in infection and sporulation.

USDA-ARS?s Scientific Manuscript database

Puccinia striiformis f. sp. tritici (Pst) causes stripe rust, one of the most important diseases of wheat worldwide. To identify Pst genes involved in infection and sporulation, a custom oligonucleotide Genechip was made using sequences of 442 genes selected from Pst cDNA libraries. Microarray analy...

Sensitive immunoassay detection of multiple environmental chemicals on protein microarrays using DNA/dye conjugate as a fluorescent label.

PubMed

Fan, Ziyan; Keum, Young Soo; Li, Qing X; Shelver, Weilin L; Guo, Liang-Hong

2012-05-01

Indirect competitive immunoassays were developed on protein microarrays for the sensitive and simultaneous detection of multiple environmental chemicals in one sample. In this assay, a DNA/SYTOX Orange conjugate was employed as an antibody label to increase the fluorescence signal and sensitivity of the immunoassays. Epoxy-modified glass slides were selected as the substrate for the production of 4 × 4 coating antigen microarrays. With this signal-enhancing system, competition curves for 17β-estradiol (E2), benzo[a]pyrene (BaP) and 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) were obtained individually on the protein microarray. The IC(50) and calculated limit of detection (LOD) are 0.32 μg L(-1) and 0.022 μg L(-1) for E2, 37.2 μg L(-1) and 24.5 μg L(-1) for BaP, and 31.6 μg L(-1) and 2.8 μg L(-1) for BDE-47, respectively. LOD of E2 is 14-fold lower than the value reported in a previous study using Cy3 labeled antibody (Du et al., Clin. Chem, 2005, 51, 368-375). The results of the microarray immunoassay were within 15% of chromatographic analysis for all three pollutants in spiked river water samples, thus verifying the immunoassay. Simultaneous detection of E2, BaP and BDE-47 in one sample was demonstrated. There was no cross-reaction in the immunoassay between these three environmental chemicals. These results suggest that microarray-based immunoassays with DNA/dye conjugate labels are useful tools for the rapid, sensitive, and high throughput screening of multiple environmental contaminants.

MICROARRAY SYSTEM FOR CONTAMINATED WATER ANALYSIS

EPA Science Inventory

We used the optimum slide treatment as determined by the previous study*: water plasma cleaning, photo-hydrolytic weathering, and silane treatment using 3-aminopropyl triethoxysilane (APS). Anti-E.coli antibodies were printed onto Corning 2947 (soda-lime-silicate) ...

Chromosomal microarray analysis as the first-tier test for the identification of pathogenic copy number variants in chromosome 9 pericentric regions and its challenge.

PubMed

Wang, Jia-Chi; Boyar, Fatih Z

2016-01-01

Chromosomal microarray analysis (CMA) has been recommended and practiced routinely in the large reference laboratories of U.S.A. as the first-tier test for the postnatal evaluation of individuals with intellectual disability, autism spectrum disorders, and/or multiple congenital anomalies. Using CMA as a diagnostic tool and without a routine setting of fluorescence in situ hybridization with labeled bacterial artificial chromosome probes (BAC-FISH) in the large reference laboratories becomes a challenge in the characterization of chromosome 9 pericentric region. This region has a very complex genomic structure and contains a variety of heterochromatic and euchromatic polymorphic variants. These variants were usually studied by G-banding, C-banding and BAC-FISH analysis. Chromosomal microarray analysis (CMA) was not recommended since it may lead to false positive results. Here, we presented a cohort of four cases, in which high-resolution CMA was used as the first-tier test or simultaneously with G-banding analysis on the proband to identify pathogenic copy number variants (CNVs) in the whole genome. CMA revealed large pathogenic CNVs from chromosome 9 in 3 cases which also revealed different G-banding patterns between the two chromosome 9 homologues. Although we demonstrated that high-resolution CMA played an important role in the identification of pathogenic copy number variants in chromosome 9 pericentric regions, the lack of BAC-FISH analysis or other useful tools renders significant challenges in the characterization of chromosome 9 pericentric regions. None; it is not a clinical trial, and the cases were retrospectively collected and analyzed.

A perspective on DNA microarray technology in food and nutritional science.

PubMed

Kato, Hisanori; Saito, Kenji; Kimura, Takeshi

2005-09-01

The functions of nutrients and other foods have been revealed at the level of gene regulation. The advent of DNA microarray technology has enabled us to analyze the body's response to these factors in a much more holistic manner than before. This review is intended to overview the present status of this DNA microarray technology, hoping to provide food and nutrition scientists, especially those who are planning to introduce this technology, with hints and suggestions. The number of papers examining transcriptomics analysis in food and nutrition science has expanded over the last few years. The effects of some dietary conditions and administration of specific nutrients or food factors are studied in various animal models and cultured cells. The target food components range from macronutrients and micronutrients to other functional food factors. Such studies have already yielded fruitful results, which include discovery of novel functions of a food, uncovering hitherto unknown mechanisms of action, and analyses of food safety. The potency of DNA microarray technology in food and nutrition science is broadly recognized. This technique will surely continue to provide researchers and the public with valuable information on the beneficial and adverse effects of food factors. It should also be acknowledged, however, that there remain problems such as standardization of the data and sharing of the results among researchers in this field.

An alternative method to amplify RNA without loss of signal conservation for expression analysis with a proteinase DNA microarray in the ArrayTube format.

PubMed

Schüler, Susann; Wenz, Ingrid; Wiederanders, B; Slickers, P; Ehricht, R

2006-06-12

Recent developments in DNA microarray technology led to a variety of open and closed devices and systems including high and low density microarrays for high-throughput screening applications as well as microarrays of lower density for specific diagnostic purposes. Beside predefined microarrays for specific applications manufacturers offer the production of custom-designed microarrays adapted to customers' wishes. Array based assays demand complex procedures including several steps for sample preparation (RNA extraction, amplification and sample labelling), hybridization and detection, thus leading to a high variability between several approaches and resulting in the necessity of extensive standardization and normalization procedures. In the present work a custom designed human proteinase DNA microarray of lower density in ArrayTube format was established. This highly economic open platform only requires standard laboratory equipment and allows the study of the molecular regulation of cell behaviour by proteinases. We established a procedure for sample preparation and hybridization and verified the array based gene expression profile by quantitative real-time PCR (QRT-PCR). Moreover, we compared the results with the well established Affymetrix microarray. By application of standard labelling procedures with e.g. Klenow fragment exo-, single primer amplification (SPA) or In Vitro Transcription (IVT) we noticed a loss of signal conservation for some genes. To overcome this problem we developed a protocol in accordance with the SPA protocol, in which we included target specific primers designed individually for each spotted oligomer. Here we present a complete array based assay in which only the specific transcripts of interest are amplified in parallel and in a linear manner. The array represents a proof of principle which can be adapted to other species as well. As the designed protocol for amplifying mRNA starts from as little as 100 ng total RNA, it presents an alternative method for detecting even low expressed genes by microarray experiments in a highly reproducible and sensitive manner. Preservation of signal integrity is demonstrated out by QRT-PCR measurements. The little amounts of total RNA necessary for the analyses make this method applicable for investigations with limited material as in clinical samples from, for example, organ or tumour biopsies. Those are arguments in favour of the high potential of our assay compared to established procedures for amplification within the field of diagnostic expression profiling. Nevertheless, the screening character of microarray data must be mentioned, and independent methods should verify the results.

Serum reactome induced by Bordetella pertussis infection and Pertussis vaccines: qualitative differences in serum antibody recognition patterns revealed by peptide microarray analysis.

PubMed

Valentini, Davide; Ferrara, Giovanni; Advani, Reza; Hallander, Hans O; Maeurer, Markus J

2015-07-01

Pertussis (whooping cough) remains a public health problem despite extensive vaccination strategies. Better understanding of the host-pathogen interaction and the detailed B. pertussis (Bp) target recognition pattern will help in guided vaccine design. We characterized the specific epitope antigen recognition profiles of serum antibodies ('the reactome') induced by whooping cough and B. pertussis (Bp) vaccines from a case-control study conducted in 1996 in infants enrolled in a Bp vaccine trial in Sweden (Gustafsson, NEJM, 1996, 334, 349-355). Sera from children with whooping cough, vaccinated with Diphtheria Tetanus Pertussis (DTP) whole-cell (wc), acellular 5 (DPTa5), or with the 2 component (a2) vaccines and from infants receiving only DT (n=10 for each group) were tested with high-content peptide microarrays containing 17 Bp proteins displayed as linear (n=3175) peptide stretches. Slides were incubated with serum and peptide-IgG complexes detected with Cy5-labeled goat anti-human IgG and analyzed using a GenePix 4000B microarray scanner, followed by statistical analysis, using PAM (Prediction Analysis for Microarrays) and the identification of uniquely recognized peptide epitopes. 367/3,085 (11.9%) peptides were recognized in 10/10 sera from children with whooping cough, 239 (7.7%) in DTPwc, 259 (8.4%) in DTPa5, 105 (3.4%) DTPa2, 179 (5.8%) in the DT groups. Recognition of strongly recognized peptides was similar between whooping cough and DPTwc, but statistically different between whooping cough vs. DTPa5 (p<0.05), DTPa2 and DT (p<0.001 vs. both) vaccines. 6/3,085 and 2/3,085 peptides were exclusively recognized in (10/10) sera from children with whooping cough and DTPa2 vaccination, respectively. DTPwc resembles more closely the whooping cough reactome as compared to acellular vaccines. We could identify a unique recognition signature common for each vaccination group (10/10 children). Peptide microarray technology allows detection of subtle differences in epitope signature responses and may help to guide rational vaccine development by the objective description of a clinically relevant immune response that confers protection against infectious pathogens.

Graph Based Study of Allergen Cross-Reactivity of Plant Lipid Transfer Proteins (LTPs) Using Microarray in a Multicenter Study

PubMed Central

Palacín, Arantxa; Gómez-Casado, Cristina; Rivas, Luis A.; Aguirre, Jacobo; Tordesillas, Leticia; Bartra, Joan; Blanco, Carlos; Carrillo, Teresa; Cuesta-Herranz, Javier; de Frutos, Consolación; Álvarez-Eire, Genoveva García; Fernández, Francisco J.; Gamboa, Pedro; Muñoz, Rosa; Sánchez-Monge, Rosa; Sirvent, Sofía; Torres, María J.; Varela-Losada, Susana; Rodríguez, Rosalía; Parro, Victor; Blanca, Miguel; Salcedo, Gabriel; Díaz-Perales, Araceli

2012-01-01

The study of cross-reactivity in allergy is key to both understanding. the allergic response of many patients and providing them with a rational treatment In the present study, protein microarrays and a co-sensitization graph approach were used in conjunction with an allergen microarray immunoassay. This enabled us to include a wide number of proteins and a large number of patients, and to study sensitization profiles among members of the LTP family. Fourteen LTPs from the most frequent plant food-induced allergies in the geographical area studied were printed into a microarray specifically designed for this research. 212 patients with fruit allergy and 117 food-tolerant pollen allergic subjects were recruited from seven regions of Spain with different pollen profiles, and their sera were tested with allergen microarray. This approach has proven itself to be a good tool to study cross-reactivity between members of LTP family, and could become a useful strategy to analyze other families of allergens. PMID:23272072

Microarray evaluation of gene expression profiles in inflamed and healthy human dental pulp: the role of IL1beta and CD40 in pulp inflammation.

PubMed

Gatta, V; Zizzari, V L; Dd ' Amico, V; Salini, L; D' Aurora, M; Franchi, S; Antonucci, I; Sberna, M T; Gherlone, E; Stuppia, L; Tetè, S

2012-01-01

Dental pulp undergoes a number of changes passing from healthy status to inflammation due to deep decay. These changes are regulated by several genes resulting differently expressed in inflamed and healthy dental pulp, and the knowledge of the processes underlying this differential expression is of great relevance in the identification of the pathogenesis of the disease. In this study, the gene expression profile of inflamed and healthy dental pulps were compared by microarray analysis, and data obtained were analyzed by Ingenuity Pathway Analysis (IPA) software. This analysis allows to focus on a variety of genes, typically expressed in inflamed tissues. The comparison analysis showed an increased expression of several genes in inflamed pulp, among which IL1β and CD40 resulted of particular interest. These results indicate that gene expression profile of human dental pulp in different physiological and pathological conditions may become an useful tool for improving our knowledge about processes regulating pulp inflammation.

Comparative pharmacokinetics of rhein in normal and loperamide-induced constipated rats and microarray analysis of drug-metabolizing genes.

PubMed

Hou, Mei-Ling; Chang, Li-Wen; Lin, Chi-Hung; Lin, Lie-Chwen; Tsai, Tung-Hu

2014-09-11

Rhein is a pharmacological active component found in Rheum palmatum L. that is the major herb of the San-Huang-Xie-Xin-Tang (SHXXT), a medicinal herbal product used as a remedy for constipation. Here we have investigated the comparative pharmacokinetics of rhein in normal and constipated rats. Microarray analysis was used to explore whether drug-metabolizing genes will be altered after SHXXT treatment. The comparative pharmacokinetics of rhein in normal and loperamide-induced constipated rats was studied by liquid chromatography with electrospray ionization tandem mass spectrometry (LC-MS/MS). Gene expression profiling in drug-metabolizing genes after SHXXT treatment was investigated by microarray analysis and real-time polymerase chain reaction (RT-PCR). A validated LC-MS/MS method was applied to investigate the comparative pharmacokinetics of rhein in normal and loperamide-induced constipated rats. The pharmacokinetic results demonstrate that the loperamide-induced constipation reduced the absorption of rhein. Cmax significantly reduced by 2.5-fold, the AUC decreased by 27.8%; however, the elimination half-life (t1/2) was prolonged by 1.6-fold. Tmax and mean residence time (MRT) were significantly prolonged by 2.8-fold, and 1.7-fold, respectively. The volume of distribution (Vss) increased by 2.2-fold. The data of microarray analysis on gene expression indicate that five drug-metabolizing genes, including Cyp7a1, Cyp2c6, Ces2e, Atp1b1, and Slc7a2 were significantly altered by the SHXXT (0.5 g/kg) treatment. The loperamide-induced constipation reduced the absorption of rhein. Since among the 25,338 genes analyzed, there were five genes significantly altered by SHXXT treatment. Thus, information on minor drug-metabolizing genes altered by SHXXT treatment indicates that SHXXT is relatively safe for clinical application. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Overexpression of GRß in colonic mucosal cell line partly reflects altered gene expression in colonic mucosa of patients with inflammatory bowel disease.

PubMed

Nagy, Zsolt; Acs, Bence; Butz, Henriett; Feldman, Karolina; Marta, Alexa; Szabo, Peter M; Baghy, Kornelia; Pazmany, Tamas; Racz, Karoly; Liko, Istvan; Patocs, Attila

2016-01-01

The glucocorticoid receptor (GR) plays a crucial role in inflammatory responses. GR has several isoforms, of which the most deeply studied are the GRα and GRß. Recently it has been suggested that in addition to its negative dominant effect on GRα, the GRß may have a GRα-independent transcriptional activity. The GRß isoform was found to be frequently overexpressed in various autoimmune diseases, including inflammatory bowel disease (IBD). In this study, we wished to test whether the gene expression profile found in a GRß overexpressing intestinal cell line (Caco-2GRß) might mimic the gene expression alterations found in patients with IBD. Whole genome microarray analysis was performed in both normal and GRß overexpressing Caco-2 cell lines with and without dexamethasone treatment. IBD-related genes were identified from a meta-analysis of 245 microarrays available in online microarray deposits performed on intestinal mucosa samples from patients with IBD and healthy individuals. The differentially expressed genes were further studied using in silico pathway analysis. Overexpression of GRß altered a large proportion of genes that were not regulated by dexamethasone suggesting that GRß may have a GRα-independent role in the regulation of gene expression. About 10% of genes differentially expressed in colonic mucosa samples from IBD patients compared to normal subjects were also detected in Caco-2 GRß intestinal cell line. Common genes are involved in cell adhesion and cell proliferation. Overexpression of GRß in intestinal cells may affect appropriate mucosal repair and intact barrier function. The proposed novel role of GRß in intestinal epithelium warrants further studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

A bioinformatics approach to identify patients with symptomatic peanut allergy using peptide microarray immunoassay

PubMed Central

Lin, Jing; Bruni, Francesca M.; Fu, Zhiyan; Maloney, Jennifer; Bardina, Ludmilla; Boner, Attilio L.; Gimenez, Gustavo; Sampson, Hugh A.

2013-01-01

Background Peanut allergy is relatively common, typically permanent, and often severe. Double-blind, placebo-controlled food challenge is considered the gold standard for the diagnosis of food allergy–related disorders. However, the complexity and potential of double-blind, placebo-controlled food challenge to cause life-threatening allergic reactions affects its clinical application. A laboratory test that could accurately diagnose symptomatic peanut allergy would greatly facilitate clinical practice. Objective We sought to develop an allergy diagnostic method that could correctly predict symptomatic peanut allergy by using peptide microarray immunoassays and bioinformatic methods. Methods Microarray immunoassays were performed by using the sera from 62 patients (31 with symptomatic peanut allergy and 31 who had outgrown their peanut allergy or were sensitized but were clinically tolerant to peanut). Specific IgE and IgG4 binding to 419 overlapping peptides (15 mers, 3 offset) covering the amino acid sequences of Ara h 1, Ara h 2, and Ara h 3 were measured by using a peptide microarray immunoassay. Bioinformatic methods were applied for data analysis. Results Individuals with peanut allergy showed significantly greater IgE binding and broader epitope diversity than did peanut-tolerant individuals. No significant difference in IgG4 binding was found between groups. By using machine learning methods, 4 peptide biomarkers were identified and prediction models that can predict the outcome of double-blind, placebo-controlled food challenges with high accuracy were developed by using a combination of the biomarkers. Conclusions In this study, we developed a novel diagnostic approach that can predict peanut allergy with high accuracy by combining the results of a peptide microarray immunoassay and bioinformatic methods. Further studies are needed to validate the efficacy of this assay in clinical practice. PMID:22444503

A hybrid approach to device integration on a genetic analysis platform

NASA Astrophysics Data System (ADS)

Brennan, Des; Jary, Dorothee; Kurg, Ants; Berik, Evgeny; Justice, John; Aherne, Margaret; Macek, Milan; Galvin, Paul

2012-10-01

Point-of-care (POC) systems require significant component integration to implement biochemical protocols associated with molecular diagnostic assays. Hybrid platforms where discrete components are combined in a single platform are a suitable approach to integration, where combining multiple device fabrication steps on a single substrate is not possible due to incompatible or costly fabrication steps. We integrate three devices each with a specific system functionality: (i) a silicon electro-wetting-on-dielectric (EWOD) device to move and mix sample and reagent droplets in an oil phase, (ii) a polymer microfluidic chip containing channels and reservoirs and (iii) an aqueous phase glass microarray for fluorescence microarray hybridization detection. The EWOD device offers the possibility of fully integrating on-chip sample preparation using nanolitre sample and reagent volumes. A key challenge is sample transfer from the oil phase EWOD device to the aqueous phase microarray for hybridization detection. The EWOD device, waveguide performance and functionality are maintained during the integration process. An on-chip biochemical protocol for arrayed primer extension (APEX) was implemented for single nucleotide polymorphism (SNiP) analysis. The prepared sample is aspirated from the EWOD oil phase to the aqueous phase microarray for hybridization. A bench-top instrumentation system was also developed around the integrated platform to drive the EWOD electrodes, implement APEX sample heating and image the microarray after hybridization.

Genomic profiling using array comparative genomic hybridization define distinct subtypes of diffuse large b-cell lymphoma: a review of the literature

PubMed Central

2012-01-01

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin Lymphoma comprising of greater than 30% of adult non-Hodgkin Lymphomas. DLBCL represents a diverse set of lymphomas, defined as diffuse proliferation of large B lymphoid cells. Numerous cytogenetic studies including karyotypes and fluorescent in situ hybridization (FISH), as well as morphological, biological, clinical, microarray and sequencing technologies have attempted to categorize DLBCL into morphological variants, molecular and immunophenotypic subgroups, as well as distinct disease entities. Despite such efforts, most lymphoma remains undistinguishable and falls into DLBCL, not otherwise specified (DLBCL-NOS). The advent of microarray-based studies (chromosome, RNA, gene expression, etc) has provided a plethora of high-resolution data that could potentially facilitate the finer classification of DLBCL. This review covers the microarray data currently published for DLBCL. We will focus on these types of data; 1) array based CGH; 2) classical CGH; and 3) gene expression profiling studies. The aims of this review were three-fold: (1) to catalog chromosome loci that are present in at least 20% or more of distinct DLBCL subtypes; a detailed list of gains and losses for different subtypes was generated in a table form to illustrate specific chromosome loci affected in selected subtypes; (2) to determine common and distinct copy number alterations among the different subtypes and based on this information, characteristic and similar chromosome loci for the different subtypes were depicted in two separate chromosome ideograms; and, (3) to list re-classified subtypes and those that remained indistinguishable after review of the microarray data. To the best of our knowledge, this is the first effort to compile and review available literatures on microarray analysis data and their practical utility in classifying DLBCL subtypes. Although conventional cytogenetic methods such as Karyotypes and FISH have played a major role in classification schemes of lymphomas, better classification models are clearly needed to further understanding the biology, disease outcome and therapeutic management of DLBCL. In summary, microarray data reviewed here can provide better subtype specific classifications models for DLBCL. PMID:22967872

Use of homologous and heterologous gene expression profiling tools to characterize transcription dynamics during apple fruit maturation and ripening

PubMed Central

2010-01-01

Background Fruit development, maturation and ripening consists of a complex series of biochemical and physiological changes that in climacteric fruits, including apple and tomato, are coordinated by the gaseous hormone ethylene. These changes lead to final fruit quality and understanding of the functional machinery underlying these processes is of both biological and practical importance. To date many reports have been made on the analysis of gene expression in apple. In this study we focused our investigation on the role of ethylene during apple maturation, specifically comparing transcriptomics of normal ripening with changes resulting from application of the hormone receptor competitor 1-Methylcyclopropene. Results To gain insight into the molecular process regulating ripening in apple, and to compare to tomato (model species for ripening studies), we utilized both homologous and heterologous (tomato) microarray to profile transcriptome dynamics of genes involved in fruit development and ripening, emphasizing those which are ethylene regulated. The use of both types of microarrays facilitated transcriptome comparison between apple and tomato (for the later using data previously published and available at the TED: tomato expression database) and highlighted genes conserved during ripening of both species, which in turn represent a foundation for further comparative genomic studies. The cross-species analysis had the secondary aim of examining the efficiency of heterologous (specifically tomato) microarray hybridization for candidate gene identification as related to the ripening process. The resulting transcriptomics data revealed coordinated gene expression during fruit ripening of a subset of ripening-related and ethylene responsive genes, further facilitating the analysis of ethylene response during fruit maturation and ripening. Conclusion Our combined strategy based on microarray hybridization enabled transcriptome characterization during normal climacteric apple ripening, as well as definition of ethylene-dependent transcriptome changes. Comparison with tomato fruit maturation and ethylene responsive transcriptome activity facilitated identification of putative conserved orthologous ripening-related genes, which serve as an initial set of candidates for assessing conservation of gene activity across genomes of fruit bearing plant species. PMID:20973957

DNA Microarray for Rapid Detection and Identification of Food and Water Borne Bacteria: From Dry to Wet Lab.

PubMed

Ranjbar, Reza; Behzadi, Payam; Najafi, Ali; Roudi, Raheleh

2017-01-01

A rapid, accurate, flexible and reliable diagnostic method may significantly decrease the costs of diagnosis and treatment. Designing an appropriate microarray chip reduces noises and probable biases in the final result. The aim of this study was to design and construct a DNA Microarray Chip for a rapid detection and identification of 10 important bacterial agents. In the present survey, 10 unique genomic regions relating to 10 pathogenic bacterial agents including Escherichia coli (E.coli), Shigella boydii, Sh.dysenteriae, Sh.flexneri, Sh.sonnei, Salmonella typhi, S.typhimurium, Brucella sp., Legionella pneumophila, and Vibrio cholera were selected for designing specific long oligo microarray probes. For this reason, the in-silico operations including utilization of the NCBI RefSeq database, Servers of PanSeq and Gview, AlleleID 7.7 and Oligo Analyzer 3.1 was done. On the other hand, the in-vitro part of the study comprised stages of robotic microarray chip probe spotting, bacterial DNAs extraction and DNA labeling, hybridization and microarray chip scanning. In wet lab section, different tools and apparatus such as Nexterion® Slide E, Qarray mini spotter, NimbleGen kit, TrayMix TM S4, and Innoscan 710 were used. A DNA microarray chip including 10 long oligo microarray probes was designed and constructed for detection and identification of 10 pathogenic bacteria. The DNA microarray chip was capable to identify all 10 bacterial agents tested simultaneously. The presence of a professional bioinformatician as a probe designer is needed to design appropriate multifunctional microarray probes to increase the accuracy of the outcomes.

Design and evaluation of Actichip, a thematic microarray for the study of the actin cytoskeleton

PubMed Central

Muller, Jean; Mehlen, André; Vetter, Guillaume; Yatskou, Mikalai; Muller, Arnaud; Chalmel, Frédéric; Poch, Olivier; Friederich, Evelyne; Vallar, Laurent

2007-01-01

Background The actin cytoskeleton plays a crucial role in supporting and regulating numerous cellular processes. Mutations or alterations in the expression levels affecting the actin cytoskeleton system or related regulatory mechanisms are often associated with complex diseases such as cancer. Understanding how qualitative or quantitative changes in expression of the set of actin cytoskeleton genes are integrated to control actin dynamics and organisation is currently a challenge and should provide insights in identifying potential targets for drug discovery. Here we report the development of a dedicated microarray, the Actichip, containing 60-mer oligonucleotide probes for 327 genes selected for transcriptome analysis of the human actin cytoskeleton. Results Genomic data and sequence analysis features were retrieved from GenBank and stored in an integrative database called Actinome. From these data, probes were designed using a home-made program (CADO4MI) allowing sequence refinement and improved probe specificity by combining the complementary information recovered from the UniGene and RefSeq databases. Actichip performance was analysed by hybridisation with RNAs extracted from epithelial MCF-7 cells and human skeletal muscle. Using thoroughly standardised procedures, we obtained microarray images with excellent quality resulting in high data reproducibility. Actichip displayed a large dynamic range extending over three logs with a limit of sensitivity between one and ten copies of transcript per cell. The array allowed accurate detection of small changes in gene expression and reliable classification of samples based on the expression profiles of tissue-specific genes. When compared to two other oligonucleotide microarray platforms, Actichip showed similar sensitivity and concordant expression ratios. Moreover, Actichip was able to discriminate the highly similar actin isoforms whereas the two other platforms did not. Conclusion Our data demonstrate that Actichip is a powerful alternative to commercial high density microarrays for cytoskeleton gene profiling in normal or pathological samples. Actichip is available upon request. PMID:17727702

Automatic image analysis and spot classification for detection of pathogenic Escherichia coli on glass slide DNA microarrays

USDA-ARS?s Scientific Manuscript database

A computer algorithm was created to inspect scanned images from DNA microarray slides developed to rapidly detect and genotype E. Coli O157 virulent strains. The algorithm computes centroid locations for signal and background pixels in RGB space and defines a plane perpendicular to the line connect...

Microarray as a First Genetic Test in Global Developmental Delay: A Cost-Effectiveness Analysis

ERIC Educational Resources Information Center

Trakadis, Yannis; Shevell, Michael

2011-01-01

Aim: Microarray technology has a significantly higher clinical yield than karyotyping in individuals with global developmental delay (GDD). Despite this, it has not yet been routinely implemented as a screening test owing to the perception that this approach is more expensive. We aimed to evaluate the effect that replacing karyotype with…

Assessing probe-specific dye and slide biases in two-color microarray data

USDA-ARS?s Scientific Manuscript database

A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide and that bind to probes on the same spot is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences bet...

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

PubMed Central

Reif, David M.; Israel, Mark A.; Moore, Jason H.

2007-01-01

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org. PMID:19390666

Clustering-based spot segmentation of cDNA microarray images.

PubMed

Uslan, Volkan; Bucak, Ihsan Ömür

2010-01-01

Microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarray image processing has been implemented. Clustering-based methods, fuzzy c-means and k-means, have been applied for the segmentation step that separates the spots from the background. The experiments show that fuzzy c-means have segmented spots of the microarray image more accurately than the k-means.

Prognostic value of matrix metalloproteinase 9 expression in patients with juvenile nasopharyngeal angiofibroma: tissue microarray analysis.

PubMed

Sun, Xicai; Guo, Limin; Wang, Jingjing; Wang, Huan; Liu, Zhuofu; Liu, Juan; Yu, Huapeng; Hu, Li; Li, Han; Wang, Dehui

2014-08-01

Although JNA is a benign neoplasm histopathologically, it has a propensity for locally destructive growth and remains a higher postoperative recurrence rate. The aim of this study was to analyze the expression and localization of MMP-9 in JNA using tissue microarray to elucidate its correlation with clinicopathological features and recurrence. The expression of MMP-9 was assessed by immunohistochemistry in a tissue microarray from 70 patients with JNA and 10 control subjects. Correlation between the levels of MMP-9 expression and clinicopathologic variables, as well as tumor recurrence, were analyzed. MMP-9 was detected in perivascular and extravascular less differentiated cells and stromal cells of patients with JNA but not in the matured vascular endothelial cells of these patients. The presence of MMP-9 expression in JNA was correlated with patient's age (p=0.001). Spearman correlation analysis suggested that high expression of MMP-9 in JNA had negative correlation with patient's age (r=-0.412, p<0.001). The recurrence rate in JNA patients with high MMP-9 expression was significantly higher than those with low MMP-9 expression (p=0.002). In multivariate and ROC curve analysis, MMP-9 was a good prognostic factor for tumor recurrence of JNA. Higher MMP-9 expression is a poor prognostic factor for patients with JNA who have been surgically treated. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Comprehensive analysis of differentially expressed profiles of lncRNAs and construction of miR-133b mediated ceRNA network in colorectal cancer.

PubMed

Wu, Hao; Wu, Runliu; Chen, Miao; Li, Daojiang; Dai, Jing; Zhang, Yi; Gao, Kai; Yu, Jun; Hu, Gui; Guo, Yihang; Lin, Changwei; Li, Xiaorong

2017-03-28

Growing evidence suggests that long non-coding RNAs (lncRNAs) play a key role in tumorigenesis. However, the mechanism remains largely unknown. Thousands of significantly dysregulated lncRNAs and mRNAs were identified by microarray. Furthermore, a miR-133b-meditated lncRNA-mRNA ceRNA network was revealed, a subset of which was validated in 14 paired CRC patient tumor/non-tumor samples. Gene set enrichment analysis (GSEA) results demonstrated that lncRNAs ENST00000520055 and ENST00000535511 shared KEGG pathways with miR-133b target genes. We used microarrays to survey the lncRNA and mRNA expression profiles of colorectal cancer and para-cancer tissues. Gene Ontology (GO) and KEGG pathway enrichment analyses were performed to explore the functions of the significantly dysregulated genes. An innovate method was employed that combined analyses of two microarray data sets to construct a miR-133b-mediated lncRNA-mRNA competing endogenous RNAs (ceRNA) network. Quantitative RT-PCR analysis was used to validate part of this network. GSEA was used to predict the potential functions of these lncRNAs. This study identifies and validates a new method to investigate the miR-133b-mediated lncRNA-mRNA ceRNA network and lays the foundation for future investigation into the role of lncRNAs in colorectal cancer.

Large scale aggregate microarray analysis reveals three distinct molecular subclasses of human preeclampsia.

PubMed

Leavey, Katherine; Bainbridge, Shannon A; Cox, Brian J

2015-01-01

Preeclampsia (PE) is a life-threatening hypertensive pathology of pregnancy affecting 3-5% of all pregnancies. To date, PE has no cure, early detection markers, or effective treatments short of the removal of what is thought to be the causative organ, the placenta, which may necessitate a preterm delivery. Additionally, numerous small placental microarray studies attempting to identify "PE-specific" genes have yielded inconsistent results. We therefore hypothesize that preeclampsia is a multifactorial disease encompassing several pathology subclasses, and that large cohort placental gene expression analysis will reveal these groups. To address our hypothesis, we utilized known bioinformatic methods to aggregate 7 microarray data sets across multiple platforms in order to generate a large data set of 173 patient samples, including 77 with preeclampsia. Unsupervised clustering of these patient samples revealed three distinct molecular subclasses of PE. This included a "canonical" PE subclass demonstrating elevated expression of known PE markers and genes associated with poor oxygenation and increased secretion, as well as two other subclasses potentially representing a poor maternal response to pregnancy and an immunological presentation of preeclampsia. Our analysis sheds new light on the heterogeneity of PE patients, and offers up additional avenues for future investigation. Hopefully, our subclassification of preeclampsia based on molecular diversity will finally lead to the development of robust diagnostics and patient-based treatments for this disorder.

Microarray Analysis of Gene Expression Alteration in Human Middle Ear Epithelial Cells Induced by Asian Sand Dust.

PubMed

Go, Yoon Young; Park, Moo Kyun; Kwon, Jee Young; Seo, Young Rok; Chae, Sung-Won; Song, Jae-Jun

2015-12-01

The primary aim of this study is to evaluate the gene expression profile of Asian sand dust (ASD)-treated human middle ear epithelial cell (HMEEC) using microarray analysis. The HMEEC was treated with ASD (400 µg/mL) and total RNA was extracted for microarray analysis. Molecular pathways among differentially expressed genes were further analyzed. For selected genes, the changes in gene expression were confirmed by real-time polymerase chain reaction. A total of 1,274 genes were differentially expressed by ASD. Among them, 1,138 genes were 2 folds up-regulated, whereas 136 genes were 2 folds down-regulated. Up-regulated genes were mainly involved in cellular processes, including apoptosis, cell differentiation, and cell proliferation. Down-regulated genes affected cellular processes, including apoptosis, cell cycle, cell differentiation, and cell proliferation. The 10 genes including ADM, CCL5, EDN1, EGR1, FOS, GHRL, JUN, SOCS3, TNF, and TNFSF10 were identified as main modulators in up-regulated genes. A total of 11 genes including CSF3, DKK1, FOSL1, FST, TERT, MMP13, PTHLH, SPRY2, TGFBR2, THBS1, and TIMP1 acted as main components of pathway associated with 2-fold down regulated genes. We identified the differentially expressed genes in ASD-treated HMEEC. Our work indicates that air pollutant like ASD, may play an important role in the pathogenesis of otitis media.

Microarray profiles reveal that circular RNA hsa_circ_0007385 functions as an oncogene in non-small cell lung cancer tumorigenesis.

PubMed

Jiang, Ming-Ming; Mai, Zhi-Tao; Wan, Shan-Zhi; Chi, Yu-Min; Zhang, Xin; Sun, Bao-Hua; Di, Qing-Guo

2018-04-01

Circular RNAs (circRNAs) are a novel class of non-protein-coding RNA. Emerging evidence indicates that circRNAs participate in the regulation of many pathophysiological processes. This study aims to explore the expression profiles and pathological effects of circRNAs in non-small cell lung cancer (NSCLC). Human circRNAs microarray analysis was performed to screen the expression profile of circRNAs in NSCLC tissue. Expressions of circRNA and miRNA in NSCLC tissues and cells were quantified by qRTPCR. Functional experiments were performed to investigate the biological functions of circRNA, including CCK-8 assay, colony formation assay, transwell assay and xenograft in vivo assay. Human circRNAs microarray revealed a total 957 abnormally expressed circRNAs (> twofold, P < 0.05) in NSCLC tissue compared with adjacent normal tissue. In further studies, hsa_circ_0007385 was significantly up regulated in NSCLC tissue and cells. In vitro experiments with hsa_circ_0007385 knockdown resulted in significant suppression of the proliferation, migration and invasion of NSCLC cells. In vivo xenograft assay using hsa_circ_0007385 knockdown, significantly reduced tumor growth. Bioinformatics analysis and luciferase reporter assay verified the potential target miR-181, suggesting a possible regulatory pathway for hsa_circ_0007385. In summary, results suggest hsa_circ_0007385 plays a role in NSCLC tumorigenesis, providing a potential therapeutic target for NSCLC.

Autoregressive-model-based missing value estimation for DNA microarray time series data.

PubMed

Choong, Miew Keen; Charbit, Maurice; Yan, Hong

2009-01-01

Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

Detecting novel genes with sparse arrays

PubMed Central

Haiminen, Niina; Smit, Bart; Rautio, Jari; Vitikainen, Marika; Wiebe, Marilyn; Martinez, Diego; Chee, Christine; Kunkel, Joe; Sanchez, Charles; Nelson, Mary Anne; Pakula, Tiina; Saloheimo, Markku; Penttilä, Merja; Kivioja, Teemu

2014-01-01

Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. PMID:20691772

Genetic programming based ensemble system for microarray data classification.

PubMed

Liu, Kun-Hong; Tong, Muchenxuan; Xie, Shu-Tong; Yee Ng, Vincent To

2015-01-01

Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

Genetic Programming Based Ensemble System for Microarray Data Classification

PubMed Central

Liu, Kun-Hong; Tong, Muchenxuan; Xie, Shu-Tong; Yee Ng, Vincent To

2015-01-01

Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved. PMID:25810748

Gene Expression Omnibus (GEO): Microarray data storage, submission, retrieval, and analysis

PubMed Central

Barrett, Tanya

2006-01-01

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (NCBI) archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data generated by DNA microarray technology. The database has a flexible design that can handle diverse styles of both unprocessed and processed data in a MIAME- (Minimum Information About a Microarray Experiment) supportive infrastructure that promotes fully annotated submissions. GEO currently stores about a billion individual gene expression measurements, derived from over 100 organisms, submitted by over 1,500 laboratories, addressing a wide range of biological phenomena. To maximize the utility of these data, several user-friendly Web-based interfaces and applications have been implemented that enable effective exploration, query, and visualization of these data, at the level of individual genes or entire studies. This chapter describes how the data are stored, submission procedures, and mechanisms for data retrieval and query. GEO is publicly accessible at http://www.ncbi.nlm.nih.gov/projects/geo/. PMID:16939800

Assessment of a direct hybridization microarray strategy for comprehensive monitoring of genetically modified organisms (GMOs).

PubMed

Turkec, Aydin; Lucas, Stuart J; Karacanli, Burçin; Baykut, Aykut; Yuksel, Hakki

2016-03-01

Detection of GMO material in crop and food samples is the primary step in GMO monitoring and regulation, with the increasing number of GM events in the world market requiring detection solutions with high multiplexing capacity. In this study, we test the suitability of a high-density oligonucleotide microarray platform for direct, quantitative detection of GMOs found in the Turkish feed market. We tested 1830 different 60nt probes designed to cover the GM cassettes from 12 different GM cultivars (3 soya, 9 maize), as well as plant species-specific and contamination controls, and developed a data analysis method aiming to provide maximum throughput and sensitivity. The system was able specifically to identify each cultivar, and in 10/12 cases was sensitive enough to detect GMO DNA at concentrations of ⩽1%. These GMOs could also be quantified using the microarray, as their fluorescence signals increased linearly with GMO concentration. Copyright © 2015 Elsevier Ltd. All rights reserved.

A microarray-based genotyping and genetic mapping approach for highly heterozygous outcrossing species enables localization of a large fraction of the unassembled Populus trichocarpa genome sequence.

PubMed

Drost, Derek R; Novaes, Evandro; Boaventura-Novaes, Carolina; Benedict, Catherine I; Brown, Ryan S; Yin, Tongming; Tuskan, Gerald A; Kirst, Matias

2009-06-01

Microarrays have demonstrated significant power for genome-wide analyses of gene expression, and recently have also revolutionized the genetic analysis of segregating populations by genotyping thousands of loci in a single assay. Although microarray-based genotyping approaches have been successfully applied in yeast and several inbred plant species, their power has not been proven in an outcrossing species with extensive genetic diversity. Here we have developed methods for high-throughput microarray-based genotyping in such species using a pseudo-backcross progeny of 154 individuals of Populus trichocarpa and P. deltoides analyzed with long-oligonucleotide in situ-synthesized microarray probes. Our analysis resulted in high-confidence genotypes for 719 single-feature polymorphism (SFP) and 1014 gene expression marker (GEM) candidates. Using these genotypes and an established microsatellite (SSR) framework map, we produced a high-density genetic map comprising over 600 SFPs, GEMs and SSRs. The abundance of gene-based markers allowed us to localize over 35 million base pairs of previously unplaced whole-genome shotgun (WGS) scaffold sequence to putative locations in the genome of P. trichocarpa. A high proportion of sampled scaffolds could be verified for their placement with independently mapped SSRs, demonstrating the previously un-utilized power that high-density genotyping can provide in the context of map-based WGS sequence reassembly. Our results provide a substantial contribution to the continued improvement of the Populus genome assembly, while demonstrating the feasibility of microarray-based genotyping in a highly heterozygous population. The strategies presented are applicable to genetic mapping efforts in all plant species with similarly high levels of genetic diversity.

Cruella: developing a scalable tissue microarray data management system.

PubMed

Cowan, James D; Rimm, David L; Tuck, David P

2006-06-01

Compared with DNA microarray technology, relatively little information is available concerning the special requirements, design influences, and implementation strategies of data systems for tissue microarray technology. These issues include the requirement to accommodate new and different data elements for each new project as well as the need to interact with pre-existing models for clinical, biological, and specimen-related data. To design and implement a flexible, scalable tissue microarray data storage and management system that could accommodate information regarding different disease types and different clinical investigators, and different clinical investigation questions, all of which could potentially contribute unforeseen data types that require dynamic integration with existing data. The unpredictability of the data elements combined with the novelty of automated analysis algorithms and controlled vocabulary standards in this area require flexible designs and practical decisions. Our design includes a custom Java-based persistence layer to mediate and facilitate interaction with an object-relational database model and a novel database schema. User interaction is provided through a Java Servlet-based Web interface. Cruella has become an indispensable resource and is used by dozens of researchers every day. The system stores millions of experimental values covering more than 300 biological markers and more than 30 disease types. The experimental data are merged with clinical data that has been aggregated from multiple sources and is available to the researchers for management, analysis, and export. Cruella addresses many of the special considerations for managing tissue microarray experimental data and the associated clinical information. A metadata-driven approach provides a practical solution to many of the unique issues inherent in tissue microarray research, and allows relatively straightforward interoperability with and accommodation of new data models.

Evaluation of the skin irritation using a DNA microarray on a reconstructed human epidermal model.

PubMed

Niwa, Makoto; Nagai, Kanji; Oike, Hideaki; Kobori, Masuko

2009-02-01

To avoid the need to use animals to test the skin irritancy potential of chemicals and cosmetics, it is important to establish an in vitro method based on the reconstructed human epidermal model. To evaluate skin irritancy efficiently and sensitively, we determined the gene expression induced by a topically-applied mild irritant sodium dodecyl sulfate (SDS) in a reconstructed human epidermal model LabCyte EPI-MODEL (LabCyte) using a DNA microarray carrying genes that were related to inflammation, immunity, stress and housekeeping. The expression and secretion of IL-1alpha in reconstructed human epidermal culture is known to be induced by irritation. We detected the induction of IL-1alpha expression and its secretion into the cell culture medium by treatment with 0.075% SDS for 18 h in LabCyte culture using DNA microarray, quantitative reverse-transcription polymerase chain reaction (RT-PCR) and ELISA. DNA microarray analysis indicated that the expression of 10 of the 205 genes carried on the DNA microarray was significantly induced in a LabCyte culture by 0.05% or 0.075% SDS irritation for 18 h. RT-PCR analysis confirmed that SDS treatment significantly induced the expressions of interleukin-1 receptor antagonist (IL-1RN), FOS-like antigen 1 (FOSL1), heat shock 70 kDa protein 1A (HSPA1) and myeloid differentiation primary response gene (88) (MYD88), as well as the known marker genes for irritation IL-1beta and IL-8 in a LabCyte culture. Our results showed that a DNA microarray is a useful tool for efficiently evaluating mild skin irritation using a reconstructed human epidermal model.

Interim Report on SNP analysis and forensic microarray probe design for South American hemorrhagic fever viruses, tick-borne encephalitis virus, henipaviruses, Old World Arenaviruses, filoviruses, Crimean-Congo hemorrhagic fever viruses, Rift Valley fever

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jaing, C; Gardner, S

The goal of this project is to develop forensic genotyping assays for select agent viruses, enhancing the current capabilities for the viral bioforensics and law enforcement community. We used a multipronged approach combining bioinformatics analysis, PCR-enriched samples, microarrays and TaqMan assays to develop high resolution and cost effective genotyping methods for strain level forensic discrimination of viruses. We have leveraged substantial experience and efficiency gained through year 1 on software development, SNP discovery, TaqMan signature design and phylogenetic signature mapping to scale up the development of forensics signatures in year 2. In this report, we have summarized the whole genomemore » wide SNP analysis and microarray probe design for forensics characterization of South American hemorrhagic fever viruses, tick-borne encephalitis viruses and henipaviruses, Old World Arenaviruses, filoviruses, Crimean-Congo hemorrhagic fever virus, Rift Valley fever virus and Japanese encephalitis virus.« less

The Importance of Normalization on Large and Heterogeneous Microarray Datasets

EPA Science Inventory

DNA microarray technology is a powerful functional genomics tool increasingly used for investigating global gene expression in environmental studies. Microarrays can also be used in identifying biological networks, as they give insight on the complex gene-to-gene interactions, ne...

Detection of gastric cancer-associated microRNAs on microRNA microarray comparing pre- and post-operative plasma

PubMed Central

Konishi, H; Ichikawa, D; Komatsu, S; Shiozaki, A; Tsujiura, M; Takeshita, H; Morimura, R; Nagata, H; Arita, T; Kawaguchi, T; Hirashima, S; Fujiwara, H; Okamoto, K; Otsuji, E

2012-01-01

Background: Recently, it was reported that plasma microRNAs (miRNAs) are low-invasive useful biomarkers for cancer. We attempted to isolate gastric cancer (GC)-associated miRNAs comparing pre- and post-operative paired plasma, thereby excluding the possible effects of individual variability. Methods: This study was divided into four steps: (1) microarray analysis comparing pre- and post-operative plasma; (2) validation of candidate miRNAs by quantitative RT–PCR; (3) validation study of selected miRNAs using paired plasma; and (4) comparison of the levels of selected miRNAs in plasma between healthy controls and patients. Results: From the results of microarray analysis, nine candidate miRNAs the levels of which were markedly decreased in post-operative plasma were selected for further studies. After confirmation of their post-operative marked reduction, two candidate miRNAs, miR-451 and miR-486, were selected as plasma biomarkers, considering the abundance in plasma, and marked decrease in post-operative samples. In validation, the two miRNAs were found to decrease in post-operative plasma in 90 and 93% of patients (both P<0.01). In comparison with healthy controls, the levels of both miRNAs were found to be significantly higher in patients, and the area under the curve values were high at 0.96 and 0.92. Conclusion: Plasma miR-451 and miR-486 could be useful blood-based biomarkers for screening GC. PMID:22262318

Establishment of a protocol for the gene expression analysis of laser microdissected rat kidney samples with affymetrix genechips

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stemmer, Kerstin; Ellinger-Ziegelbauer, Heidrun; Lotz, Kerstin

2006-11-15

Laser microdissection in conjunction with microarray technology allows selective isolation and analysis of specific cell populations, e.g., preneoplastic renal lesions. To date, only limited information is available on sample preparation and preservation techniques that result in both optimal histomorphological preservation of sections and high-quality RNA for microarray analysis. Furthermore, amplification of minute amounts of RNA from microdissected renal samples allowing analysis with genechips has only scantily been addressed to date. The objective of this study was therefore to establish a reliable and reproducible protocol for laser microdissection in conjunction with microarray technology using kidney tissue from Eker rats p.o. treatedmore » for 7 days and 6 months with 10 and 1 mg Aristolochic acid/kg bw, respectively. Kidney tissues were preserved in RNAlater or snap frozen. Cryosections were cut and stained with either H and E or cresyl violet for subsequent morphological and RNA quality assessment and laser microdissection. RNA quality was comparable in snap frozen and RNAlater-preserved samples, however, the histomorphological preservation of renal sections was much better following cryopreservation. Moreover, the different staining techniques in combination with sample processing time at room temperature can have an influence on RNA quality. Different RNA amplification protocols were shown to have an impact on gene expression profiles as demonstrated with Affymetrix Rat Genome 230{sub 2}.0 arrays. Considering all the parameters analyzed in this study, a protocol for RNA isolation from laser microdissected samples with subsequent Affymetrix chip hybridization was established that was also successfully applied to preneoplastic lesions laser microdissected from Aristolochic acid-treated rats.« less

Microarray analysis of laser capture microdissected-anulus cells from the human intervertebral disc.

PubMed

Gruber, Helen E; Mougeot, Jean-Luc; Hoelscher, Gretchen; Ingram, Jane A; Hanley, Edward N

2007-05-15

Five Thompson Grade I/II discs (Group 1), 7 Grade III discs (Group 2), and 3 Grade IV discs (Group IV) were studied here in a project approved by the authors' Human Subjects Institutional Review Board. Our objective was to use laser capture microdissection (LCM) to harvest cells from the human anulus and to derive gene expression profiles using microarray analysis. Appropriate gene expression is essential in the intervertebral disc for maintenance of extracellular matrix (ECM), ECM remodeling, and maintenance of a viable disc cell population. During disc degeneration, cell numbers drop, making gene expression studies challenging. LCM was used to harvest cells from paraffin-embedded sections of human anulus tissue. Gene profiling used Affymetrix GeneChip Human X3P arrays. ANOVA and SAM permutation analysis were applied to dCHIP normalized, filtered, and log-transformed gene expression data ( approximately 33,500 probes), and data analyzed to identify genes that were significantly differentially expressed between the 3 groups. We identified 47 genes that were significantly differentially expressed between the 3 groups (P < 0.001 and lowest q values). Compared with the healthiest discs (Grade I/II), 13 genes were up-regulated and 19 down-regulated in both the Grade III and the Grade IV discs. Genes with biologic significance regulated during degeneration involved cell senescence, low cell division rates, hypoxia-related genes, heat-shock protein 70 interacting protein, neuropilin 2, and interleukin-23p19 (interleukin-12 family). Results expand our understanding of disc aging and degeneration and show that LCM is a valuable technique that can be used to collect mRNA amounts adequate for microarray analysis from the sparse cell population of the human anulus.

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

PubMed

Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

2009-12-21

Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

Genomic analysis of soybean defense response to Sclerotinia sclerotiorum

USDA-ARS?s Scientific Manuscript database

We have conducted microarray studies on changes in soybean transcript levels in response to Sclerotinia sclerotiorum infection. These stem inoculations enabled us to identify genes that are differentially expressed in soybean plants in partially resistant versus susceptible varieties. We are expandi...

Tissue microarray immunohistochemical detection of brachyury is not a prognostic indicator in chordoma.

PubMed

Zhang, Linlin; Guo, Shang; Schwab, Joseph H; Nielsen, G Petur; Choy, Edwin; Ye, Shunan; Zhang, Zhan; Mankin, Henry; Hornicek, Francis J; Duan, Zhenfeng

2013-01-01

Brachyury is a marker for notochord-derived tissues and neoplasms, such as chordoma. However, the prognostic relevance of brachyury expression in chordoma is still unknown. The improvement of tissue microarray technology has provided the opportunity to perform analyses of tumor tissues on a large scale in a uniform and consistent manner. This study was designed with the use of tissue microarray to determine the expression of brachyury. Brachyury expression in chordoma tissues from 78 chordoma patients was analyzed by immunohistochemical staining of tissue microarray. The clinicopathologic parameters, including gender, age, location of tumor and metastatic status were evaluated. Fifty-nine of 78 (75.64%) tumors showed nuclear staining for brachyury, and among them, 29 tumors (49.15%) showed 1+ (<30% positive cells) staining, 15 tumors (25.42%) had 2+ (31% to 60% positive cells) staining, and 15 tumors (25.42%) demonstrated 3+ (61% to 100% positive cells) staining. Brachyury nuclear staining was detected more frequently in sacral chordomas than in chordomas of the mobile spine. However, there was no significant relationship between brachyury expression and other clinical variables. By Kaplan-Meier analysis, brachyury expression failed to produce any significant relationship with the overall survival rate. In conclusion, brachyury expression is not a prognostic indicator in chordoma.

Tumor immunology.

PubMed

Mocellin, Simone; Lise, Mario; Nitti, Donato

2007-01-01

Advances in tumor immunology are supporting the clinical implementation of several immunological approaches to cancer in the clinical setting. However, the alternate success of current immunotherapeutic regimens underscores the fact that the molecular mechanisms underlying immune-mediated tumor rejection are still poorly understood. Given the complexity of the immune system network and the multidimensionality of tumor/host interactions, the comprehension of tumor immunology might greatly benefit from high-throughput microarray analysis, which can portrait the molecular kinetics of immune response on a genome-wide scale, thus accelerating the discovery pace and ultimately catalyzing the development of new hypotheses in cell biology. Although in its infancy, the implementation of microarray technology in tumor immunology studies has already provided investigators with novel data and intriguing new hypotheses on the molecular cascade leading to an effective immune response against cancer. Although the general principles of microarray-based gene profiling have rapidly spread in the scientific community, the need for mastering this technique to produce meaningful data and correctly interpret the enormous output of information generated by this technology is critical and represents a tremendous challenge for investigators, as outlined in the first section of this book. In the present Chapter, we report on some of the most significant results obtained with the application of DNA microarray in this oncology field.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

An Undergraduate Laboratory Exercise to Study the Effect of Darkness on Plant Gene Expression Using DNA Microarray

ERIC Educational Resources Information Center

Chang, Ming-Mei; Briggs, George M.

2007-01-01

DNA microarrays are microscopic arrays on a solid surface, typically a glass slide, on which DNA oligonucleotides are deposited or synthesized in a high-density matrix with a predetermined spatial order. Several types of DNA microarrays have been developed and used for various biological studies. Here, we developed an undergraduate laboratory…

Multi-membership gene regulation in pathway based microarray analysis

PubMed Central

2011-01-01

Background Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes. PMID:21939531

Multi-membership gene regulation in pathway based microarray analysis.

PubMed

Pavlidis, Stelios P; Payne, Annette M; Swift, Stephen M

2011-09-22

Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

Quantitative comparison of microarray experiments with published leukemia related gene expression signatures.

PubMed

Klein, Hans-Ulrich; Ruckert, Christian; Kohlmann, Alexander; Bullinger, Lars; Thiede, Christian; Haferlach, Torsten; Dugas, Martin

2009-12-15

Multiple gene expression signatures derived from microarray experiments have been published in the field of leukemia research. A comparison of these signatures with results from new experiments is useful for verification as well as for interpretation of the results obtained. Currently, the percentage of overlapping genes is frequently used to compare published gene signatures against a signature derived from a new experiment. However, it has been shown that the percentage of overlapping genes is of limited use for comparing two experiments due to the variability of gene signatures caused by different array platforms or assay-specific influencing parameters. Here, we present a robust approach for a systematic and quantitative comparison of published gene expression signatures with an exemplary query dataset. A database storing 138 leukemia-related published gene signatures was designed. Each gene signature was manually annotated with terms according to a leukemia-specific taxonomy. Two analysis steps are implemented to compare a new microarray dataset with the results from previous experiments stored and curated in the database. First, the global test method is applied to assess gene signatures and to constitute a ranking among them. In a subsequent analysis step, the focus is shifted from single gene signatures to chromosomal aberrations or molecular mutations as modeled in the taxonomy. Potentially interesting disease characteristics are detected based on the ranking of gene signatures associated with these aberrations stored in the database. Two example analyses are presented. An implementation of the approach is freely available as web-based application. The presented approach helps researchers to systematically integrate the knowledge derived from numerous microarray experiments into the analysis of a new dataset. By means of example leukemia datasets we demonstrate that this approach detects related experiments as well as related molecular mutations and may help to interpret new microarray data.

ValWorkBench: an open source Java library for cluster validation, with applications to microarray data analysis.

PubMed

Giancarlo, R; Scaturro, D; Utro, F

2015-02-01

The prediction of the number of clusters in a dataset, in particular microarrays, is a fundamental task in biological data analysis, usually performed via validation measures. Unfortunately, it has received very little attention and in fact there is a growing need for software tools/libraries dedicated to it. Here we present ValWorkBench, a software library consisting of eleven well known validation measures, together with novel heuristic approximations for some of them. The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re-used. Consequently, the focus of the presentation is on the architecture of the library, since it provides an essential map that can be used to access the full software documentation, which is available at the supplementary material website [1]. The mentioned main features of ValWorkBench are also discussed and exemplified, with emphasis on software abstraction design and re-usability. A comparison with existing cluster validation software libraries, mainly in terms of the mentioned features, is also offered. It suggests that ValWorkBench is a much needed contribution to the microarray software development/algorithm engineering community. For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Glycome Diagnosis of Human Induced Pluripotent Stem Cells Using Lectin Microarray*

PubMed Central

Tateno, Hiroaki; Toyota, Masashi; Saito, Shigeru; Onuma, Yasuko; Ito, Yuzuru; Hiemori, Keiko; Fukumura, Mihoko; Matsushima, Asako; Nakanishi, Mio; Ohnuma, Kiyoshi; Akutsu, Hidenori; Umezawa, Akihiro; Horimoto, Katsuhisa; Hirabayashi, Jun; Asashima, Makoto

2011-01-01

Induced pluripotent stem cells (iPSCs) can now be produced from various somatic cell (SC) lines by ectopic expression of the four transcription factors. Although the procedure has been demonstrated to induce global change in gene and microRNA expressions and even epigenetic modification, it remains largely unknown how this transcription factor-induced reprogramming affects the total glycan repertoire expressed on the cells. Here we performed a comprehensive glycan analysis using 114 types of human iPSCs generated from five different SCs and compared their glycomes with those of human embryonic stem cells (ESCs; nine cell types) using a high density lectin microarray. In unsupervised cluster analysis of the results obtained by lectin microarray, both undifferentiated iPSCs and ESCs were clustered as one large group. However, they were clearly separated from the group of differentiated SCs, whereas all of the four SCs had apparently distinct glycome profiles from one another, demonstrating that SCs with originally distinct glycan profiles have acquired those similar to ESCs upon induction of pluripotency. Thirty-eight lectins discriminating between SCs and iPSCs/ESCs were statistically selected, and characteristic features of the pluripotent state were then obtained at the level of the cellular glycome. The expression profiles of relevant glycosyltransferase genes agreed well with the results obtained by lectin microarray. Among the 38 lectins, rBC2LCN was found to detect only undifferentiated iPSCs/ESCs and not differentiated SCs. Hence, the high density lectin microarray has proved to be valid for not only comprehensive analysis of glycans but also diagnosis of stem cells under the concept of the cellular glycome. PMID:21471226

Study of pharmacological effects of nilvadipine on RCS rat retinal degeneration by microarray analysis.

PubMed

Sato, Motoya; Ohguro, Hiroshi; Ohguro, Ikuyo; Mamiya, Kazuhisa; Takano, Yoshiko; Yamazaki, Hitoshi; Metoki, Tomomi; Miyagawa, Yasuhiro; Ishikawa, Fotoshi; Nakazawa, Mitsuru

2003-07-11

In our recent study, we found that the Ca(2+) antagonist, nilvadipine caused significant preservation of photoreceptor cells in The Royal College of Surgeons (RCS) rats [Invest. Ophthalmol. Vis. Sci. 43 (2002) 919]. Here, to elucidate the mechanisms of nilvadipine-induced effects we analyzed altered gene expression of 1101 genes commonly expressed in rodent by DNA microarray analysis in the retinas of nilvadipine-treated and untreated RCS rats and SD rat. In the total number of genes, the expression of 30 genes was altered upon administration of nilvadipine to RCS rats, including several genes related to the apoptotic pathway and other mechanisms. Remarkably, neurotrophic factors, FGF-2 and Arc, known to suppress the apoptosis in the central nervous system, were up-regulated. These changes were also confirmed by real-time quantitative (Taqman) RT-PCR and Western blot analysis. Therefore, our present data suggested that administration of nilvadipine to RCS rats increases the expression of endogenous FGF-2 and Arc in retina, and potentially has a protective effect against retinal degeneration.

Low-level laser therapy induces an upregulation of collagen gene expression during the initial process of bone healing: a microarray analysis

NASA Astrophysics Data System (ADS)

Tim, Carla Roberta; Bossini, Paulo Sérgio; Kido, Hueliton Wilian; Malavazi, Iran; von Zeska Kress, Marcia Regina; Carazzolle, Marcelo Falsarella; Rennó, Ana Cláudia; Parizotto, Nivaldo Antonio

2016-08-01

This study investigates the histological modifications produced by low level laser therapy (LLLT) on the first day of bone repair, as well as evaluates the LLLT effects on collagen expression on the site of a fracture. Twenty Wistar rats were distributed into a control group (CG) and a laser group (LG). Laser irradiation of Ga-Al-As laser 830 nm, 30 mW, 94 s, 2.8 J was performed in five sessions. Animals were euthanized on day 5 postsurgery. Histopathological analysis showed that LLLT was able to increase deposition of granulation tissue and newly formed bone at the site of the injury. In addition, picrosirius analysis showed that collagen fiber organization in the LG was enhanced compared to CG. Microarray analysis demonstrated that LLLT produced an upregulation type I collagen (COL-I). Immunohistochemical analysis revealed that the subjects that were treated presented a higher immunoexpression of COL-I. Our findings indicated that LLLT improves bone healing by producing a significant increase in the expression of collagen genes.

Modulation of Differentiation Processes in Murine Embryonic Stem Cells Exposed to Parabolic Flight-Induced Acute Hypergravity and Microgravity.

PubMed

Acharya, Aviseka; Brungs, Sonja; Henry, Margit; Rotshteyn, Tamara; Singh Yaduvanshi, Nirmala; Wegener, Lucia; Jentzsch, Simon; Hescheler, Jürgen; Hemmersbach, Ruth; Boeuf, Helene; Sachinidis, Agapios

2018-06-15

Embryonic developmental studies under microgravity conditions in space are very limited. To study the effects of short-term altered gravity on embryonic development processes, we exposed mouse embryonic stem cells (mESCs) to phases of hypergravity and microgravity and studied the differentiation potential of the cells using wide-genome microarray analysis. During the 64th European Space Agency's parabolic flight campaign, mESCs were exposed to 31 parabolas. Each parabola comprised phases lasting 22 s of hypergravity, microgravity, and a repeat of hypergravity. On different parabolas, RNA was isolated for microarray analysis. After exposure to 31 parabolas, mESCs (P31 mESCs) were further differentiated under normal gravity (1 g) conditions for 12 days, producing P31 12-day embryoid bodies (EBs). After analysis of the microarrays, the differentially expressed genes were analyzed using different bioinformatic tools to identify developmental and nondevelopmental biological processes affected by conditions on the parabolic flight experiment. Our results demonstrated that several genes belonging to GOs associated with cell cycle and proliferation were downregulated in undifferentiated mESCs exposed to gravity changes. However, several genes belonging to developmental processes, such as vasculature development, kidney development, skin development, and to the TGF-β signaling pathway, were upregulated. Interestingly, similar enriched and suppressed GOs were obtained in P31 12-day EBs compared with ground control 12-day EBs. Our results show that undifferentiated mESCs exposed to alternate hypergravity and microgravity phases expressed several genes associated with developmental/differentiation and cell cycle processes, suggesting a transition from the undifferentiated pluripotent to a more differentiated stage of mESCs.

Clofibrate-induced gene expression changes in rat liver: a cross-laboratory analysis using membrane cDNA arrays.

PubMed Central

Baker, Valerie A; Harries, Helen M; Waring, Jeff F; Duggan, Colette M; Ni, Hong A; Jolly, Robert A; Yoon, Lawrence W; De Souza, Angus T; Schmid, Judith E; Brown, Roger H; Ulrich, Roger G; Rockett, John C

2004-01-01

Microarrays have the potential to significantly impact our ability to identify toxic hazards by the identification of mechanistically relevant markers of toxicity. To be useful for risk assessment, however, microarray data must be challenged to determine reliability and interlaboratory reproducibility. As part of a series of studies conducted by the International Life Sciences Institute Health and Environmental Science Institute Technical Committee on the Application of Genomics to Mechanism-Based Risk Assessment, the biological response in rats to the hepatotoxin clofibrate was investigated. Animals were treated with high (250 mg/kg/day) or low (25 mg/kg/day) doses for 1, 3, or 7 days in two laboratories. Clinical chemistry parameters were measured, livers removed for histopathological assessment, and gene expression analysis was conducted using cDNA arrays. Expression changes in genes involved in fatty acid metabolism (e.g., acyl-CoA oxidase), cell proliferation (e.g., topoisomerase II-Alpha), and fatty acid oxidation (e.g., cytochrome P450 4A1), consistent with the mechanism of clofibrate hepatotoxicity, were detected. Observed differences in gene expression levels correlated with the level of biological response induced in the two in vivo studies. Generally, there was a high level of concordance between the gene expression profiles generated from pooled and individual RNA samples. Quantitative real-time polymerase chain reaction was used to confirm modulations for a number of peroxisome proliferator marker genes. Though the results indicate some variability in the quantitative nature of the microarray data, this appears due largely to differences in experimental and data analysis procedures used within each laboratory. In summary, this study demonstrates the potential for gene expression profiling to identify toxic hazards by the identification of mechanistically relevant markers of toxicity. PMID:15033592

Differences in gene expression profiles and signaling pathways in rhabdomyolysis-induced acute kidney injury.

PubMed

Geng, Xiaodong; Wang, Yuanda; Hong, Quan; Yang, Jurong; Zheng, Wei; Zhang, Gang; Cai, Guangyan; Chen, Xiangmei; Wu, Di

2015-01-01

Rhabdomyolysis is a threatening syndrome because it causes the breakdown of skeletal muscle. Muscle destruction leads to the release of myoglobin, intracellular proteins, and electrolytes into the circulation. The aim of this study was to investigate the differences in gene expression profiles and signaling pathways upon rhabdomyolysis-induced acute kidney injury (AKI). In this study, we used glycerol-induced renal injury as a model of rhabdomyolysis-induced AKI. We analyzed data and relevant information from the Gene Expression Omnibus database (No: GSE44925). The gene expression data for three untreated mice were compared to data for five mice with rhabdomyolysis-induced AKI. The expression profiling of the three untreated mice and the five rhabdomyolysis-induced AKI mice was performed using microarray analysis. We examined the levels of Cyp3a13, Rela, Aldh7a1, Jun, CD14. And Cdkn1a using RT-PCR to determine the accuracy of the microarray results. The microarray analysis showed that there were 1050 downregulated and 659 upregulated genes in the rhabdomyolysis-induced AKI mice compared to the control group. The interactions of all differentially expressed genes in the Signal-Net were analyzed. Cyp3a13 and Rela had the most interactions with other genes. The data showed that Rela and Aldh7a1 were the key nodes and had important positions in the Signal-Net. The genes Jun, CD14, and Cdkn1a were also significantly upregulated. The pathway analysis classified the differentially expressed genes into 71 downregulated and 48 upregulated pathways including the PI3K/Akt, MAPK, and NF-κB signaling pathways. The results of this study indicate that the NF-κB, MAPK, PI3K/Akt, and apoptotic pathways are regulated in rhabdomyolysis-induced AKI.

Genome-wide profiling of gene expression in the epididymis of alpha-chlorohydrin-induced infertile rats using an oligonucleotide microarray

PubMed Central

2010-01-01

Background As one of the chlorinated antifertility compounds, alpha-chlorohydrin (ACH) can inhibit glyceraldehyde-3-phosphate dehydrogenase (G3PDH) activity in epididymal sperm and affect sperm energy metabolism, maturation and fertilization, eventually leading to male infertility. Further studies demonstrated that the inhibitory effect of ACH on G3PDH is not only confined to epididymal sperm but also to the epididymis. Moreover, little investigation on gene expression changes in the epididymis after ACH treatment has been conducted. Therefore, gene expression studies may indicate new epididymal targets related to sperm maturation and fertility through the analysis of ACH-treated infertile animals. Methods Rats were treated with ACH for ten consecutive days, and then each male rat copulated with two female rats in proestrus. Then sperm maturation and other fertility parameters were analyzed. Furthermore, we identified epididymal-specific genes that are associated with fertility between control and ACH groups using an Affymetrix Rat 230 2.0 oligo-microarray. Finally, we performed RT-PCR analysis for several differentially expressed genes to validate the alteration in gene expression observed by oligonucleotide microarray. Results Among all the differentially expressed genes, we analyzed and screened the down-regulated genes associated with metabolism processes, which are considered the major targets of ACH action. Simultaneously, the genes that were up-regulated by chlorohydrin were detected. The genes that negatively regulate sperm maturation and fertility include apoptosis and immune-related genes and have not been reported previously. The overall results of PCR analysis for selected genes were consistent with the array data. Conclusions In this study, we have described the genome-wide profiles of gene expression in the epididymides of infertile rats induced by ACH, which could become potential epididymal specific targets for male contraception and infertility treatment. PMID:20409345

Genome-wide profiling of gene expression in the epididymis of alpha-chlorohydrin-induced infertile rats using an oligonucleotide microarray.

PubMed

Xie, Shuwu; Zhu, Yan; Ma, Li; Lu, Yingying; Zhou, Jieyun; Gui, Youlun; Cao, Lin

2010-04-22

As one of the chlorinated antifertility compounds, alpha-chlorohydrin (ACH) can inhibit glyceraldehyde-3-phosphate dehydrogenase (G3PDH) activity in epididymal sperm and affect sperm energy metabolism, maturation and fertilization, eventually leading to male infertility. Further studies demonstrated that the inhibitory effect of ACH on G3PDH is not only confined to epididymal sperm but also to the epididymis. Moreover, little investigation on gene expression changes in the epididymis after ACH treatment has been conducted. Therefore, gene expression studies may indicate new epididymal targets related to sperm maturation and fertility through the analysis of ACH-treated infertile animals. Rats were treated with ACH for ten consecutive days, and then each male rat copulated with two female rats in proestrus. Then sperm maturation and other fertility parameters were analyzed. Furthermore, we identified epididymal-specific genes that are associated with fertility between control and ACH groups using an Affymetrix Rat 230 2.0 oligo-microarray. Finally, we performed RT-PCR analysis for several differentially expressed genes to validate the alteration in gene expression observed by oligonucleotide microarray. Among all the differentially expressed genes, we analyzed and screened the down-regulated genes associated with metabolism processes, which are considered the major targets of ACH action. Simultaneously, the genes that were up-regulated by chlorohydrin were detected. The genes that negatively regulate sperm maturation and fertility include apoptosis and immune-related genes and have not been reported previously. The overall results of PCR analysis for selected genes were consistent with the array data. In this study, we have described the genome-wide profiles of gene expression in the epididymides of infertile rats induced by ACH, which could become potential epididymal specific targets for male contraception and infertility treatment.

Development and Comparison of Two Assay Formats for Parallel Detection of Four Biothreat Pathogens by Using Suspension Microarrays

PubMed Central

Janse, Ingmar; Bok, Jasper M.; Hamidjaja, Raditijo A.; Hodemaekers, Hennie M.; van Rotterdam, Bart J.

2012-01-01

Microarrays provide a powerful analytical tool for the simultaneous detection of multiple pathogens. We developed diagnostic suspension microarrays for sensitive and specific detection of the biothreat pathogens Bacillus anthracis, Yersinia pestis, Francisella tularensis and Coxiella burnetii. Two assay chemistries for amplification and labeling were developed, one method using direct hybridization and the other using target-specific primer extension, combined with hybridization to universal arrays. Asymmetric PCR products for both assay chemistries were produced by using a multiplex asymmetric PCR amplifying 16 DNA signatures (16-plex). The performances of both assay chemistries were compared and their advantages and disadvantages are discussed. The developed microarrays detected multiple signature sequences and an internal control which made it possible to confidently identify the targeted pathogens and assess their virulence potential. The microarrays were highly specific and detected various strains of the targeted pathogens. Detection limits for the different pathogen signatures were similar or slightly higher compared to real-time PCR. Probit analysis showed that even a few genomic copies could be detected with 95% confidence. The microarrays detected DNA from different pathogens mixed in different ratios and from spiked or naturally contaminated samples. The assays that were developed have a potential for application in surveillance and diagnostics. PMID:22355407

Development and comparison of two assay formats for parallel detection of four biothreat pathogens by using suspension microarrays.

PubMed

Janse, Ingmar; Bok, Jasper M; Hamidjaja, Raditijo A; Hodemaekers, Hennie M; van Rotterdam, Bart J

2012-01-01

Microarrays provide a powerful analytical tool for the simultaneous detection of multiple pathogens. We developed diagnostic suspension microarrays for sensitive and specific detection of the biothreat pathogens Bacillus anthracis, Yersinia pestis, Francisella tularensis and Coxiella burnetii. Two assay chemistries for amplification and labeling were developed, one method using direct hybridization and the other using target-specific primer extension, combined with hybridization to universal arrays. Asymmetric PCR products for both assay chemistries were produced by using a multiplex asymmetric PCR amplifying 16 DNA signatures (16-plex). The performances of both assay chemistries were compared and their advantages and disadvantages are discussed. The developed microarrays detected multiple signature sequences and an internal control which made it possible to confidently identify the targeted pathogens and assess their virulence potential. The microarrays were highly specific and detected various strains of the targeted pathogens. Detection limits for the different pathogen signatures were similar or slightly higher compared to real-time PCR. Probit analysis showed that even a few genomic copies could be detected with 95% confidence. The microarrays detected DNA from different pathogens mixed in different ratios and from spiked or naturally contaminated samples. The assays that were developed have a potential for application in surveillance and diagnostics.

Assessment of data processing to improve reliability of microarray experiments using genomic DNA reference.

PubMed

Yang, Yunfeng; Zhu, Mengxia; Wu, Liyou; Zhou, Jizhong

2008-09-16

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories. Conflicting results have been reported with regard to the reliability of microarray results using this method. To explain it, we hypothesize that data processing is a critical element that impacts the data quality. Microarray experiments were performed in a gamma-proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either with two labeled cDNA samples co-hybridized to the same array, or by employing Shewanella genomic DNA as a standard reference. Various data processing techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses. These findings demonstrate that data processing significantly influences data quality, which provides an explanation for the conflicting evaluation in the literature. This work could serve as a guideline for microarray data analysis using genomic DNA as a standard reference.

Improved microarray methods for profiling the yeast knockout strain collection

PubMed Central

Yuan, Daniel S.; Pan, Xuewen; Ooi, Siew Loon; Peyser, Brian D.; Spencer, Forrest A.; Irizarry, Rafael A.; Boeke, Jef D.

2005-01-01

A remarkable feature of the Yeast Knockout strain collection is the presence of two unique 20mer TAG sequences in almost every strain. In principle, the relative abundances of strains in a complex mixture can be profiled swiftly and quantitatively by amplifying these sequences and hybridizing them to microarrays, but TAG microarrays have not been widely used. Here, we introduce a TAG microarray design with sophisticated controls and describe a robust method for hybridizing high concentrations of dye-labeled TAGs in single-stranded form. We also highlight the importance of avoiding PCR contamination and provide procedures for detection and eradication. Validation experiments using these methods yielded false positive (FP) and false negative (FN) rates for individual TAG detection of 3–6% and 15–18%, respectively. Analysis demonstrated that cross-hybridization was the chief source of FPs, while TAG amplification defects were the main cause of FNs. The materials, protocols, data and associated software described here comprise a suite of experimental resources that should facilitate the use of TAG microarrays for a wide variety of genetic screens. PMID:15994458

An efficient method to identify differentially expressed genes in microarray experiments

PubMed Central

Qin, Huaizhen; Feng, Tao; Harding, Scott A.; Tsai, Chung-Jui; Zhang, Shuanglin

2013-01-01

Motivation Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. Results We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes. Availability The C++ code to implement the proposed method is available upon request for academic use. PMID:18453554

Clustering approaches to identifying gene expression patterns from DNA microarray data.

PubMed

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Specific Discrimination of Three Pathogenic Salmonella enterica subsp. enterica Serotypes by carB-Based Oligonucleotide Microarray

PubMed Central

Shin, Hwa Hui; Hwang, Byeong Hee; Seo, Jeong Hyun

2014-01-01

It is important to rapidly and selectively detect and analyze pathogenic Salmonella enterica subsp. enterica in contaminated food to reduce the morbidity and mortality of Salmonella infection and to guarantee food safety. In the present work, we developed an oligonucleotide microarray containing duplicate specific capture probes based on the carB gene, which encodes the carbamoyl phosphate synthetase large subunit, as a competent biomarker evaluated by genetic analysis to selectively and efficiently detect and discriminate three S. enterica subsp. enterica serotypes: Choleraesuis, Enteritidis, and Typhimurium. Using the developed microarray system, three serotype targets were successfully analyzed in a range as low as 1.6 to 3.1 nM and were specifically discriminated from each other without nonspecific signals. In addition, the constructed microarray did not have cross-reactivity with other common pathogenic bacteria and even enabled the clear discrimination of the target Salmonella serotype from a bacterial mixture. Therefore, these results demonstrated that our novel carB-based oligonucleotide microarray can be used as an effective and specific detection system for S. enterica subsp. enterica serotypes. PMID:24185846

Specific discrimination of three pathogenic Salmonella enterica subsp. enterica serotypes by carB-based oligonucleotide microarray.

PubMed

Shin, Hwa Hui; Hwang, Byeong Hee; Seo, Jeong Hyun; Cha, Hyung Joon

2014-01-01

It is important to rapidly and selectively detect and analyze pathogenic Salmonella enterica subsp. enterica in contaminated food to reduce the morbidity and mortality of Salmonella infection and to guarantee food safety. In the present work, we developed an oligonucleotide microarray containing duplicate specific capture probes based on the carB gene, which encodes the carbamoyl phosphate synthetase large subunit, as a competent biomarker evaluated by genetic analysis to selectively and efficiently detect and discriminate three S. enterica subsp. enterica serotypes: Choleraesuis, Enteritidis, and Typhimurium. Using the developed microarray system, three serotype targets were successfully analyzed in a range as low as 1.6 to 3.1 nM and were specifically discriminated from each other without nonspecific signals. In addition, the constructed microarray did not have cross-reactivity with other common pathogenic bacteria and even enabled the clear discrimination of the target Salmonella serotype from a bacterial mixture. Therefore, these results demonstrated that our novel carB-based oligonucleotide microarray can be used as an effective and specific detection system for S. enterica subsp. enterica serotypes.

Genome-wide transcription analysis of histidine-related cataract in Atlantic salmon (Salmo salar L)

PubMed Central

Waagbø, Rune; Breck, Olav; Stavrum, Anne-Kristin; Petersen, Kjell; Olsvik, Pål A.

2009-01-01

Purpose Elevated levels of dietary histidine have previously been shown to prevent or mitigate cataract formation in farmed Atlantic salmon (Salmo salar L). The aim of this study was to shed light on the mechanisms by which histidine acts. Applying microarray analysis to the lens transcriptome, we screened for differentially expressed genes in search for a model explaining cataract development in Atlantic salmon and possible markers for early cataract diagnosis. Methods Adult Atlantic salmon (1.7 kg) were fed three standard commercial salmon diets only differing in the histidine content (9, 13, and 17 g histidine/kg diet) for four months. Individual cataract scores for both eyes were assessed by slit-lamp biomicroscopy. Lens N-acetyl histidine contents were measured by high performance liquid chromatography (HPLC). Total RNA extracted from whole lenses was analyzed using the GRASP 16K salmonid microarray. The microarray data were analyzed using J-Express Pro 2.7 and validated by quantitative real-time polymerase chain reaction (qRT–PCR). Results Fish developed cataracts with different severity in response to dietary histidine levels. Lens N-acetyl histidine contents reflected the dietary histidine levels and were negatively correlated to cataract scores. Significance analysis of microarrays (SAM) revealed 248 significantly up-regulated transcripts and 266 significantly down-regulated transcripts in fish that were fed a low level of histidine compared to fish fed a higher histidine level. Among the differentially expressed transcripts were metallothionein A and B as well as transcripts involved in lipid metabolism, carbohydrate metabolism, regulation of ion homeostasis, and protein degradation. Hierarchical clustering and correspondence analysis plot confirmed differences in gene expression between the feeding groups. The differentially expressed genes could be categorized as “early” and “late” responsive according to their expression pattern relative to progression in cataract formation. Conclusions Dietary histidine regimes affected cataract formation and lens gene expression in adult Atlantic salmon. Regulated transcripts selected from the results of this genome-wide transcription analysis might be used as possible biological markers for cataract development in Atlantic salmon. PMID:19597568

Tissue microarrays and quantitative tissue-based image analysis as a tool for oncology biomarker and diagnostic development.

PubMed

Dolled-Filhart, Marisa P; Gustavson, Mark D

2012-11-01

Translational oncology has been improved by using tissue microarrays (TMAs), which facilitate biomarker analysis of large cohorts on a single slide. This has allowed for rapid analysis and validation of potential biomarkers for prognostic and predictive value, as well as for evaluation of biomarker prevalence. Coupled with quantitative analysis of immunohistochemical (IHC) staining, objective and standardized biomarker data from tumor samples can further advance companion diagnostic approaches for the identification of drug-responsive or resistant patient subpopulations. This review covers the advantages, disadvantages and applications of TMAs for biomarker research. Research literature and reviews of TMAs and quantitative image analysis methodology have been surveyed for this review (with an AQUA® analysis focus). Applications such as multi-marker diagnostic development and pathway-based biomarker subpopulation analyses are described. Tissue microarrays are a useful tool for biomarker analyses including prevalence surveys, disease progression assessment and addressing potential prognostic or predictive value. By combining quantitative image analysis with TMAs, analyses will be more objective and reproducible, allowing for more robust IHC-based diagnostic test development. Quantitative multi-biomarker IHC diagnostic tests that can predict drug response will allow for greater success of clinical trials for targeted therapies and provide more personalized clinical decision making.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

EPA Science Inventory

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, ...

Flow-pattern Guided Fabrication of High-density Barcode Antibody Microarray

PubMed Central

Ramirez, Lisa S.; Wang, Jun

2016-01-01

Antibody microarray as a well-developed technology is currently challenged by a few other established or emerging high-throughput technologies. In this report, we renovate the antibody microarray technology by using a novel approach for manufacturing and by introducing new features. The fabrication of our high-density antibody microarray is accomplished through perpendicularly oriented flow-patterning of single stranded DNAs and subsequent conversion mediated by DNA-antibody conjugates. This protocol outlines the critical steps in flow-patterning DNA, producing and purifying DNA-antibody conjugates, and assessing the quality of the fabricated microarray. The uniformity and sensitivity are comparable with conventional microarrays, while our microarray fabrication does not require the assistance of an array printer and can be performed in most research laboratories. The other major advantage is that the size of our microarray units is 10 times smaller than that of printed arrays, offering the unique capability of analyzing functional proteins from single cells when interfacing with generic microchip designs. This barcode technology can be widely employed in biomarker detection, cell signaling studies, tissue engineering, and a variety of clinical applications. PMID:26780370

An efficient pseudomedian filter for tiling microrrays.

PubMed

Royce, Thomas E; Carriero, Nicholas J; Gerstein, Mark B

2007-06-07

Tiling microarrays are becoming an essential technology in the functional genomics toolbox. They have been applied to the tasks of novel transcript identification, elucidation of transcription factor binding sites, detection of methylated DNA and several other applications in several model organisms. These experiments are being conducted at increasingly finer resolutions as the microarray technology enjoys increasingly greater feature densities. The increased densities naturally lead to increased data analysis requirements. Specifically, the most widely employed algorithm for tiling array analysis involves smoothing observed signals by computing pseudomedians within sliding windows, a O(n2logn) calculation in each window. This poor time complexity is an issue for tiling array analysis and could prove to be a real bottleneck as tiling microarray experiments become grander in scope and finer in resolution. We therefore implemented Monahan's HLQEST algorithm that reduces the runtime complexity for computing the pseudomedian of n numbers to O(nlogn) from O(n2logn). For a representative tiling microarray dataset, this modification reduced the smoothing procedure's runtime by nearly 90%. We then leveraged the fact that elements within sliding windows remain largely unchanged in overlapping windows (as one slides across genomic space) to further reduce computation by an additional 43%. This was achieved by the application of skip lists to maintaining a sorted list of values from window to window. This sorted list could be maintained with simple O(log n) inserts and deletes. We illustrate the favorable scaling properties of our algorithms with both time complexity analysis and benchmarking on synthetic datasets. Tiling microarray analyses that rely upon a sliding window pseudomedian calculation can require many hours of computation. We have eased this requirement significantly by implementing efficient algorithms that scale well with genomic feature density. This result not only speeds the current standard analyses, but also makes possible ones where many iterations of the filter may be required, such as might be required in a bootstrap or parameter estimation setting. Source code and executables are available at http://tiling.gersteinlab.org/pseudomedian/.

An efficient pseudomedian filter for tiling microrrays

PubMed Central

Royce, Thomas E; Carriero, Nicholas J; Gerstein, Mark B

2007-01-01

Background Tiling microarrays are becoming an essential technology in the functional genomics toolbox. They have been applied to the tasks of novel transcript identification, elucidation of transcription factor binding sites, detection of methylated DNA and several other applications in several model organisms. These experiments are being conducted at increasingly finer resolutions as the microarray technology enjoys increasingly greater feature densities. The increased densities naturally lead to increased data analysis requirements. Specifically, the most widely employed algorithm for tiling array analysis involves smoothing observed signals by computing pseudomedians within sliding windows, a O(n2logn) calculation in each window. This poor time complexity is an issue for tiling array analysis and could prove to be a real bottleneck as tiling microarray experiments become grander in scope and finer in resolution. Results We therefore implemented Monahan's HLQEST algorithm that reduces the runtime complexity for computing the pseudomedian of n numbers to O(nlogn) from O(n2logn). For a representative tiling microarray dataset, this modification reduced the smoothing procedure's runtime by nearly 90%. We then leveraged the fact that elements within sliding windows remain largely unchanged in overlapping windows (as one slides across genomic space) to further reduce computation by an additional 43%. This was achieved by the application of skip lists to maintaining a sorted list of values from window to window. This sorted list could be maintained with simple O(log n) inserts and deletes. We illustrate the favorable scaling properties of our algorithms with both time complexity analysis and benchmarking on synthetic datasets. Conclusion Tiling microarray analyses that rely upon a sliding window pseudomedian calculation can require many hours of computation. We have eased this requirement significantly by implementing efficient algorithms that scale well with genomic feature density. This result not only speeds the current standard analyses, but also makes possible ones where many iterations of the filter may be required, such as might be required in a bootstrap or parameter estimation setting. Source code and executables are available at . PMID:17555595

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms.

PubMed

Jani, Saurin D; Argraves, Gary L; Barth, Jeremy L; Argraves, W Scott

2010-04-01

An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases. Here we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina. GeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG pathway diagrams and heatmaps of expression intensity values. GeneMesh is freely available online at http://proteogenomics.musc.edu/genemesh/.

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data.

PubMed

Guzzi, Pietro Hiram; Cannataro, Mario

2013-08-01

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power Tools), (ii) the manual loading of preprocessing libraries, and (iii) the management of intermediate files, such as results and metadata. Micro-Analyzer users can directly manage Affymetrix binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the Micro-Analyzer tool can load the preprocessed data directly into the well-known TM4 platform, extending in such a way also the TM4 capabilities. Consequently, Micro Analyzer offers the following advantages: (i) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or due to the use of old libraries, (ii) it enables the combined and centralized pre-processing of different arrays, (iii) it may enhance the quality of further analysis by storing the workflow, i.e. information about the preprocessing steps, and (iv) finally Micro-Analzyer is freely available as a standalone application at the project web site http://sourceforge.net/projects/microanalyzer/. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Analysis of population structure and genetic history of cattle breeds based on high-density SNP data

USDA-ARS?s Scientific Manuscript database

Advances in single nucleotide polymorphism (SNP) genotyping microarrays have facilitated a new understanding of population structure and evolutionary history for several species. Most existing studies in livestock were based on low density SNP arrays. The first wave of low density SNP studies on cat...

A Versatile Microarray Platform for Capturing Rare Cells

NASA Astrophysics Data System (ADS)

Brinkmann, Falko; Hirtz, Michael; Haller, Anna; Gorges, Tobias M.; Vellekoop, Michael J.; Riethdorf, Sabine; Müller, Volkmar; Pantel, Klaus; Fuchs, Harald

2015-10-01

Analyses of rare events occurring at extremely low frequencies in body fluids are still challenging. We established a versatile microarray-based platform able to capture single target cells from large background populations. As use case we chose the challenging application of detecting circulating tumor cells (CTCs) - about one cell in a billion normal blood cells. After incubation with an antibody cocktail, targeted cells are extracted on a microarray in a microfluidic chip. The accessibility of our platform allows for subsequent recovery of targets for further analysis. The microarray facilitates exclusion of false positive capture events by co-localization allowing for detection without fluorescent labelling. Analyzing blood samples from cancer patients with our platform reached and partly outreached gold standard performance, demonstrating feasibility for clinical application. Clinical researchers free choice of antibody cocktail without need for altered chip manufacturing or incubation protocol, allows virtual arbitrary targeting of capture species and therefore wide spread applications in biomedical sciences.

DNA microarrays and their use in dermatology.

PubMed

Mlakar, Vid; Glavac, Damjan

2007-03-01

Multiple different DNA microarray technologies are available on the market today. They can be used for studying either DNA or RNA with the purpose of identifying and explaining the role of genes involved in different processes. This paper reviews different DNA microarray platforms available for such studies and their usage in cases of malignant melanomas, psoriasis, and exposure of keratinocytes and melanocytes to UV illumination.

Microarray analysis of genes associated with cell surface NIS protein levels in breast cancer.

PubMed

Beyer, Sasha J; Zhang, Xiaoli; Jimenez, Rafael E; Lee, Mei-Ling T; Richardson, Andrea L; Huang, Kun; Jhiang, Sissy M

2011-10-11

Na+/I- symporter (NIS)-mediated iodide uptake allows radioiodine therapy for thyroid cancer. NIS is also expressed in breast tumors, raising potential for radionuclide therapy of breast cancer. However, NIS expression in most breast cancers is low and may not be sufficient for radionuclide therapy. We aimed to identify biomarkers associated with NIS expression such that mechanisms underlying NIS modulation in human breast tumors may be elucidated. Published oligonucleotide microarray data within the National Center for Biotechnology Information Gene Expression Omnibus database were analyzed to identify gene expression tightly correlated with NIS mRNA level among human breast tumors. NIS immunostaining was performed in a tissue microarray composed of 28 human breast tumors which had corresponding oligonucleotide microarray data available for each tumor such that gene expression associated with cell surface NIS protein level could be identified. NIS mRNA levels do not vary among breast tumors or when compared to normal breast tissues when detected by Affymetrix oligonucleotide microarray platforms. Cell surface NIS protein levels are much more variable than their corresponding NIS mRNA levels. Despite a limited number of breast tumors examined, our analysis identified cysteinyl-tRNA synthetase as a biomarker that is highly associated with cell surface NIS protein levels in the ER-positive breast cancer subtype. Further investigation on genes associated with cell surface NIS protein levels within each breast cancer molecular subtype may lead to novel targets for selectively increasing NIS expression/function in a subset of breast cancers patients.

Transcriptomic Dose-Response Analysis for Mode of Action and Risk Assessment

EPA Science Inventory

Microarray and RNA-seq technologies can play an important role in assessing the health risks associated with environmental exposures. The utility of gene expression data to predict hazard has been well documented. Early toxicogenomics studies used relatively high, single doses w...

Inheritance of a Balanced t(12;20)(q24.33;p12.2) and Unbalanced der(13)t(7;13)(p21.3;q33.2) from a Maternally Derived Double Balanced Translocation Carrier.

PubMed

Peterson, Jess F; Geddes, Gabrielle C; Basel, Donald G; Schippman, Dana; Grignon, John W; vanTuinen, Peter; Kappes, Ulrike P

2018-03-01

We report a 4-month-old male proband with a history of prominent forehead, hypertelorism, ear abnormalities, micrognathia, hypospadias, and multiple cardiac abnormalities. Initial microarray analysis detected a concurrent 7p21.3-p22.3 duplication and 13q33.2-q34 deletion indicating an unbalanced rearrangement. However, subsequent conventional cytogenetic studies only revealed what appeared to be a balanced t(12;20)(q24.33;p12.2). Fluorescence in situ hybridization (FISH) using chromosome-specific subtelomere probes confirmed the presence of an unbalanced der(13)t(7;13)(p21.3;q33.2) and balanced t(12;20)(q24.33;p12.2), both of maternal origin. In addition to our unique clinical findings, this case highlights the benefits and limitations of both conventional cytogenetic studies and microarray analysis and how FISH complements each methodology.

Molecular profiles of pre- and postoperative breast cancer tumours reveal differentially expressed genes.

PubMed

Riis, Margit L H; Lüders, Torben; Markert, Elke K; Haakensen, Vilde D; Nesbakken, Anne-Jorun; Kristensen, Vessela N; Bukholm, Ida R K

2012-01-01

Gene expression studies on breast cancer have generally been performed on tissue obtained at the time of surgery. In this study, we have compared the gene expression profiles in preoperative tissue (core needle biopsies) while tumor is still in its normal milieu to postoperative tissue from the same tumor obtained during surgery. Thirteen patients were included of which eleven had undergone sentinel node diagnosis procedure before operation. Microarray gene expression analysis was performed using total RNA from all the samples. Paired significance analysis of microarrays revealed 228 differently expressed genes, including several early response stress-related genes such as members of the fos and jun families as well as genes of which the expression has previously been associated with cancer. The expression profiles found in the analyses of breast cancer tissue must be evaluated with caution. Different profiles may simply be the result of differences in the surgical trauma and timing of when samples are taken and not necessarily associated with tumor biology.

Molecular Profiles of Pre- and Postoperative Breast Cancer Tumours Reveal Differentially Expressed Genes

PubMed Central

Riis, Margit L. H.; Lüders, Torben; Markert, Elke K.; Haakensen, Vilde D.; Nesbakken, Anne-Jorun; Kristensen, Vessela N.; Bukholm, Ida R. K.

2012-01-01

Gene expression studies on breast cancer have generally been performed on tissue obtained at the time of surgery. In this study, we have compared the gene expression profiles in preoperative tissue (core needle biopsies) while tumor is still in its normal milieu to postoperative tissue from the same tumor obtained during surgery. Thirteen patients were included of which eleven had undergone sentinel node diagnosis procedure before operation. Microarray gene expression analysis was performed using total RNA from all the samples. Paired significance analysis of microarrays revealed 228 differently expressed genes, including several early response stress-related genes such as members of the fos and jun families as well as genes of which the expression has previously been associated with cancer. The expression profiles found in the analyses of breast cancer tissue must be evaluated with caution. Different profiles may simply be the result of differences in the surgical trauma and timing of when samples are taken and not necessarily associated with tumor biology. PMID:23227362

Meta-review of protein network regulating obesity between validated obesity candidate genes in the white adipose tissue of high-fat diet-induced obese C57BL/6J mice.

PubMed

Kim, Eunjung; Kim, Eun Jung; Seo, Seung-Won; Hur, Cheol-Goo; McGregor, Robin A; Choi, Myung-Sook

2014-01-01

Worldwide obesity and related comorbidities are increasing, but identifying new therapeutic targets remains a challenge. A plethora of microarray studies in diet-induced obesity models has provided large datasets of obesity associated genes. In this review, we describe an approach to examine the underlying molecular network regulating obesity, and we discuss interactions between obesity candidate genes. We conducted network analysis on functional protein-protein interactions associated with 25 obesity candidate genes identified in a literature-driven approach based on published microarray studies of diet-induced obesity. The obesity candidate genes were closely associated with lipid metabolism and inflammation. Peroxisome proliferator activated receptor gamma (Pparg) appeared to be a core obesity gene, and obesity candidate genes were highly interconnected, suggesting a coordinately regulated molecular network in adipose tissue. In conclusion, the current network analysis approach may help elucidate the underlying molecular network regulating obesity and identify anti-obesity targets for therapeutic intervention.

Unravelling Glucan Recognition Systems by Glycome Microarrays Using the Designer Approach and Mass Spectrometry*

PubMed Central

Palma, Angelina S.; Liu, Yan; Zhang, Hongtao; Zhang, Yibing; McCleary, Barry V.; Yu, Guangli; Huang, Qilin; Guidolin, Leticia S.; Ciocchini, Andres E.; Torosantucci, Antonella; Wang, Denong; Carvalho, Ana Luísa; Fontes, Carlos M. G. A.; Mulloy, Barbara; Childs, Robert A.; Feizi, Ten; Chai, Wengang

2015-01-01

Glucans are polymers of d-glucose with differing linkages in linear or branched sequences. They are constituents of microbial and plant cell-walls and involved in important bio-recognition processes, including immunomodulation, anticancer activities, pathogen virulence, and plant cell-wall biodegradation. Translational possibilities for these activities in medicine and biotechnology are considerable. High-throughput micro-methods are needed to screen proteins for recognition of specific glucan sequences as a lead to structure–function studies and their exploitation. We describe construction of a “glucome” microarray, the first sequence-defined glycome-scale microarray, using a “designer” approach from targeted ligand-bearing glucans in conjunction with a novel high-sensitivity mass spectrometric sequencing method, as a screening tool to assign glucan recognition motifs. The glucome microarray comprises 153 oligosaccharide probes with high purity, representing major sequences in glucans. Negative-ion electrospray tandem mass spectrometry with collision-induced dissociation was used for complete linkage analysis of gluco-oligosaccharides in linear “homo” and “hetero” and branched sequences. The system is validated using antibodies and carbohydrate-binding modules known to target α- or β-glucans in different biological contexts, extending knowledge on their specificities, and applied to reveal new information on glucan recognition by two signaling molecules of the immune system against pathogens: Dectin-1 and DC-SIGN. The sequencing of the glucan oligosaccharides by the MS method and their interrogation on the microarrays provides detailed information on linkage, sequence and chain length requirements of glucan-recognizing proteins, and are a sensitive means of revealing unsuspected sequences in the polysaccharides. PMID:25670804

Screening for Intellectual Disability Using High-Resolution CMA Technology in a Retrospective Cohort from Central Brazil

PubMed Central

Pereira, Rodrigo Roncato; Pinto, Irene Plaza; Minasi, Lysa Bernardes; de Melo, Aldaires Vieira; da Cruz e Cunha, Damiana Mirian; Cruz, Alex Silva; Ribeiro, Cristiano Luiz; da Silva, Cláudio Carlos; de Melo e Silva, Daniela; da Cruz, Aparecido Divino

2014-01-01

Intellectual disability is a complex, variable, and heterogeneous disorder, representing a disabling condition diagnosed worldwide, and the etiologies are multiple and highly heterogeneous. Microscopic chromosomal abnormalities and well-characterized genetic conditions are the most common causes of intellectual disability. Chromosomal Microarray Analysis analyses have made it possible to identify putatively pathogenic copy number variation that could explain the molecular etiology of intellectual disability. The aim of the current study was to identify possible submicroscopic genomic alterations using a high-density chromosomal microarray in a retrospective cohort of patients with otherwise undiagnosable intellectual disabilities referred by doctors from the public health system in Central Brazil. The CytoScan HD technology was used to detect changes in the genome copy number variation of patients who had intellectual disability and a normal karyotype. The analysis detected 18 CNVs in 60% of patients. Pathogenic CNVs represented about 22%, so it was possible to propose the etiology of intellectual disability for these patients. Likely pathogenic and unknown clinical significance CNVs represented 28% and 50%, respectively. Inherited and de novo CNVs were equally distributed. We report the nature of CNVs in patients from Central Brazil, representing a population not yet screened by microarray technologies. PMID:25061755

DNA microarray-based experimental strategy for trustworthy expression profiling of the hippocampal genes by astaxanthin supplementation in adult mouse

PubMed Central

Yook, Jang Soo; Shibato, Junko; Rakwal, Randeep; Soya, Hideaki

2015-01-01

Naturally occurring astaxantin (ASX) is one of the noticeable carotenoid and dietary supplement, which has strong antioxidant and anti-inflammatory properties, and neuroprotective effects in the brain through crossing the blood–brain barrier. Specially, we are interested in the role of ASX as a brain food. Although ASX has been suggested to have potential benefit to the brain function, the underlying molecular mechanisms and events mediating such effect remain unknown. Here we examined molecular factors in the hippocampus of adult mouse fed ASX diets (0.1% and 0.5% doses) using DNA microarray (Agilent 4 × 44 K whole mouse genome chip) analysis. In this study, we described in detail our experimental workflow and protocol, and validated quality controls with the housekeeping gene expression (Gapdh and Beta-actin) on the dye-swap based approach to advocate our microarray data, which have been uploaded to Gene Expression Omnibus (accession number GSE62197) as a gene resource for the scientific community. This data will also form an important basis for further detailed experiments and bioinformatics analysis with an aim to unravel the potential molecular pathways or mechanisms underlying the positive effects of ASX supplementation on the brain, in particular the hippocampus. PMID:26981356

MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis

PubMed Central

Chu, Vu T; Gottardo, Raphael; Raftery, Adrian E; Bumgarner, Roger E; Yeung, Ka Yee

2008-01-01

We present MeV+R, an integration of the JAVA MultiExperiment Viewer program with Bioconductor packages. This integration of MultiExperiment Viewer and R is easily extensible to other R packages and provides users with point and click access to traditionally command line driven tools written in R. We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA. PMID:18652698