BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network.

PubMed

Ishiwata, Ryosuke R; Morioka, Masaki S; Ogishima, Soichi; Tanaka, Hiroshi

2009-02-15

BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions. BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.

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

SIMULATION AND VISUALIZATION OF FLOW PATTERN IN MICROARRAYS FOR LIQUID PHASE OLIGONUCLEOTIDE AND PEPTIDE SYNTHESIS

PubMed Central

O-Charoen, Sirimon; Srivannavit, Onnop; Gulari, Erdogan

2008-01-01

Microfluidic microarrays have been developed for economical and rapid parallel synthesis of oligonucleotide and peptide libraries. For a synthesis system to be reproducible and uniform, it is crucial to have a uniform reagent delivery throughout the system. Computational fluid dynamics (CFD) is used to model and simulate the microfluidic microarrays to study geometrical effects on flow patterns. By proper design geometry, flow uniformity could be obtained in every microreactor in the microarrays. PMID:17480053

eXframe: reusable framework for storage, analysis and visualization of genomics experiments

PubMed Central

2011-01-01

Background Genome-wide experiments are routinely conducted to measure gene expression, DNA-protein interactions and epigenetic status. Structured metadata for these experiments is imperative for a complete understanding of experimental conditions, to enable consistent data processing and to allow retrieval, comparison, and integration of experimental results. Even though several repositories have been developed for genomics data, only a few provide annotation of samples and assays using controlled vocabularies. Moreover, many of them are tailored for a single type of technology or measurement and do not support the integration of multiple data types. Results We have developed eXframe - a reusable web-based framework for genomics experiments that provides 1) the ability to publish structured data compliant with accepted standards 2) support for multiple data types including microarrays and next generation sequencing 3) query, analysis and visualization integration tools (enabled by consistent processing of the raw data and annotation of samples) and is available as open-source software. We present two case studies where this software is currently being used to build repositories of genomics experiments - one contains data from hematopoietic stem cells and another from Parkinson's disease patients. Conclusion The web-based framework eXframe offers structured annotation of experiments as well as uniform processing and storage of molecular data from microarray and next generation sequencing platforms. The framework allows users to query and integrate information across species, technologies, measurement types and experimental conditions. Our framework is reusable and freely modifiable - other groups or institutions can deploy their own custom web-based repositories based on this software. It is interoperable with the most important data formats in this domain. We hope that other groups will not only use eXframe, but also contribute their own useful modifications. PMID:22103807

A Web-Based Multi-Database System Supporting Distributed Collaborative Management and Sharing of Microarray Experiment Information

PubMed Central

Burgarella, Sarah; Cattaneo, Dario; Masseroli, Marco

2006-01-01

We developed MicroGen, a multi-database Web based system for managing all the information characterizing spotted microarray experiments. It supports information gathering and storing according to the Minimum Information About Microarray Experiments (MIAME) standard. It also allows easy sharing of information and data among all multidisciplinary actors involved in spotted microarray experiments. PMID:17238488

NCBI GEO: mining millions of expression profiles--database and tools.

PubMed

Barrett, Tanya; Suzek, Tugba O; Troup, Dennis B; Wilhite, Stephen E; Ngau, Wing-Chi; Ledoux, Pierre; Rudnev, Dmitry; Lash, Alex E; Fujibuchi, Wataru; Edgar, Ron

2005-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30,000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

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

KUTE-BASE: storing, downloading and exporting MIAME-compliant microarray experiments in minutes rather than hours.

PubMed

Draghici, Sorin; Tarca, Adi L; Yu, Longfei; Ethier, Stephen; Romero, Roberto

2008-03-01

The BioArray Software Environment (BASE) is a very popular MIAME-compliant, web-based microarray data repository. However in BASE, like in most other microarray data repositories, the experiment annotation and raw data uploading can be very timeconsuming, especially for large microarray experiments. We developed KUTE (Karmanos Universal daTabase for microarray Experiments), as a plug-in for BASE 2.0 that addresses these issues. KUTE provides an automatic experiment annotation feature and a completely redesigned data work-flow that dramatically reduce the human-computer interaction time. For instance, in BASE 2.0 a typical Affymetrix experiment involving 100 arrays required 4 h 30 min of user interaction time forexperiment annotation, and 45 min for data upload/download. In contrast, for the same experiment, KUTE required only 28 min of user interaction time for experiment annotation, and 3.3 min for data upload/download. http://vortex.cs.wayne.edu/kute/index.html.

MicroGen: a MIAME compliant web system for microarray experiment information and workflow management.

PubMed

Burgarella, Sarah; Cattaneo, Dario; Pinciroli, Francesco; Masseroli, Marco

2005-12-01

Improvements of bio-nano-technologies and biomolecular techniques have led to increasing production of high-throughput experimental data. Spotted cDNA microarray is one of the most diffuse technologies, used in single research laboratories and in biotechnology service facilities. Although they are routinely performed, spotted microarray experiments are complex procedures entailing several experimental steps and actors with different technical skills and roles. During an experiment, involved actors, who can also be located in a distance, need to access and share specific experiment information according to their roles. Furthermore, complete information describing all experimental steps must be orderly collected to allow subsequent correct interpretation of experimental results. We developed MicroGen, a web system for managing information and workflow in the production pipeline of spotted microarray experiments. It is constituted of a core multi-database system able to store all data completely characterizing different spotted microarray experiments according to the Minimum Information About Microarray Experiments (MIAME) standard, and of an intuitive and user-friendly web interface able to support the collaborative work required among multidisciplinary actors and roles involved in spotted microarray experiment production. MicroGen supports six types of user roles: the researcher who designs and requests the experiment, the spotting operator, the hybridisation operator, the image processing operator, the system administrator, and the generic public user who can access the unrestricted part of the system to get information about MicroGen services. MicroGen represents a MIAME compliant information system that enables managing workflow and supporting collaborative work in spotted microarray experiment production.

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.

ExpressionDB: An open source platform for distributing genome-scale datasets.

PubMed

Hughes, Laura D; Lewis, Scott A; Hughes, Michael E

2017-01-01

RNA-sequencing (RNA-seq) and microarrays are methods for measuring gene expression across the entire transcriptome. Recent advances have made these techniques practical and affordable for essentially any laboratory with experience in molecular biology. A variety of computational methods have been developed to decrease the amount of bioinformatics expertise necessary to analyze these data. Nevertheless, many barriers persist which discourage new labs from using functional genomics approaches. Since high-quality gene expression studies have enduring value as resources to the entire research community, it is of particular importance that small labs have the capacity to share their analyzed datasets with the research community. Here we introduce ExpressionDB, an open source platform for visualizing RNA-seq and microarray data accommodating virtually any number of different samples. ExpressionDB is based on Shiny, a customizable web application which allows data sharing locally and online with customizable code written in R. ExpressionDB allows intuitive searches based on gene symbols, descriptions, or gene ontology terms, and it includes tools for dynamically filtering results based on expression level, fold change, and false-discovery rates. Built-in visualization tools include heatmaps, volcano plots, and principal component analysis, ensuring streamlined and consistent visualization to all users. All of the scripts for building an ExpressionDB with user-supplied data are freely available on GitHub, and the Creative Commons license allows fully open customization by end-users. We estimate that a demo database can be created in under one hour with minimal programming experience, and that a new database with user-supplied expression data can be completed and online in less than one day.

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data

PubMed Central

Jupiter, Daniel; Chen, Hailin; VanBuren, Vincent

2009-01-01

Background Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult. Results STARNET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. STARNET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new HEATSEEKER module. Conclusion STARNET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a STARNET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at , and does not require user registration. PMID:19828039

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.

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

NASA Astrophysics Data System (ADS)

Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

2016-03-01

Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context.

PubMed

Mader, Malte; Simon, Ronald; Steinbiss, Sascha; Kurtz, Stefan

2011-07-28

The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate. We have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists. FISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle.

FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context

PubMed Central

2011-01-01

Background The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate. Results We have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists. Conclusions FISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle. PMID:21884636

Design of microarray experiments for genetical genomics studies.

PubMed

Bueno Filho, Júlio S S; Gilmour, Steven G; Rosa, Guilherme J M

2006-10-01

Microarray experiments have been used recently in genetical genomics studies, as an additional tool to understand the genetic mechanisms governing variation in complex traits, such as for estimating heritabilities of mRNA transcript abundances, for mapping expression quantitative trait loci, and for inferring regulatory networks controlling gene expression. Several articles on the design of microarray experiments discuss situations in which treatment effects are assumed fixed and without any structure. In the case of two-color microarray platforms, several authors have studied reference and circular designs. Here, we discuss the optimal design of microarray experiments whose goals refer to specific genetic questions. Some examples are used to illustrate the choice of a design for comparing fixed, structured treatments, such as genotypic groups. Experiments targeting single genes or chromosomic regions (such as with transgene research) or multiple epistatic loci (such as within a selective phenotyping context) are discussed. In addition, microarray experiments in which treatments refer to families or to subjects (within family structures or complex pedigrees) are presented. In these cases treatments are more appropriately considered to be random effects, with specific covariance structures, in which the genetic goals relate to the estimation of genetic variances and the heritability of transcriptional abundances.

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

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.

An expression database for roots of the model legume Medicago truncatula under salt stress

PubMed Central

2009-01-01

Background Medicago truncatula is a model legume whose genome is currently being sequenced by an international consortium. Abiotic stresses such as salt stress limit plant growth and crop productivity, including those of legumes. We anticipate that studies on M. truncatula will shed light on other economically important legumes across the world. Here, we report the development of a database called MtED that contains gene expression profiles of the roots of M. truncatula based on time-course salt stress experiments using the Affymetrix Medicago GeneChip. Our hope is that MtED will provide information to assist in improving abiotic stress resistance in legumes. Description The results of our microarray experiment with roots of M. truncatula under 180 mM sodium chloride were deposited in the MtED database. Additionally, sequence and annotation information regarding microarray probe sets were included. MtED provides functional category analysis based on Gene and GeneBins Ontology, and other Web-based tools for querying and retrieving query results, browsing pathways and transcription factor families, showing metabolic maps, and comparing and visualizing expression profiles. Utilities like mapping probe sets to genome of M. truncatula and In-Silico PCR were implemented by BLAT software suite, which were also available through MtED database. Conclusion MtED was built in the PHP script language and as a MySQL relational database system on a Linux server. It has an integrated Web interface, which facilitates ready examination and interpretation of the results of microarray experiments. It is intended to help in selecting gene markers to improve abiotic stress resistance in legumes. MtED is available at http://bioinformatics.cau.edu.cn/MtED/. PMID:19906315

An expression database for roots of the model legume Medicago truncatula under salt stress.

PubMed

Li, Daofeng; Su, Zhen; Dong, Jiangli; Wang, Tao

2009-11-11

Medicago truncatula is a model legume whose genome is currently being sequenced by an international consortium. Abiotic stresses such as salt stress limit plant growth and crop productivity, including those of legumes. We anticipate that studies on M. truncatula will shed light on other economically important legumes across the world. Here, we report the development of a database called MtED that contains gene expression profiles of the roots of M. truncatula based on time-course salt stress experiments using the Affymetrix Medicago GeneChip. Our hope is that MtED will provide information to assist in improving abiotic stress resistance in legumes. The results of our microarray experiment with roots of M. truncatula under 180 mM sodium chloride were deposited in the MtED database. Additionally, sequence and annotation information regarding microarray probe sets were included. MtED provides functional category analysis based on Gene and GeneBins Ontology, and other Web-based tools for querying and retrieving query results, browsing pathways and transcription factor families, showing metabolic maps, and comparing and visualizing expression profiles. Utilities like mapping probe sets to genome of M. truncatula and In-Silico PCR were implemented by BLAT software suite, which were also available through MtED database. MtED was built in the PHP script language and as a MySQL relational database system on a Linux server. It has an integrated Web interface, which facilitates ready examination and interpretation of the results of microarray experiments. It is intended to help in selecting gene markers to improve abiotic stress resistance in legumes. MtED is available at http://bioinformatics.cau.edu.cn/MtED/.

The MGED Ontology: a resource for semantics-based description of microarray experiments.

PubMed

Whetzel, Patricia L; Parkinson, Helen; Causton, Helen C; Fan, Liju; Fostel, Jennifer; Fragoso, Gilberto; Game, Laurence; Heiskanen, Mervi; Morrison, Norman; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Taylor, Chris; White, Joseph; Stoeckert, Christian J

2006-04-01

The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). Stoeckrt@pcbi.upenn.edu Supplementary data are available at Bioinformatics online.

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.

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.

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.

A DNA microarray-based methylation-sensitive (MS)-AFLP hybridization method for genetic and epigenetic analyses.

PubMed

Yamamoto, F; Yamamoto, M

2004-07-01

We previously developed a PCR-based DNA fingerprinting technique named the Methylation Sensitive (MS)-AFLP method, which permits comparative genome-wide scanning of methylation status with a manageable number of fingerprinting experiments. The technique uses the methylation sensitive restriction enzyme NotI in the context of the existing Amplified Fragment Length Polymorphism (AFLP) method. Here we report the successful conversion of this gel electrophoresis-based DNA fingerprinting technique into a DNA microarray hybridization technique (DNA Microarray MS-AFLP). By performing a total of 30 (15 x 2 reciprocal labeling) DNA Microarray MS-AFLP hybridization experiments on genomic DNA from two breast and three prostate cancer cell lines in all pairwise combinations, and Southern hybridization experiments using more than 100 different probes, we have demonstrated that the DNA Microarray MS-AFLP is a reliable method for genetic and epigenetic analyses. No statistically significant differences were observed in the number of differences between the breast-prostate hybridization experiments and the breast-breast or prostate-prostate comparisons.

Arabidopsis Gene Family Profiler (aGFP)--user-oriented transcriptomic database with easy-to-use graphic interface.

PubMed

Dupl'áková, Nikoleta; Renák, David; Hovanec, Patrik; Honysová, Barbora; Twell, David; Honys, David

2007-07-23

Microarray technologies now belong to the standard functional genomics toolbox and have undergone massive development leading to increased genome coverage, accuracy and reliability. The number of experiments exploiting microarray technology has markedly increased in recent years. In parallel with the rapid accumulation of transcriptomic data, on-line analysis tools are being introduced to simplify their use. Global statistical data analysis methods contribute to the development of overall concepts about gene expression patterns and to query and compose working hypotheses. More recently, these applications are being supplemented with more specialized products offering visualization and specific data mining tools. We present a curated gene family-oriented gene expression database, Arabidopsis Gene Family Profiler (aGFP; http://agfp.ueb.cas.cz), which gives the user access to a large collection of normalised Affymetrix ATH1 microarray datasets. The database currently contains NASC Array and AtGenExpress transcriptomic datasets for various tissues at different developmental stages of wild type plants gathered from nearly 350 gene chips. The Arabidopsis GFP database has been designed as an easy-to-use tool for users needing an easily accessible resource for expression data of single genes, pre-defined gene families or custom gene sets, with the further possibility of keyword search. Arabidopsis Gene Family Profiler presents a user-friendly web interface using both graphic and text output. Data are stored at the MySQL server and individual queries are created in PHP script. The most distinguishable features of Arabidopsis Gene Family Profiler database are: 1) the presentation of normalized datasets (Affymetrix MAS algorithm and calculation of model-based gene-expression values based on the Perfect Match-only model); 2) the choice between two different normalization algorithms (Affymetrix MAS4 or MAS5 algorithms); 3) an intuitive interface; 4) an interactive "virtual plant" visualizing the spatial and developmental expression profiles of both gene families and individual genes. Arabidopsis GFP gives users the possibility to analyze current Arabidopsis developmental transcriptomic data starting with simple global queries that can be expanded and further refined to visualize comparative and highly selective gene expression profiles.

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

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.

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.

HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline.

PubMed

Khomtchouk, Bohdan B; Van Booven, Derek J; Wahlestedt, Claes

2014-01-01

The graphical visualization of gene expression data using heatmaps has become an integral component of modern-day medical research. Heatmaps are used extensively to plot quantitative differences in gene expression levels, such as those measured with RNAseq and microarray experiments, to provide qualitative large-scale views of the transcriptonomic landscape. Creating high-quality heatmaps is a computationally intensive task, often requiring considerable programming experience, particularly for customizing features to a specific dataset at hand. Software to create publication-quality heatmaps is developed with the R programming language, C++ programming language, and OpenGL application programming interface (API) to create industry-grade high performance graphics. We create a graphical user interface (GUI) software package called HeatmapGenerator for Windows OS and Mac OS X as an intuitive, user-friendly alternative to researchers with minimal prior coding experience to allow them to create publication-quality heatmaps using R graphics without sacrificing their desired level of customization. The simplicity of HeatmapGenerator is that it only requires the user to upload a preformatted input file and download the publicly available R software language, among a few other operating system-specific requirements. Advanced features such as color, text labels, scaling, legend construction, and even database storage can be easily customized with no prior programming knowledge. We provide an intuitive and user-friendly software package, HeatmapGenerator, to create high-quality, customizable heatmaps generated using the high-resolution color graphics capabilities of R. The software is available for Microsoft Windows and Apple Mac OS X. HeatmapGenerator is released under the GNU General Public License and publicly available at: http://sourceforge.net/projects/heatmapgenerator/. The Mac OS X direct download is available at: http://sourceforge.net/projects/heatmapgenerator/files/HeatmapGenerator_MAC_OSX.tar.gz/download. The Windows OS direct download is available at: http://sourceforge.net/projects/heatmapgenerator/files/HeatmapGenerator_WINDOWS.zip/download.

Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images

PubMed Central

2015-01-01

Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies. PMID:24417579

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.

Experimental design for three-color and four-color gene expression microarrays.

PubMed

Woo, Yong; Krueger, Winfried; Kaur, Anupinder; Churchill, Gary

2005-06-01

Three-color microarrays, compared with two-color microarrays, can increase design efficiency and power to detect differential expression without additional samples and arrays. Furthermore, three-color microarray technology is currently available at a reasonable cost. Despite the potential advantages, clear guidelines for designing and analyzing three-color experiments do not exist. We propose a three- and a four-color cyclic design (loop) and a complementary graphical representation to help design experiments that are balanced, efficient and robust to hybridization failures. In theory, three-color loop designs are more efficient than two-color loop designs. Experiments using both two- and three-color platforms were performed in parallel and their outputs were analyzed using linear mixed model analysis in R/MAANOVA. These results demonstrate that three-color experiments using the same number of samples (and fewer arrays) will perform as efficiently as two-color experiments. The improved efficiency of the design is somewhat offset by a reduced dynamic range and increased variability in the three-color experimental system. This result suggests that, with minor technological improvements, three-color microarrays using loop designs could detect differential expression more efficiently than two-color loop designs. http://www.jax.org/staff/churchill/labsite/software Multicolor cyclic design construction methods and examples along with additional results of the experiment are provided at http://www.jax.org/staff/churchill/labsite/pubs/yong.

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.

VisANT 3.0: new modules for pathway visualization, editing, prediction and construction.

PubMed

Hu, Zhenjun; Ng, David M; Yamada, Takuji; Chen, Chunnuan; Kawashima, Shuichi; Mellor, Joe; Linghu, Bolan; Kanehisa, Minoru; Stuart, Joshua M; DeLisi, Charles

2007-07-01

With the integration of the KEGG and Predictome databases as well as two search engines for coexpressed genes/proteins using data sets obtained from the Stanford Microarray Database (SMD) and Gene Expression Omnibus (GEO) database, VisANT 3.0 supports exploratory pathway analysis, which includes multi-scale visualization of multiple pathways, editing and annotating pathways using a KEGG compatible visual notation and visualization of expression data in the context of pathways. Expression levels are represented either by color intensity or by nodes with an embedded expression profile. Multiple experiments can be navigated or animated. Known KEGG pathways can be enriched by querying either coexpressed components of known pathway members or proteins with known physical interactions. Predicted pathways for genes/proteins with unknown functions can be inferred from coexpression or physical interaction data. Pathways produced in VisANT can be saved as computer-readable XML format (VisML), graphic images or high-resolution Scalable Vector Graphics (SVG). Pathways in the format of VisML can be securely shared within an interested group or published online using a simple Web link. VisANT is freely available at http://visant.bu.edu.

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.

A perspective on microarrays: current applications, pitfalls, and potential uses

PubMed Central

Jaluria, Pratik; Konstantopoulos, Konstantinos; Betenbaugh, Michael; Shiloach, Joseph

2007-01-01

With advances in robotics, computational capabilities, and the fabrication of high quality glass slides coinciding with increased genomic information being available on public databases, microarray technology is increasingly being used in laboratories around the world. In fact, fields as varied as: toxicology, evolutionary biology, drug development and production, disease characterization, diagnostics development, cellular physiology and stress responses, and forensics have benefiting from its use. However, for many researchers not familiar with microarrays, current articles and reviews often address neither the fundamental principles behind the technology nor the proper designing of experiments. Although, microarray technology is relatively simple, conceptually, its practice does require careful planning and detailed understanding of the limitations inherently present. Without these considerations, it can be exceedingly difficult to ascertain valuable information from microarray data. Therefore, this text aims to outline key features in microarray technology, paying particular attention to current applications as outlined in recent publications, experimental design, statistical methods, and potential uses. Furthermore, this review is not meant to be comprehensive, but rather substantive; highlighting important concepts and detailing steps necessary to conduct and interpret microarray experiments. Collectively, the information included in this text will highlight the versatility of microarray technology and provide a glimpse of what the future may hold. PMID:17254338

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.

Volcano plots in analyzing differential expressions with mRNA microarrays.

PubMed

Li, Wentian

2012-12-01

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

Questioning the utility of pooling samples in microarray experiments with cell lines.

PubMed

Lusa, L; Cappelletti, V; Gariboldi, M; Ferrario, C; De Cecco, L; Reid, J F; Toffanin, S; Gallus, G; McShane, L M; Daidone, M G; Pierotti, M A

2006-01-01

We describe a microarray experiment using the MCF-7 breast cancer cell line in two different experimental conditions for which the same number of independent pools as the number of individual samples was hybridized on Affymetrix GeneChips. Unexpectedly, when using individual samples, the number of probe sets found to be differentially expressed between treated and untreated cells was about three times greater than that found using pools. These findings indicate that pooling samples in microarray experiments where the biological variability is expected to be small might not be helpful and could even decrease one's ability to identify differentially expressed genes.

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

Molecular biology of myopia.

PubMed

Schaeffel, Frank; Simon, Perikles; Feldkaemper, Marita; Ohngemach, Sibylle; Williams, Robert W

2003-09-01

Experiments in animal models of myopia have emphasised the importance of visual input in emmetropisation but it is also evident that the development of human myopia is influenced to some degree by genetic factors. Molecular genetic approaches can help to identify both the genes involved in the control of ocular development and the potential targets for pharmacological intervention. This review covers a variety of techniques that are being used to study the molecular biology of myopia. In the first part, we describe techniques used to analyse visually induced changes in gene expression: Northern Blot, polymerase chain reaction (PCR) and real-time PCR to obtain semi-quantitative and quantitative measures of changes in transcription level of a known gene, differential display reverse transcription PCR (DD-RT-PCR) to search for new genes that are controlled by visual input, rapid amplification of 5' cDNA (5'-RACE) to extend the 5' end of sequences that are regulated by visual input, in situ hybridisation to localise the expression of a given gene in a tissue and oligonucleotide microarray assays to simultaneously test visually induced changes in thousands of transcripts in single experiments. In the second part, we describe techniques that are used to localise regions in the genome that contain genes that are involved in the control of eye growth and refractive errors in mice and humans. These include quantitative trait loci (QTL) mapping, exploiting experimental test crosses of mice and transmission disequilibrium tests (TDT) in humans to find chromosomal intervals that harbour genes involved in myopia development. We review several successful applications of this battery of techniques in myopia research.

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.

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

Integrated in silico and biological validation of the blocking effect of Cot-1 DNA on Microarray-CGH.

PubMed

Kang, Seung-Hui; Park, Chan Hee; Jeung, Hei Cheul; Kim, Ki-Yeol; Rha, Sun Young; Chung, Hyun Cheol

2007-06-01

In array-CGH, various factors may act as variables influencing the result of experiments. Among them, Cot-1 DNA, which has been used as a repetitive sequence-blocking agent, may become an artifact-inducing factor in BAC array-CGH. To identify the effect of Cot-1 DNA on Microarray-CGH experiments, Cot-1 DNA was labeled directly and Microarray-CGH experiments were performed. The results confirmed that probes which hybridized more completely with Cot-1 DNA had a higher sequence similarity to the Alu element. Further, in the sex-mismatched Microarray-CGH experiments, the variation and intensity in the fluorescent signal were reduced in the high intensity probe group in which probes were better hybridized with Cot-1 DNA. Otherwise, those of the low intensity probe group showed no alterations regardless of Cot-1 DNA. These results confirmed by in silico methods that Cot-1 DNA could block repetitive sequences in gDNA and probes. In addition, it was confirmed biologically that the blocking effect of Cot-1 DNA could be presented via its repetitive sequences, especially Alu elements. Thus, in contrast to BAC-array CGH, the use of Cot-1 DNA is advantageous in controlling experimental variation in Microarray-CGH.

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.

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.

CGI: Java Software for Mapping and Visualizing Data from Array-based Comparative Genomic Hybridization and Expression Profiling

PubMed Central

Gu, Joyce Xiuweu-Xu; Wei, Michael Yang; Rao, Pulivarthi H.; Lau, Ching C.; Behl, Sanjiv; Man, Tsz-Kwong

2007-01-01

With the increasing application of various genomic technologies in biomedical research, there is a need to integrate these data to correlate candidate genes/regions that are identified by different genomic platforms. Although there are tools that can analyze data from individual platforms, essential software for integration of genomic data is still lacking. Here, we present a novel Java-based program called CGI (Cytogenetics-Genomics Integrator) that matches the BAC clones from array-based comparative genomic hybridization (aCGH) to genes from RNA expression profiling datasets. The matching is computed via a fast, backend MySQL database containing UCSC Genome Browser annotations. This program also provides an easy-to-use graphical user interface for visualizing and summarizing the correlation of DNA copy number changes and RNA expression patterns from a set of experiments. In addition, CGI uses a Java applet to display the copy number values of a specific BAC clone in aCGH experiments side by side with the expression levels of genes that are mapped back to that BAC clone from the microarray experiments. The CGI program is built on top of extensible, reusable graphic components specifically designed for biologists. It is cross-platform compatible and the source code is freely available under the General Public License. PMID:19936083

CGI: Java software for mapping and visualizing data from array-based comparative genomic hybridization and expression profiling.

PubMed

Gu, Joyce Xiuweu-Xu; Wei, Michael Yang; Rao, Pulivarthi H; Lau, Ching C; Behl, Sanjiv; Man, Tsz-Kwong

2007-10-06

With the increasing application of various genomic technologies in biomedical research, there is a need to integrate these data to correlate candidate genes/regions that are identified by different genomic platforms. Although there are tools that can analyze data from individual platforms, essential software for integration of genomic data is still lacking. Here, we present a novel Java-based program called CGI (Cytogenetics-Genomics Integrator) that matches the BAC clones from array-based comparative genomic hybridization (aCGH) to genes from RNA expression profiling datasets. The matching is computed via a fast, backend MySQL database containing UCSC Genome Browser annotations. This program also provides an easy-to-use graphical user interface for visualizing and summarizing the correlation of DNA copy number changes and RNA expression patterns from a set of experiments. In addition, CGI uses a Java applet to display the copy number values of a specific BAC clone in aCGH experiments side by side with the expression levels of genes that are mapped back to that BAC clone from the microarray experiments. The CGI program is built on top of extensible, reusable graphic components specifically designed for biologists. It is cross-platform compatible and the source code is freely available under the General Public License.

High-density fiber-optic DNA random microsphere array.

PubMed

Ferguson, J A; Steemers, F J; Walt, D R

2000-11-15

A high-density fiber-optic DNA microarray sensor was developed to monitor multiple DNA sequences in parallel. Microarrays were prepared by randomly distributing DNA probe-functionalized 3.1-microm-diameter microspheres in an array of wells etched in a 500-microm-diameter optical imaging fiber. Registration of the microspheres was performed using an optical encoding scheme and a custom-built imaging system. Hybridization was visualized using fluorescent-labeled DNA targets with a detection limit of 10 fM. Hybridization times of seconds are required for nanomolar target concentrations, and analysis is performed in minutes.

Characterization and simulation of cDNA microarray spots using a novel mathematical model

PubMed Central

Kim, Hye Young; Lee, Seo Eun; Kim, Min Jung; Han, Jin Il; Kim, Bo Kyung; Lee, Yong Sung; Lee, Young Seek; Kim, Jin Hyuk

2007-01-01

Background The quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images. Results We developed a governing equation of cDNA deposition during evaporation of a drop in the microarray spotting process. The governing equation included four parameters: the surface site density on the support, the extrapolated equilibrium constant for the binding of cDNA molecules with surface sites on glass slides, the macromolecular interaction factor, and the volume constant of a drop of cDNA solution. We simulated cDNA deposition from the single model equation by varying the value of the parameters. The morphology of the resulting cDNA deposit can be classified into three types: a doughnut shape, a peak shape, and a volcano shape. The spot morphology can be changed into a flat shape by varying the experimental conditions while considering the parameters of the governing equation of cDNA deposition. The four parameters were estimated by fitting the governing equation to the real microarray images. With the results of the simulation and the parameter estimation, the phenomenon of the formation of cDNA deposits in each type was investigated. Conclusion This study explains how various spot shapes can exist and suggests which parameters are to be adjusted for obtaining a good spot. This system is able to explore the cDNA microarray spotting process in a predictable, manageable and descriptive manner. We hope it can provide a way to predict the incidents that can occur during a real cDNA microarray experiment, and produce useful data for several research applications involving cDNA microarrays. PMID:18096047

The MGED ontology: a framework for describing functional genomics experiments.

PubMed

Stoeckert, Christian J; Parkinson, Helen

2003-01-01

The Microarray Gene Expression Data (MGED) society was formed with an initial focus on experiments involving microarray technology. Despite the diversity of applications, there are common concepts used and a common need to capture experimental information in a standardized manner. In building the MGED ontology, it was recognized that it would be impractical to cover all the different types of experiments on all the different types of organisms by listing and defining all the types of organisms and their properties. Our solution was to create a framework for describing microarray experiments with an initial focus on the biological sample and its manipulation. For concepts that are common for many species, we could provide a manageable listing of controlled terms. For concepts that are species-specific or whose values cannot be readily listed, we created an 'OntologyEntry' concept that referenced an external resource. The MGED ontology is a work in progress that needs additional instances and particularly needs constraints to be added. The ontology currently covers the experimental sample and design, and we have begun capturing aspects of the microarrays themselves as well. The primary application of the ontology will be to develop forms for entering information into databases, and consequently allowing queries, taking advantage of the structure provided by the ontology. The application of an ontology of experimental conditions extends beyond microarray experiments and, as the scope of MGED includes other aspects of functional genomics, so too will the MGED ontology.

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

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.

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.

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

A method adapting microarray technology for signature tagged mutagenesis of Dusulfovibrio dusulfuricans G20 and Shewanella oneidensis MR-1 in anaerobic sediment survival experiments

USGS Publications Warehouse

Groh, Jennifer L.; Luo, Qingwei; Ballard , Jimmy D.; Krumholz, Lee R.

2005-01-01

Signature-tagged mutagenesis (STM) is a powerful technique that can be used to identify genes expressed by bacteria during exposure to conditions in their natural environments. To date, there have been no reports of studies in which this approach was used to study organisms of environmental, rather than pathogenic, significance. We used a mini-Tn10 transposon-bearing plasmid, pBSL180, that efficiently and randomly mutagenized Desulfovibrio desulfuricans G20 in addition to Shewanella oneidensis MR-1. Using these organisms as model sediment-dwelling anaerobic bacteria, we developed a new screening system, modified from former STM procedures, to identify genes that are critical for sediment survival. The screening system uses microarray technology to visualize tags from input and output pools, allowing us to identify those lost during sediment incubations. While the majority of data on survival genes identified will be presented in future papers, we report here on chemotaxis-related genes identified by our STM method in both bacteria in order to validate our method. This system may be applicable to the study of numerous environmental bacteria, allowing us to identify functions and roles of survival genes in various habitats.

RDFBuilder: a tool to automatically build RDF-based interfaces for MAGE-OM microarray data sources.

PubMed

Anguita, Alberto; Martin, Luis; Garcia-Remesal, Miguel; Maojo, Victor

2013-07-01

This paper presents RDFBuilder, a tool that enables RDF-based access to MAGE-ML-compliant microarray databases. We have developed a system that automatically transforms the MAGE-OM model and microarray data stored in the ArrayExpress database into RDF format. Additionally, the system automatically enables a SPARQL endpoint. This allows users to execute SPARQL queries for retrieving microarray data, either from specific experiments or from more than one experiment at a time. Our system optimizes response times by caching and reusing information from previous queries. In this paper, we describe our methods for achieving this transformation. We show that our approach is complementary to other existing initiatives, such as Bio2RDF, for accessing and retrieving data from the ArrayExpress database. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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%.

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

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

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

Approximate geodesic distances reveal biologically relevant structures in microarray data.

PubMed

Nilsson, Jens; Fioretos, Thoas; Höglund, Mattias; Fontes, Magnus

2004-04-12

Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance. We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.

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.

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.

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.

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.

Detection of changes in gene regulatory patterns, elicited by perturbations of the Hsp90 molecular chaperone complex, by visualizing multiple experiments with an animation

PubMed Central

2011-01-01

Background To make sense out of gene expression profiles, such analyses must be pushed beyond the mere listing of affected genes. For example, if a group of genes persistently display similar changes in expression levels under particular experimental conditions, and the proteins encoded by these genes interact and function in the same cellular compartments, this could be taken as very strong indicators for co-regulated protein complexes. One of the key requirements is having appropriate tools to detect such regulatory patterns. Results We have analyzed the global adaptations in gene expression patterns in the budding yeast when the Hsp90 molecular chaperone complex is perturbed either pharmacologically or genetically. We integrated these results with publicly accessible expression, protein-protein interaction and intracellular localization data. But most importantly, all experimental conditions were simultaneously and dynamically visualized with an animation. This critically facilitated the detection of patterns of gene expression changes that suggested underlying regulatory networks that a standard analysis by pairwise comparison and clustering could not have revealed. Conclusions The results of the animation-assisted detection of changes in gene regulatory patterns make predictions about the potential roles of Hsp90 and its co-chaperone p23 in regulating whole sets of genes. The simultaneous dynamic visualization of microarray experiments, represented in networks built by integrating one's own experimental with publicly accessible data, represents a powerful discovery tool that allows the generation of new interpretations and hypotheses. PMID:21672238

Functional comparison of microarray data across multiple platforms using the method of percentage of overlapping functions.

PubMed

Li, Zhiguang; Kwekel, Joshua C; Chen, Tao

2012-01-01

Functional comparison across microarray platforms is used to assess the comparability or similarity of the biological relevance associated with the gene expression data generated by multiple microarray platforms. Comparisons at the functional level are very important considering that the ultimate purpose of microarray technology is to determine the biological meaning behind the gene expression changes under a specific condition, not just to generate a list of genes. Herein, we present a method named percentage of overlapping functions (POF) and illustrate how it is used to perform the functional comparison of microarray data generated across multiple platforms. This method facilitates the determination of functional differences or similarities in microarray data generated from multiple array platforms across all the functions that are presented on these platforms. This method can also be used to compare the functional differences or similarities between experiments, projects, or laboratories.

BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.

PubMed

Angelini, Claudia; Cutillo, Luisa; De Canditiis, Daniela; Mutarelli, Margherita; Pensky, Marianna

2008-10-06

Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. The software package BATS (Bayesian Analysis of Time Series) presented here implements the methodology described above. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles when at least 5-6 time points are available. The package has a user-friendly interface. BATS successfully manages various technical difficulties which arise in time-course microarray experiments, such as a small number of observations, non-uniform sampling intervals and replicated or missing data. BATS is a free user-friendly software for the analysis of both simulated and real microarray time course experiments. The software, the user manual and a brief illustrative example are freely available online at the BATS website: http://www.na.iac.cnr.it/bats.

Selection of higher order regression models in the analysis of multi-factorial transcription data.

PubMed

Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim

2014-01-01

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.

A universal reference sample derived from clone vector for improved detection of differential gene expression

PubMed Central

Khan, Rishi L; Gonye, Gregory E; Gao, Guang; Schwaber, James S

2006-01-01

Background Using microarrays by co-hybridizing two samples labeled with different dyes enables differential gene expression measurements and comparisons across slides while controlling for within-slide variability. Typically one dye produces weaker signal intensities than the other often causing signals to be undetectable. In addition, undetectable spots represent a large problem for two-color microarray designs and most arrays contain at least 40% undetectable spots even when labeled with reference samples such as Stratagene's Universal Reference RNAs™. Results We introduce a novel universal reference sample that produces strong signal for all spots on the array, increasing the average fraction of detectable spots to 97%. Maximizing detectable spots on the reference image channel also decreases the variability of microarray data allowing for reliable detection of smaller differential gene expression changes. The reference sample is derived from sequence contained in the parental EST clone vector pT7T3D-Pac and is called vector RNA (vRNA). We show that vRNA can also be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This reference sample can be made inexpensively in large quantities as a renewable resource that is consistent across experiments. Conclusion Results of this study show that vRNA provides a useful universal reference that yields high signal for almost all spots on a microarray, reduces variation and allows for comparisons between experiments and laboratories. Further, it can be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This type of reference allows for detection of small changes in differential expression while reference designs in general allow for large-scale multivariate experimental designs. vRNA in combination with reference designs enable systems biology microarray experiments of small physiologically relevant changes. PMID:16677381

Equalizer reduces SNP bias in Affymetrix microarrays.

PubMed

Quigley, David

2015-07-30

Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray's performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans. By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings. The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.

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.

VirtualPlant: A Software Platform to Support Systems Biology Research1[W][OA

PubMed Central

Katari, Manpreet S.; Nowicki, Steve D.; Aceituno, Felipe F.; Nero, Damion; Kelfer, Jonathan; Thompson, Lee Parnell; Cabello, Juan M.; Davidson, Rebecca S.; Goldberg, Arthur P.; Shasha, Dennis E.; Coruzzi, Gloria M.; Gutiérrez, Rodrigo A.

2010-01-01

Data generation is no longer the limiting factor in advancing biological research. In addition, data integration, analysis, and interpretation have become key bottlenecks and challenges that biologists conducting genomic research face daily. To enable biologists to derive testable hypotheses from the increasing amount of genomic data, we have developed the VirtualPlant software platform. VirtualPlant enables scientists to visualize, integrate, and analyze genomic data from a systems biology perspective. VirtualPlant integrates genome-wide data concerning the known and predicted relationships among genes, proteins, and molecules, as well as genome-scale experimental measurements. VirtualPlant also provides visualization techniques that render multivariate information in visual formats that facilitate the extraction of biological concepts. Importantly, VirtualPlant helps biologists who are not trained in computer science to mine lists of genes, microarray experiments, and gene networks to address questions in plant biology, such as: What are the molecular mechanisms by which internal or external perturbations affect processes controlling growth and development? We illustrate the use of VirtualPlant with three case studies, ranging from querying a gene of interest to the identification of gene networks and regulatory hubs that control seed development. Whereas the VirtualPlant software was developed to mine Arabidopsis (Arabidopsis thaliana) genomic data, its data structures, algorithms, and visualization tools are designed in a species-independent way. VirtualPlant is freely available at www.virtualplant.org. PMID:20007449

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

Global gene expression profiling of oral cavity cancers suggests molecular heterogeneity within anatomic subsites

PubMed Central

Severino, Patricia; Alvares, Adriana M; Michaluart, Pedro; Okamoto, Oswaldo K; Nunes, Fabio D; Moreira-Filho, Carlos A; Tajara, Eloiza H

2008-01-01

Background Oral squamous cell carcinoma (OSCC) is a frequent neoplasm, which is usually aggressive and has unpredictable biological behavior and unfavorable prognosis. The comprehension of the molecular basis of this variability should lead to the development of targeted therapies as well as to improvements in specificity and sensitivity of diagnosis. Results Samples of primary OSCCs and their corresponding surgical margins were obtained from male patients during surgery and their gene expression profiles were screened using whole-genome microarray technology. Hierarchical clustering and Principal Components Analysis were used for data visualization and One-way Analysis of Variance was used to identify differentially expressed genes. Samples clustered mostly according to disease subsite, suggesting molecular heterogeneity within tumor stages. In order to corroborate our results, two publicly available datasets of microarray experiments were assessed. We found significant molecular differences between OSCC anatomic subsites concerning groups of genes presently or potentially important for drug development, including mRNA processing, cytoskeleton organization and biogenesis, metabolic process, cell cycle and apoptosis. Conclusion Our results corroborate literature data on molecular heterogeneity of OSCCs. Differences between disease subsites and among samples belonging to the same TNM class highlight the importance of gene expression-based classification and challenge the development of targeted therapies. PMID:19014556

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.

NCBI GEO: archive for functional genomics data sets--10 years on.

PubMed

Barrett, Tanya; Troup, Dennis B; Wilhite, Stephen E; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F; Tomashevsky, Maxim; Marshall, Kimberly A; Phillippy, Katherine H; Sherman, Patti M; Muertter, Rolf N; Holko, Michelle; Ayanbule, Oluwabukunmi; Yefanov, Andrey; Soboleva, Alexandra

2011-01-01

A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20,000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.

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.

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.

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

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.

An 80-year experience with optic nerve glioma cases at the Armed Forces Institute of Pathology: evolution from museum to molecular evaluation suggests possibe interventions in the cellular senescence and microglial pathways (an American Ophthalmological Society thesis).

PubMed

Cameron, J Douglas; Rodriguez, Fausto J; Rushing, Elisabeth; Horkayne-Szakaly, Iren; Eberhart, Charles

2014-01-01

To determine whether p16, a molecular marker of cellular senescence, and CD68, a microglial marker, are detectible in optic nerve glioma tissue stored for decades, thus providing potential targets for pharmacologic intervention. Cases were retrieved from the Armed Forces Institute of Pathology Registry of Ophthalmic Pathology. Clinical information was tabulated. In specimens with sufficient tissue, a tissue microarray was constructed to conduct molecular studies. Ninety-two cases were included: gender distribution was in a ratio of one male to 1.6 females, and age range was 2 months to 50 years (average age, 10.8 years). Neurofibromatosis type 1 was identified in 10 cases (10.8%). The majority presented with decreased vision and exophthalmos. Forty-eight cases were studied by a tissue microarray construction. Glial fibrillary acidic protein, a control for immunoreactivity, was positive in 46 cases (96%). Immunoreactivity for p16 protein was seen in 36 cases (75%) and CD68-positive cells in 34 (71%). Limitations include referral bias, limited clinical information, limited amount of tissue, and extended period of tissue preservation. Optic nerve glioma is a tumor of the visual axis in young individuals, which is generally indolent but with a variable clinical course. Traditional histopathologic techniques have not been reliably predictive of clinical course. This microarray contains tumors with representative demographic, clinical, and histologic characteristics for optic nerve glioma. Immunoreactivity for p16 protein and CD68 is positive in the majority. These findings suggest a possible explanation for the variable clinical course and identify therapeutic targets in the cell senescence and microglial pathways.

Host responses of Japanese flounder Paralichthys olivaceus with lymphocystis cell formation.

PubMed

Iwakiri, Shogo; Song, Jun-Young; Nakayama, Kei; Oh, Myung-Joo; Ishida, Minoru; Kitamura, Shin-Ichi

2014-06-01

Lymphocystis disease virus (LCDV) is the causative agent of lymphocystis disease (LCD). In this study, we investigated the mechanisms of lymphocystis cell (LCC) formation from the viewpoint of gene expression changes in the infected fish. LCC occurrence and virus titers in the experimentally infected Japanese flounder, Paralichthys olivaceus were monitored by visual confirmation and real-time PCR, respectively. The gene expression changes in the fish fin were investigated by microarray experiments. LCCs firstly appeared in the fish at 21 days post infection (dpi). LCD incidence increased with time and reached 92.9% at 62 dpi. LCDV genome was firstly detected from dorsal fins at 14 dpi, and the relative amount of the genome gradually-increased until 56 dpi. Since the occurrence of LCC was approximately synchronized with increasing of the virus genome, virus replication might play important roles for LCC formation. The microarray detected a few gene expression changes until 28 dpi. However, the number of expression changed genes dramatically increased between 28 and 42 dpi in which LCCs formation was active. From the microarray data analyses, apoptosis and cell division related genes were down-regulated, whereas cell fusion and collagen related genes were up-regulated at 42 dpi. Together with the observation of morphological changes of LCCs in previous reports, it is suggested that the following steps are involved in LCC formation: the virus infected cells were (1) inhibited apoptotic death and (2) cell division before enlargement, (3) hypertrophied by cell fusion, and (4) surrounded by a hyaline capsule associated with the alteration of collagen fibers. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

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…

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

PubMed Central

Zhu, Yuerong; Zhu, Yuelin; Xu, Wei

2008-01-01

Background Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand. Results EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data. Conclusion EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from . PMID:18218103

CAFE: an R package for the detection of gross chromosomal abnormalities from gene expression microarray data.

PubMed

Bollen, Sander; Leddin, Mathias; Andrade-Navarro, Miguel A; Mah, Nancy

2014-05-15

The current methods available to detect chromosomal abnormalities from DNA microarray expression data are cumbersome and inflexible. CAFE has been developed to alleviate these issues. It is implemented as an R package that analyzes Affymetrix *.CEL files and comes with flexible plotting functions, easing visualization of chromosomal abnormalities. CAFE is available from https://bitbucket.org/cob87icW6z/cafe/ as both source and compiled packages for Linux and Windows. It is released under the GPL version 3 license. CAFE will also be freely available from Bioconductor. sander.h.bollen@gmail.com or nancy.mah@mdc-berlin.de Supplementary data are available at Bioinformatics online.

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

Novel statistical framework to identify differentially expressed genes allowing transcriptomic background differences.

PubMed

Ling, Zhi-Qiang; Wang, Yi; Mukaisho, Kenichi; Hattori, Takanori; Tatsuta, Takeshi; Ge, Ming-Hua; Jin, Li; Mao, Wei-Min; Sugihara, Hiroyuki

2010-06-01

Tests of differentially expressed genes (DEGs) from microarray experiments are based on the null hypothesis that genes that are irrelevant to the phenotype/stimulus are expressed equally in the target and control samples. However, this strict hypothesis is not always true, as there can be several transcriptomic background differences between target and control samples, including different cell/tissue types, different cell cycle stages and different biological donors. These differences lead to increased false positives, which have little biological/medical significance. In this article, we propose a statistical framework to identify DEGs between target and control samples from expression microarray data allowing transcriptomic background differences between these samples by introducing a modified null hypothesis that the gene expression background difference is normally distributed. We use an iterative procedure to perform robust estimation of the null hypothesis and identify DEGs as outliers. We evaluated our method using our own triplicate microarray experiment, followed by validations with reverse transcription-polymerase chain reaction (RT-PCR) and on the MicroArray Quality Control dataset. The evaluations suggest that our technique (i) results in less false positive and false negative results, as measured by the degree of agreement with RT-PCR of the same samples, (ii) can be applied to different microarray platforms and results in better reproducibility as measured by the degree of DEG identification concordance both intra- and inter-platforms and (iii) can be applied efficiently with only a few microarray replicates. Based on these evaluations, we propose that this method not only identifies more reliable and biologically/medically significant DEG, but also reduces the power-cost tradeoff problem in the microarray field. Source code and binaries freely available for download at http://comonca.org.cn/fdca/resources/softwares/deg.zip.

Mining meiosis and gametogenesis with DNA microarrays.

PubMed

Schlecht, Ulrich; Primig, Michael

2003-04-01

Gametogenesis is a key developmental process that involves complex transcriptional regulation of numerous genes including many that are conserved between unicellular eukaryotes and mammals. Recent expression-profiling experiments using microarrays have provided insight into the co-ordinated transcription of several hundred genes during mitotic growth and meiotic development in budding and fission yeast. Furthermore, microarray-based studies have identified numerous loci that are regulated during the cell cycle or expressed in a germ-cell specific manner in eukaryotic model systems like Caenorhabditis elegans, Mus musculus as well as Homo sapiens. The unprecedented amount of information produced by post-genome biology has spawned novel approaches to organizing biological knowledge using currently available information technology. This review outlines experiments that contribute to an emerging comprehensive picture of the molecular machinery governing sexual reproduction in eukaryotes.

Whole CMV Proteome Pattern Recognition Analysis after HSCT Identifies Unique Epitope Targets Associated with the CMV Status

PubMed Central

Pérez-Bercoff, Lena; Valentini, Davide; Gaseitsiwe, Simani; Mahdavifar, Shahnaz; Schutkowski, Mike; Poiret, Thomas; Pérez-Bercoff, Åsa; Ljungman, Per; Maeurer, Markus J.

2014-01-01

Cytomegalovirus (CMV) infection represents a vital complication after Hematopoietic Stem Cell Transplantation (HSCT). We screened the entire CMV proteome to visualize the humoral target epitope-focus profile in serum after HSCT. IgG profiling from four patient groups (donor and/or recipient +/− for CMV) was performed at 6, 12 and 24 months after HSCT using microarray slides containing 17174 of 15mer-peptides overlapping by 4 aa covering 214 proteins from CMV. Data were analyzed using maSigPro, PAM and the ‘exclusive recognition analysis (ERA)’ to identify unique CMV epitope responses for each patient group. The ‘exclusive recognition analysis’ of serum epitope patterns segregated best 12 months after HSCT for the D+/R+ group (versus D−/R−). Epitopes were derived from UL123 (IE1), UL99 (pp28), UL32 (pp150), this changed at 24 months to 2 strongly recognized peptides provided from UL123 and UL100. Strongly (IgG) recognized CMV targets elicited also robust cytokine production in T-cells from patients after HSCT defined by intracellular cytokine staining (IL-2, TNF, IFN and IL-17). High-content peptide microarrays allow epitope profiling of entire viral proteomes; this approach can be useful to map relevant targets for diagnostics and therapy in patients with well defined clinical endpoints. Peptide microarray analysis visualizes the breadth of B-cell immune reconstitution after HSCT and provides a useful tool to gauge immune reconstitution. PMID:24740411

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.

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.

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.

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

Gentry, T.; Schadt, C.; Zhou, J.

Microarray technology has the unparalleled potential tosimultaneously determine the dynamics and/or activities of most, if notall, of the microbial populations in complex environments such as soilsand sediments. Researchers have developed several types of arrays thatcharacterize the microbial populations in these samples based on theirphylogenetic relatedness or functional genomic content. Several recentstudies have used these microarrays to investigate ecological issues;however, most have only analyzed a limited number of samples withrelatively few experiments utilizing the full high-throughput potentialof microarray analysis. This is due in part to the unique analyticalchallenges that these samples present with regard to sensitivity,specificity, quantitation, and data analysis. Thismore » review discussesspecific applications of microarrays to microbial ecology research alongwith some of the latest studies addressing the difficulties encounteredduring analysis of complex microbial communities within environmentalsamples. With continued development, microarray technology may ultimatelyachieve its potential for comprehensive, high-throughput characterizationof microbial populations in near real-time.« less

Evaluating concentration estimation errors in ELISA microarray experiments

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

Daly, Don S.; White, Amanda M.; Varnum, Susan M.

Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Althoughmore » propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.« less

maxdLoad2 and maxdBrowse: standards-compliant tools for microarray experimental annotation, data management and dissemination.

PubMed

Hancock, David; Wilson, Michael; Velarde, Giles; Morrison, Norman; Hayes, Andrew; Hulme, Helen; Wood, A Joseph; Nashar, Karim; Kell, Douglas B; Brass, Andy

2005-11-03

maxdLoad2 is a relational database schema and Java application for microarray experimental annotation and storage. It is compliant with all standards for microarray meta-data capture; including the specification of what data should be recorded, extensive use of standard ontologies and support for data exchange formats. The output from maxdLoad2 is of a form acceptable for submission to the ArrayExpress microarray repository at the European Bioinformatics Institute. maxdBrowse is a PHP web-application that makes contents of maxdLoad2 databases accessible via web-browser, the command-line and web-service environments. It thus acts as both a dissemination and data-mining tool. maxdLoad2 presents an easy-to-use interface to an underlying relational database and provides a full complement of facilities for browsing, searching and editing. There is a tree-based visualization of data connectivity and the ability to explore the links between any pair of data elements, irrespective of how many intermediate links lie between them. Its principle novel features are: the flexibility of the meta-data that can be captured, the tools provided for importing data from spreadsheets and other tabular representations, the tools provided for the automatic creation of structured documents, the ability to browse and access the data via web and web-services interfaces. Within maxdLoad2 it is very straightforward to customise the meta-data that is being captured or change the definitions of the meta-data. These meta-data definitions are stored within the database itself allowing client software to connect properly to a modified database without having to be specially configured. The meta-data definitions (configuration file) can also be centralized allowing changes made in response to revisions of standards or terminologies to be propagated to clients without user intervention.maxdBrowse is hosted on a web-server and presents multiple interfaces to the contents of maxd databases. maxdBrowse emulates many of the browse and search features available in the maxdLoad2 application via a web-browser. This allows users who are not familiar with maxdLoad2 to browse and export microarray data from the database for their own analysis. The same browse and search features are also available via command-line and SOAP server interfaces. This both enables scripting of data export for use embedded in data repositories and analysis environments, and allows access to the maxd databases via web-service architectures. maxdLoad2 http://www.bioinf.man.ac.uk/microarray/maxd/ and maxdBrowse http://dbk.ch.umist.ac.uk/maxdBrowse are portable and compatible with all common operating systems and major database servers. They provide a powerful, flexible package for annotation of microarray experiments and a convenient dissemination environment. They are available for download and open sourced under the Artistic License.

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.

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/.

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.

NCBI GEO: archive for functional genomics data sets—10 years on

PubMed Central

Barrett, Tanya; Troup, Dennis B.; Wilhite, Stephen E.; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F.; Tomashevsky, Maxim; Marshall, Kimberly A.; Phillippy, Katherine H.; Sherman, Patti M.; Muertter, Rolf N.; Holko, Michelle; Ayanbule, Oluwabukunmi; Yefanov, Andrey; Soboleva, Alexandra

2011-01-01

A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20 000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/. PMID:21097893

Plant-pathogen interactions: what microarray tells about it?

PubMed

Lodha, T D; Basak, J

2012-01-01

Plant defense responses are mediated by elementary regulatory proteins that affect expression of thousands of genes. Over the last decade, microarray technology has played a key role in deciphering the underlying networks of gene regulation in plants that lead to a wide variety of defence responses. Microarray is an important tool to quantify and profile the expression of thousands of genes simultaneously, with two main aims: (1) gene discovery and (2) global expression profiling. Several microarray technologies are currently in use; most include a glass slide platform with spotted cDNA or oligonucleotides. Till date, microarray technology has been used in the identification of regulatory genes, end-point defence genes, to understand the signal transduction processes underlying disease resistance and its intimate links to other physiological pathways. Microarray technology can be used for in-depth, simultaneous profiling of host/pathogen genes as the disease progresses from infection to resistance/susceptibility at different developmental stages of the host, which can be done in different environments, for clearer understanding of the processes involved. A thorough knowledge of plant disease resistance using successful combination of microarray and other high throughput techniques, as well as biochemical, genetic, and cell biological experiments is needed for practical application to secure and stabilize yield of many crop plants. This review starts with a brief introduction to microarray technology, followed by the basics of plant-pathogen interaction, the use of DNA microarrays over the last decade to unravel the mysteries of plant-pathogen interaction, and ends with the future prospects of this technology.

An Inexpensive Gel Electrophoresis-Based Polymerase Chain Reaction Method for Quantifying mRNA Levels

ERIC Educational Resources Information Center

Bradford, William D.; Cahoon, Laty; Freel, Sara R.; Hoopes, Laura L. Mays; Eckdahl, Todd T.

2005-01-01

In order to engage their students in a core methodology of the new genomics era, an everincreasing number of faculty at primarily undergraduate institutions are gaining access to microarray technology. Their students are conducting successful microarray experiments designed to address a variety of interesting questions. A next step in these…

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.

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

An object model and database for functional genomics.

PubMed

Jones, Andrew; Hunt, Ela; Wastling, Jonathan M; Pizarro, Angel; Stoeckert, Christian J

2004-07-10

Large-scale functional genomics analysis is now feasible and presents significant challenges in data analysis, storage and querying. Data standards are required to enable the development of public data repositories and to improve data sharing. There is an established data format for microarrays (microarray gene expression markup language, MAGE-ML) and a draft standard for proteomics (PEDRo). We believe that all types of functional genomics experiments should be annotated in a consistent manner, and we hope to open up new ways of comparing multiple datasets used in functional genomics. We have created a functional genomics experiment object model (FGE-OM), developed from the microarray model, MAGE-OM and two models for proteomics, PEDRo and our own model (Gla-PSI-Glasgow Proposal for the Proteomics Standards Initiative). FGE-OM comprises three namespaces representing (i) the parts of the model common to all functional genomics experiments; (ii) microarray-specific components; and (iii) proteomics-specific components. We believe that FGE-OM should initiate discussion about the contents and structure of the next version of MAGE and the future of proteomics standards. A prototype database called RNA And Protein Abundance Database (RAPAD), based on FGE-OM, has been implemented and populated with data from microbial pathogenesis. FGE-OM and the RAPAD schema are available from http://www.gusdb.org/fge.html, along with a set of more detailed diagrams. RAPAD can be accessed by registration at the site.

Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE).

PubMed

Deutsch, Eric W; Ball, Catherine A; Berman, Jules J; Bova, G Steven; Brazma, Alvis; Bumgarner, Roger E; Campbell, David; Causton, Helen C; Christiansen, Jeffrey H; Daian, Fabrice; Dauga, Delphine; Davidson, Duncan R; Gimenez, Gregory; Goo, Young Ah; Grimmond, Sean; Henrich, Thorsten; Herrmann, Bernhard G; Johnson, Michael H; Korb, Martin; Mills, Jason C; Oudes, Asa J; Parkinson, Helen E; Pascal, Laura E; Pollet, Nicolas; Quackenbush, John; Ramialison, Mirana; Ringwald, Martin; Salgado, David; Sansone, Susanna-Assunta; Sherlock, Gavin; Stoeckert, Christian J; Swedlow, Jason; Taylor, Ronald C; Walashek, Laura; Warford, Anthony; Wilkinson, David G; Zhou, Yi; Zon, Leonard I; Liu, Alvin Y; True, Lawrence D

2008-03-01

One purpose of the biomedical literature is to report results in sufficient detail that the methods of data collection and analysis can be independently replicated and verified. Here we present reporting guidelines for gene expression localization experiments: the minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). MISFISHIE is modeled after the Minimum Information About a Microarray Experiment (MIAME) specification for microarray experiments. Both guidelines define what information should be reported without dictating a format for encoding that information. MISFISHIE describes six types of information to be provided for each experiment: experimental design, biomaterials and treatments, reporters, staining, imaging data and image characterizations. This specification has benefited the consortium within which it was developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal.

A python module to normalize microarray data by the quantile adjustment method.

PubMed

Baber, Ibrahima; Tamby, Jean Philippe; Manoukis, Nicholas C; Sangaré, Djibril; Doumbia, Seydou; Traoré, Sekou F; Maiga, Mohamed S; Dembélé, Doulaye

2011-06-01

Microarray technology is widely used for gene expression research targeting the development of new drug treatments. In the case of a two-color microarray, the process starts with labeling DNA samples with fluorescent markers (cyanine 635 or Cy5 and cyanine 532 or Cy3), then mixing and hybridizing them on a chemically treated glass printed with probes, or fragments of genes. The level of hybridization between a strand of labeled DNA and a probe present on the array is measured by scanning the fluorescence of spots in order to quantify the expression based on the quality and number of pixels for each spot. The intensity data generated from these scans are subject to errors due to differences in fluorescence efficiency between Cy5 and Cy3, as well as variation in human handling and quality of the sample. Consequently, data have to be normalized to correct for variations which are not related to the biological phenomena under investigation. Among many existing normalization procedures, we have implemented the quantile adjustment method using the python computer language, and produced a module which can be run via an HTML dynamic form. This module is composed of different functions for data files reading, intensity and ratio computations and visualization. The current version of the HTML form allows the user to visualize the data before and after normalization. It also gives the option to subtract background noise before normalizing the data. The output results of this module are in agreement with the results of other normalization tools. Published by Elsevier B.V.

Stochastic models for inferring genetic regulation from microarray gene expression data.

PubMed

Tian, Tianhai

2010-03-01

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information. 2009 Elsevier Ireland Ltd. All rights reserved.

Emerging Use of Gene Expression Microarrays in Plant Physiology

DOE PAGES

Wullschleger, Stan D.; Difazio, Stephen P.

2003-01-01

Microarrays have become an important technology for the global analysis of gene expression in humans, animals, plants, and microbes. Implemented in the context of a well-designed experiment, cDNA and oligonucleotide arrays can provide highthroughput, simultaneous analysis of transcript abundance for hundreds, if not thousands, of genes. However, despite widespread acceptance, the use of microarrays as a tool to better understand processes of interest to the plant physiologist is still being explored. To help illustrate current uses of microarrays in the plant sciences, several case studies that we believe demonstrate the emerging application of gene expression arrays in plant physiology weremore » selected from among the many posters and presentations at the 2003 Plant and Animal Genome XI Conference. Based on this survey, microarrays are being used to assess gene expression in plants exposed to the experimental manipulation of air temperature, soil water content and aluminium concentration in the root zone. Analysis often includes characterizing transcript profiles for multiple post-treatment sampling periods and categorizing genes with common patterns of response using hierarchical clustering techniques. In addition, microarrays are also providing insights into developmental changes in gene expression associated with fibre and root elongation in cotton and maize, respectively. Technical and analytical limitations of microarrays are discussed and projects attempting to advance areas of microarray design and data analysis are highlighted. Finally, although much work remains, we conclude that microarrays are a valuable tool for the plant physiologist interested in the characterization and identification of individual genes and gene families with potential application in the fields of agriculture, horticulture and forestry.« less

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

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

Development of a rapid microarray-based DNA subtyping assay for the alleles of Shiga toxins 1 and 2 of Escherichia coli.

PubMed

Geue, Lutz; Stieber, Bettina; Monecke, Stefan; Engelmann, Ines; Gunzer, Florian; Slickers, Peter; Braun, Sascha D; Ehricht, Ralf

2014-08-01

In this study, we developed a new rapid, economic, and automated microarray-based genotyping test for the standardized subtyping of Shiga toxins 1 and 2 of Escherichia coli. The microarrays from Alere Technologies can be used in two different formats, the ArrayTube and the ArrayStrip (which enables high-throughput testing in a 96-well format). One microarray chip harbors all the gene sequences necessary to distinguish between all Stx subtypes, facilitating the identification of single and multiple subtypes within a single isolate in one experiment. Specific software was developed to automatically analyze all data obtained from the microarray. The assay was validated with 21 Shiga toxin-producing E. coli (STEC) reference strains that were previously tested by the complete set of conventional subtyping PCRs. The microarray results showed 100% concordance with the PCR results. Essentially identical results were detected when the standard DNA extraction method was replaced by a time-saving heat lysis protocol. For further validation of the microarray, we identified the Stx subtypes or combinations of the subtypes in 446 STEC field isolates of human and animal origin. In summary, this oligonucleotide array represents an excellent diagnostic tool that provides some advantages over standard PCR-based subtyping. The number of the spotted probes on the microarrays can be increased by additional probes, such as for novel alleles, species markers, or resistance genes, should the need arise. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Estimation of gene induction enables a relevance-based ranking of gene sets.

PubMed

Bartholomé, Kilian; Kreutz, Clemens; Timmer, Jens

2009-07-01

In order to handle and interpret the vast amounts of data produced by microarray experiments, the analysis of sets of genes with a common biological functionality has been shown to be advantageous compared to single gene analyses. Some statistical methods have been proposed to analyse the differential gene expression of gene sets in microarray experiments. However, most of these methods either require threshhold values to be chosen for the analysis, or they need some reference set for the determination of significance. We present a method that estimates the number of differentially expressed genes in a gene set without requiring a threshold value for significance of genes. The method is self-contained (i.e., it does not require a reference set for comparison). In contrast to other methods which are focused on significance, our approach emphasizes the relevance of the regulation of gene sets. The presented method measures the degree of regulation of a gene set and is a useful tool to compare the induction of different gene sets and place the results of microarray experiments into the biological context. An R-package is available.

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.

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

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

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

PubMed Central

Hurley, Daniel; Araki, Hiromitsu; Tamada, Yoshinori; Dunmore, Ben; Sanders, Deborah; Humphreys, Sally; Affara, Muna; Imoto, Seiya; Yasuda, Kaori; Tomiyasu, Yuki; Tashiro, Kosuke; Savoie, Christopher; Cho, Vicky; Smith, Stephen; Kuhara, Satoru; Miyano, Satoru; Charnock-Jones, D. Stephen; Crampin, Edmund J.; Print, Cristin G.

2012-01-01

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions. PMID:22121215

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.

GenomeCAT: a versatile tool for the analysis and integrative visualization of DNA copy number variants.

PubMed

Tebel, Katrin; Boldt, Vivien; Steininger, Anne; Port, Matthias; Ebert, Grit; Ullmann, Reinhard

2017-01-06

The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS platforms is complicated by the considerable variability of the human genome. Therefore, tools for multidimensional data analysis and comparison of patient cohorts are needed to assist in the discrimination of clinically relevant CNVs from others. We developed GenomeCAT, a standalone Java application for the analysis and integrative visualization of CNVs. GenomeCAT is composed of three modules dedicated to the inspection of single cases, comparative analysis of multidimensional data and group comparisons aiming at the identification of recurrent aberrations in patients sharing the same phenotype, respectively. Its flexible import options ease the comparative analysis of own results derived from microarray or NGS platforms with data from literature or public depositories. Multidimensional data obtained from different experiment types can be merged into a common data matrix to enable common visualization and analysis. All results are stored in the integrated MySQL database, but can also be exported as tab delimited files for further statistical calculations in external programs. GenomeCAT offers a broad spectrum of visualization and analysis tools that assist in the evaluation of CNVs in the context of other experiment data and annotations. The use of GenomeCAT does not require any specialized computer skills. The various R packages implemented for data analysis are fully integrated into GenomeCATs graphical user interface and the installation process is supported by a wizard. The flexibility in terms of data import and export in combination with the ability to create a common data matrix makes the program also well suited as an interface between genomic data from heterogeneous sources and external software tools. Due to the modular architecture the functionality of GenomeCAT can be easily extended by further R packages or customized plug-ins to meet future requirements.

Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)

PubMed Central

Deutsch, Eric W; Ball, Catherine A; Berman, Jules J; Bova, G Steven; Brazma, Alvis; Bumgarner, Roger E; Campbell, David; Causton, Helen C; Christiansen, Jeffrey H; Daian, Fabrice; Dauga, Delphine; Davidson, Duncan R; Gimenez, Gregory; Goo, Young Ah; Grimmond, Sean; Henrich, Thorsten; Herrmann, Bernhard G; Johnson, Michael H; Korb, Martin; Mills, Jason C; Oudes, Asa J; Parkinson, Helen E; Pascal, Laura E; Pollet, Nicolas; Quackenbush, John; Ramialison, Mirana; Ringwald, Martin; Salgado, David; Sansone, Susanna-Assunta; Sherlock, Gavin; Stoeckert, Christian J; Swedlow, Jason; Taylor, Ronald C; Walashek, Laura; Warford, Anthony; Wilkinson, David G; Zhou, Yi; Zon, Leonard I; Liu, Alvin Y; True, Lawrence D

2015-01-01

One purpose of the biomedical literature is to report results in sufficient detail so that the methods of data collection and analysis can be independently replicated and verified. Here we present for consideration a minimum information specification for gene expression localization experiments, called the “Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)”. It is modelled after the MIAME (Minimum Information About a Microarray Experiment) specification for microarray experiments. Data specifications like MIAME and MISFISHIE specify the information content without dictating a format for encoding that information. The MISFISHIE specification describes six types of information that should be provided for each experiment: Experimental Design, Biomaterials and Treatments, Reporters, Staining, Imaging Data, and Image Characterizations. This specification has benefited the consortium within which it was initially developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal. PMID:18327244

ArrayExpress update--trends in database growth and links to data analysis tools.

PubMed

Rustici, Gabriella; Kolesnikov, Nikolay; Brandizi, Marco; Burdett, Tony; Dylag, Miroslaw; Emam, Ibrahim; Farne, Anna; Hastings, Emma; Ison, Jon; Keays, Maria; Kurbatova, Natalja; Malone, James; Mani, Roby; Mupo, Annalisa; Pedro Pereira, Rui; Pilicheva, Ekaterina; Rung, Johan; Sharma, Anjan; Tang, Y Amy; Ternent, Tobias; Tikhonov, Andrew; Welter, Danielle; Williams, Eleanor; Brazma, Alvis; Parkinson, Helen; Sarkans, Ugis

2013-01-01

The ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress) is one of three international functional genomics public data repositories, alongside the Gene Expression Omnibus at NCBI and the DDBJ Omics Archive, supporting peer-reviewed publications. It accepts data generated by sequencing or array-based technologies and currently contains data from almost a million assays, from over 30 000 experiments. The proportion of sequencing-based submissions has grown significantly over the last 2 years and has reached, in 2012, 15% of all new data. All data are available from ArrayExpress in MAGE-TAB format, which allows robust linking to data analysis and visualization tools, including Bioconductor and GenomeSpace. Additionally, R objects, for microarray data, and binary alignment format files, for sequencing data, have been generated for a significant proportion of ArrayExpress data.

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

PubMed Central

Zhang, Bing; Schmoyer, Denise; Kirov, Stefan; Snoddy, Jay

2004-01-01

Background Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets. Results We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at . Conclusion GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets. PMID:14975175

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

PubMed

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Designing microarray and RNA-Seq experiments for greater systems biology discovery in modern plant genomics.

PubMed

Yang, Chuanping; Wei, Hairong

2015-02-01

Microarray and RNA-seq experiments have become an important part of modern genomics and systems biology. Obtaining meaningful biological data from these experiments is an arduous task that demands close attention to many details. Negligence at any step can lead to gene expression data containing inadequate or composite information that is recalcitrant for pattern extraction. Therefore, it is imperative to carefully consider experimental design before launching a time-consuming and costly experiment. Contemporarily, most genomics experiments have two objectives: (1) to generate two or more groups of comparable data for identifying differentially expressed genes, gene families, biological processes, or metabolic pathways under experimental conditions; (2) to build local gene regulatory networks and identify hierarchically important regulators governing biological processes and pathways of interest. Since the first objective aims to identify the active molecular identities and the second provides a basis for understanding the underlying molecular mechanisms through inferring causality relationships mediated by treatment, an optimal experiment is to produce biologically relevant and extractable data to meet both objectives without substantially increasing the cost. This review discusses the major issues that researchers commonly face when embarking on microarray or RNA-seq experiments and summarizes important aspects of experimental design, which aim to help researchers deliberate how to generate gene expression profiles with low background noise but with more interaction to facilitate novel biological discoveries in modern plant genomics. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

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

DNA microarrays: a powerful genomic tool for biomedical and clinical research

PubMed Central

Trevino, Victor; Falciani, Francesco; Barrera-Saldaña, Hugo A.

2007-01-01

Among the many benefits of the Human Genome Project are new and powerful tools such as the genome-wide hybridization devices referred as microarrays. Initially designed to measure gene transcriptional levels, microarray technologies are now used for comparing other genome features among individuals and their tissues and cells. Results provide valuable information on disease subcategories, disease prognosis, and treatment outcome. Likewise, reveal differences in genetic makeup, regulatory mechanisms and subtle variations are approaching the era of personalized medicine. To understand this powerful tool, its versatility and how it is dramatically changing the molecular approach to biomedical and clinical research, this review describes the technology, its applications, a didactic step-by-step review of a typical microarray protocol, and a real experiment. Finally, it calls the attention of the medical community to integrate multidisciplinary teams, to take advantage of this technology and its expanding applications that in a slide reveals our genetic inheritance and destiny. PMID:17660860

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

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.

ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping.

PubMed

Hansen, Christian Skjødt; Østerbye, Thomas; Marcatili, Paolo; Lund, Ole; Buus, Søren; Nielsen, Morten

2017-01-01

Identification of epitopes targeted by antibodies (B cell epitopes) is of critical importance for the development of many diagnostic and therapeutic tools. For clinical usage, such epitopes must be extensively characterized in order to validate specificity and to document potential cross-reactivity. B cell epitopes are typically classified as either linear epitopes, i.e. short consecutive segments from the protein sequence or conformational epitopes adapted through native protein folding. Recent advances in high-density peptide microarrays enable high-throughput, high-resolution identification and characterization of linear B cell epitopes. Using exhaustive amino acid substitution analysis of peptides originating from target antigens, these microarrays can be used to address the specificity of polyclonal antibodies raised against such antigens containing hundreds of epitopes. However, the interpretation of the data provided in such large-scale screenings is far from trivial and in most cases it requires advanced computational and statistical skills. Here, we present an online application for automated identification of linear B cell epitopes, allowing the non-expert user to analyse peptide microarray data. The application takes as input quantitative peptide data of fully or partially substituted overlapping peptides from a given antigen sequence and identifies epitope residues (residues that are significantly affected by substitutions) and visualize the selectivity towards each residue by sequence logo plots. Demonstrating utility, the application was used to identify and address the antibody specificity of 18 linear epitope regions in Human Serum Albumin (HSA), using peptide microarray data consisting of fully substituted peptides spanning the entire sequence of HSA and incubated with polyclonal rabbit anti-HSA (and mouse anti-rabbit-Cy3). The application is made available at: www.cbs.dtu.dk/services/ArrayPitope.

ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping

PubMed Central

Hansen, Christian Skjødt; Østerbye, Thomas; Marcatili, Paolo; Lund, Ole; Buus, Søren

2017-01-01

Identification of epitopes targeted by antibodies (B cell epitopes) is of critical importance for the development of many diagnostic and therapeutic tools. For clinical usage, such epitopes must be extensively characterized in order to validate specificity and to document potential cross-reactivity. B cell epitopes are typically classified as either linear epitopes, i.e. short consecutive segments from the protein sequence or conformational epitopes adapted through native protein folding. Recent advances in high-density peptide microarrays enable high-throughput, high-resolution identification and characterization of linear B cell epitopes. Using exhaustive amino acid substitution analysis of peptides originating from target antigens, these microarrays can be used to address the specificity of polyclonal antibodies raised against such antigens containing hundreds of epitopes. However, the interpretation of the data provided in such large-scale screenings is far from trivial and in most cases it requires advanced computational and statistical skills. Here, we present an online application for automated identification of linear B cell epitopes, allowing the non-expert user to analyse peptide microarray data. The application takes as input quantitative peptide data of fully or partially substituted overlapping peptides from a given antigen sequence and identifies epitope residues (residues that are significantly affected by substitutions) and visualize the selectivity towards each residue by sequence logo plots. Demonstrating utility, the application was used to identify and address the antibody specificity of 18 linear epitope regions in Human Serum Albumin (HSA), using peptide microarray data consisting of fully substituted peptides spanning the entire sequence of HSA and incubated with polyclonal rabbit anti-HSA (and mouse anti-rabbit-Cy3). The application is made available at: www.cbs.dtu.dk/services/ArrayPitope. PMID:28095436

Parents' Experience with Pediatric Microarray: Transferrable Lessons in the Era of Genomic Counseling.

PubMed

Hayeems, R Z; Babul-Hirji, R; Hoang, N; Weksberg, R; Shuman, C

2016-04-01

Advances in genome-based microarray and sequencing technologies hold tremendous promise for understanding, better-managing and/or preventing disease and disease-related risk. Chromosome microarray technology (array based comparative genomic hybridization [aCGH]) is widely utilized in pediatric care to inform diagnostic etiology and medical management. Less clear is how parents experience and perceive the value of this technology. This study explored parents' experiences with aCGH in the pediatric setting, focusing on how they make meaning of various types of test results. We conducted in-person or telephone-based semi-structured interviews with parents of 21 children who underwent aCGH testing in 2010. Transcripts were coded and analyzed thematically according to the principles of interpretive description. We learned that parents expect genomic tests to be of personal use; their experiences with aCGH results characterize this use as intrinsic in the test's ability to provide a much sought-after answer for their child's condition, and instrumental in its ability to guide care, access to services, and family planning. In addition, parents experience uncertainty regardless of whether aCGH results are of pathogenic, uncertain, or benign significance; this triggers frustration, fear, and hope. Findings reported herein better characterize the notion of personal utility and highlight the pervasive nature of uncertainty in the context of genomic testing. Empiric research that links pre-test counseling content and psychosocial outcomes is warranted to optimize patient care.

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.

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

CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

PubMed Central

Baumbach, Jan

2007-01-01

Background Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Results Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB. Conclusion The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at . PMID:17986320

Development and validation of a flax (Linum usitatissimum L.) gene expression oligo microarray

PubMed Central

2010-01-01

Background Flax (Linum usitatissimum L.) has been cultivated for around 9,000 years and is therefore one of the oldest cultivated species. Today, flax is still grown for its oil (oil-flax or linseed cultivars) and its cellulose-rich fibres (fibre-flax cultivars) used for high-value linen garments and composite materials. Despite the wide industrial use of flax-derived products, and our actual understanding of the regulation of both wood fibre production and oil biosynthesis more information must be acquired in both domains. Recent advances in genomics are now providing opportunities to improve our fundamental knowledge of these complex processes. In this paper we report the development and validation of a high-density oligo microarray platform dedicated to gene expression analyses in flax. Results Nine different RNA samples obtained from flax inner- and outer-stems, seeds, leaves and roots were used to generate a collection of 1,066,481 ESTs by massive parallel pyrosequencing. Sequences were assembled into 59,626 unigenes and 48,021 sequences were selected for oligo design and high-density microarray (Nimblegen 385K) fabrication with eight, non-overlapping 25-mers oligos per unigene. 18 independent experiments were used to evaluate the hybridization quality, precision, specificity and accuracy and all results confirmed the high technical quality of our microarray platform. Cross-validation of microarray data was carried out using quantitative qRT-PCR. Nine target genes were selected on the basis of microarray results and reflected the whole range of fold change (both up-regulated and down-regulated genes in different samples). A statistically significant positive correlation was obtained comparing expression levels for each target gene across all biological replicates both in qRT-PCR and microarray results. Further experiments illustrated the capacity of our arrays to detect differential gene expression in a variety of flax tissues as well as between two contrasted flax varieties. Conclusion All results suggest that our high-density flax oligo-microarray platform can be used as a very sensitive tool for analyzing gene expression in a large variety of tissues as well as in different cultivars. Moreover, this highly reliable platform can also be used for the quantification of mRNA transcriptional profiling in different flax tissues. PMID:20964859

Development and validation of a flax (Linum usitatissimum L.) gene expression oligo microarray.

PubMed

Fenart, Stéphane; Ndong, Yves-Placide Assoumou; Duarte, Jorge; Rivière, Nathalie; Wilmer, Jeroen; van Wuytswinkel, Olivier; Lucau, Anca; Cariou, Emmanuelle; Neutelings, Godfrey; Gutierrez, Laurent; Chabbert, Brigitte; Guillot, Xavier; Tavernier, Reynald; Hawkins, Simon; Thomasset, Brigitte

2010-10-21

Flax (Linum usitatissimum L.) has been cultivated for around 9,000 years and is therefore one of the oldest cultivated species. Today, flax is still grown for its oil (oil-flax or linseed cultivars) and its cellulose-rich fibres (fibre-flax cultivars) used for high-value linen garments and composite materials. Despite the wide industrial use of flax-derived products, and our actual understanding of the regulation of both wood fibre production and oil biosynthesis more information must be acquired in both domains. Recent advances in genomics are now providing opportunities to improve our fundamental knowledge of these complex processes. In this paper we report the development and validation of a high-density oligo microarray platform dedicated to gene expression analyses in flax. Nine different RNA samples obtained from flax inner- and outer-stems, seeds, leaves and roots were used to generate a collection of 1,066,481 ESTs by massive parallel pyrosequencing. Sequences were assembled into 59,626 unigenes and 48,021 sequences were selected for oligo design and high-density microarray (Nimblegen 385K) fabrication with eight, non-overlapping 25-mers oligos per unigene. 18 independent experiments were used to evaluate the hybridization quality, precision, specificity and accuracy and all results confirmed the high technical quality of our microarray platform. Cross-validation of microarray data was carried out using quantitative qRT-PCR. Nine target genes were selected on the basis of microarray results and reflected the whole range of fold change (both up-regulated and down-regulated genes in different samples). A statistically significant positive correlation was obtained comparing expression levels for each target gene across all biological replicates both in qRT-PCR and microarray results. Further experiments illustrated the capacity of our arrays to detect differential gene expression in a variety of flax tissues as well as between two contrasted flax varieties. All results suggest that our high-density flax oligo-microarray platform can be used as a very sensitive tool for analyzing gene expression in a large variety of tissues as well as in different cultivars. Moreover, this highly reliable platform can also be used for the quantification of mRNA transcriptional profiling in different flax tissues.

Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants.

PubMed

Kudo, Toru; Sasaki, Yohei; Terashima, Shin; Matsuda-Imai, Noriko; Takano, Tomoyuki; Saito, Misa; Kanno, Maasa; Ozaki, Soichi; Suwabe, Keita; Suzuki, Go; Watanabe, Masao; Matsuoka, Makoto; Takayama, Seiji; Yano, Kentaro

2016-10-13

In quantitative gene expression analysis, normalization using a reference gene as an internal control is frequently performed for appropriate interpretation of the results. Efforts have been devoted to exploring superior novel reference genes using microarray transcriptomic data and to evaluating commonly used reference genes by targeting analysis. However, because the number of specifically detectable genes is totally dependent on probe design in the microarray analysis, exploration using microarray data may miss some of the best choices for the reference genes. Recently emerging RNA sequencing (RNA-seq) provides an ideal resource for comprehensive exploration of reference genes since this method is capable of detecting all expressed genes, in principle including even unknown genes. We report the results of a comprehensive exploration of reference genes using public RNA-seq data from plants such as Arabidopsis thaliana (Arabidopsis), Glycine max (soybean), Solanum lycopersicum (tomato) and Oryza sativa (rice). To select reference genes suitable for the broadest experimental conditions possible, candidates were surveyed by the following four steps: (1) evaluation of the basal expression level of each gene in each experiment; (2) evaluation of the expression stability of each gene in each experiment; (3) evaluation of the expression stability of each gene across the experiments; and (4) selection of top-ranked genes, after ranking according to the number of experiments in which the gene was expressed stably. Employing this procedure, 13, 10, 12 and 21 top candidates for reference genes were proposed in Arabidopsis, soybean, tomato and rice, respectively. Microarray expression data confirmed that the expression of the proposed reference genes under broad experimental conditions was more stable than that of commonly used reference genes. These novel reference genes will be useful for analyzing gene expression profiles across experiments carried out under various experimental conditions.

Evaluation of microarray data normalization procedures using spike-in experiments

PubMed Central

Rydén, Patrik; Andersson, Henrik; Landfors, Mattias; Näslund, Linda; Hartmanová, Blanka; Noppa, Laila; Sjöstedt, Anders

2006-01-01

Background Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-called spike-in experiments, it is possible to characterize the analyzed data and thereby enable comparisons of different analysis methods. Results A spike-in experiment using eight in-house produced arrays was used to evaluate established and novel methods for filtration, background adjustment, scanning, channel adjustment, and censoring. The S-plus package EDMA, a stand-alone tool providing characterization of analyzed cDNA-microarray data obtained from spike-in experiments, was developed and used to evaluate 252 normalization methods. For all analyses, the sensitivities at low false positive rates were observed together with estimates of the overall bias and the standard deviation. In general, there was a trade-off between the ability of the analyses to identify differentially expressed genes (i.e. the analyses' sensitivities) and their ability to provide unbiased estimators of the desired ratios. Virtually all analysis underestimated the magnitude of the regulations; often less than 50% of the true regulations were observed. Moreover, the bias depended on the underlying mRNA-concentration; low concentration resulted in high bias. Many of the analyses had relatively low sensitivities, but analyses that used either the constrained model (i.e. a procedure that combines data from several scans) or partial filtration (a novel method for treating data from so-called not-found spots) had with few exceptions high sensitivities. These methods gave considerable higher sensitivities than some commonly used analysis methods. Conclusion The use of spike-in experiments is a powerful approach for evaluating microarray preprocessing procedures. Analyzed data are characterized by properties of the observed log-ratios and the analysis' ability to detect differentially expressed genes. If bias is not a major problem; we recommend the use of either the CM-procedure or partial filtration. PMID:16774679

A new functional membrane protein microarray based on tethered phospholipid bilayers.

PubMed

Chadli, Meriem; Maniti, Ofelia; Marquette, Christophe; Tillier, Bruno; Cortès, Sandra; Girard-Egrot, Agnès

2018-04-30

A new prototype of a membrane protein biochip is presented in this article. This biochip was created by the combination of novel technologies of peptide-tethered bilayer lipid membrane (pep-tBLM) formation and solid support micropatterning. Pep-tBLMs integrating a membrane protein were obtained in the form of microarrays on a gold chip. The formation of the microspots was visualized in real-time by surface plasmon resonance imaging (SPRi) and the functionality of a GPCR (CXCR4), reinserted locally into microwells, was assessed by ligand binding studies. In brief, to achieve micropatterning, P19-4H, a 4 histidine-possessing peptide spacer, was spotted inside microwells obtained on polystyrene-coated gold, and Ni-chelating proteoliposomes were injected into the reaction chamber. Proteoliposome binding to the peptide was based on metal-chelate interaction. The peptide-tethered lipid bilayer was finally obtained by addition of a fusogenic peptide (AH peptide) to promote proteoliposome fusion. The CXCR4 pep-tBLM microarray was characterized by surface plasmon resonance imaging (SPRi) throughout the building-up process. This new generation of membrane protein biochip represents a promising method of developing a screening tool for drug discovery.

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/].

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.

Variable approaches to genetic counseling for microarray regions of homozygosity associated with parental relatedness.

PubMed

Grote, Lauren; Myers, Melanie; Lovell, Anne; Saal, Howard; Sund, Kristen Lipscomb

2014-01-01

SNP microarrays are capable of detecting regions of homozygosity (ROH) which can suggest parental relatedness. This study was designed to describe pre- and post-test counseling practices of genetics professionals regarding ROH, explore perceived comfort and ethical concerns in the follow-up of such results, demonstrate awareness of laws surrounding duty to report consanguinity and incest, and allow respondents to share their personal experiences with results suggesting a parental relationship. A 35 question survey was administered to 240 genetic counselors and geneticists who had ordered or counseled for SNP microarray. The results are presented using descriptive statistics. There was variation in both pre- and post-test counseling practices of genetics professionals. Twenty-five percent of respondents reported pre-test counseling that ROH can indicate parental relatedness. The most commonly reported ethical concern was disclosure of findings suggesting parental relatedness to parents of the patient; only 48.4% reported disclosing parental relatedness when indicated. Fifty-seven percent felt comfortable receiving results suggesting parental consanguinity while 17% felt comfortable receiving results suggesting parental incest. Twenty percent of respondents were extremely/moderately familiar with the laws about duty to report incest. Personal experiences in post-test counseling included both parental acknowledgement and denial of relatedness. This study highlights the differences in genetics professionals' pre- and post-test counseling practices, comfort, and experiences surrounding parental relatedness suggested by SNP microarray results. It identifies a need for professional organizations to offer guidance to genetics professionals about how to respond to and counsel for molecular results suggesting parental consanguinity or incest. © 2013 Wiley Periodicals, Inc.

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.

Identifying Fishes through DNA Barcodes and Microarrays.

PubMed

Kochzius, Marc; Seidel, Christian; Antoniou, Aglaia; Botla, Sandeep Kumar; Campo, Daniel; Cariani, Alessia; Vazquez, Eva Garcia; Hauschild, Janet; Hervet, Caroline; Hjörleifsdottir, Sigridur; Hreggvidsson, Gudmundur; Kappel, Kristina; Landi, Monica; Magoulas, Antonios; Marteinsson, Viggo; Nölte, Manfred; Planes, Serge; Tinti, Fausto; Turan, Cemal; Venugopal, Moleyur N; Weber, Hannes; Blohm, Dietmar

2010-09-07

International fish trade reached an import value of 62.8 billion Euro in 2006, of which 44.6% are covered by the European Union. Species identification is a key problem throughout the life cycle of fishes: from eggs and larvae to adults in fisheries research and control, as well as processed fish products in consumer protection. This study aims to evaluate the applicability of the three mitochondrial genes 16S rRNA (16S), cytochrome b (cyt b), and cytochrome oxidase subunit I (COI) for the identification of 50 European marine fish species by combining techniques of "DNA barcoding" and microarrays. In a DNA barcoding approach, neighbour Joining (NJ) phylogenetic trees of 369 16S, 212 cyt b, and 447 COI sequences indicated that cyt b and COI are suitable for unambiguous identification, whereas 16S failed to discriminate closely related flatfish and gurnard species. In course of probe design for DNA microarray development, each of the markers yielded a high number of potentially species-specific probes in silico, although many of them were rejected based on microarray hybridisation experiments. None of the markers provided probes to discriminate the sibling flatfish and gurnard species. However, since 16S-probes were less negatively influenced by the "position of label" effect and showed the lowest rejection rate and the highest mean signal intensity, 16S is more suitable for DNA microarray probe design than cty b and COI. The large portion of rejected COI-probes after hybridisation experiments (>90%) renders the DNA barcoding marker as rather unsuitable for this high-throughput technology. Based on these data, a DNA microarray containing 64 functional oligonucleotide probes for the identification of 30 out of the 50 fish species investigated was developed. It represents the next step towards an automated and easy-to-handle method to identify fish, ichthyoplankton, and fish products.

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data

PubMed Central

Glez-Peña, Daniel; Álvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-01

Background Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. Results DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. Conclusion DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a successful tool for cancer diagnosis using microarray datasets, has recently been released. PMID:19178723

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data.

PubMed

Glez-Peña, Daniel; Alvarez, Rodrigo; Díaz, Fernando; Fdez-Riverola, Florentino

2009-01-29

Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models. DFP is a new Bioconductor R package that implements a method for discretizing and selecting differentially expressed genes based on the application of fuzzy logic. DFP takes advantage of fuzzy membership functions to assign linguistic labels to gene expression levels. The technique builds a reduced set of relevant genes (FP, Fuzzy Pattern) able to summarize and represent each underlying class (pathology). A last step constructs a biased set of genes (DFP, Discriminant Fuzzy Pattern) by intersecting existing fuzzy patterns in order to detect discriminative elements. In addition, the software provides new functions and visualisation tools that summarize achieved results and aid in the interpretation of differentially expressed genes from multiple microarray experiments. DFP integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It has the advantage that its parameters are highly configurable, facilitating the discovery of biologically relevant connections between sets of genes belonging to different pathologies. This information makes it possible to automatically filter irrelevant genes thereby reducing the large volume of data supplied by microarray experiments. Based on these contributions GENECBR, a successful tool for cancer diagnosis using microarray datasets, has recently been released.

Retrieving relevant time-course experiments: a study on Arabidopsis microarrays.

PubMed

Şener, Duygu Dede; Oğul, Hasan

2016-06-01

Understanding time-course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta-analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time-course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time-course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time-course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.

GPFrontend and GPGraphics: graphical analysis tools for genetic association studies.

PubMed

Uebe, Steffen; Pasutto, Francesca; Krumbiegel, Mandy; Schanze, Denny; Ekici, Arif B; Reis, André

2010-09-21

Most software packages for whole genome association studies are non-graphical, purely text based programs originally designed to run with UNIX-like operating systems. Graphical output is often not intended or supposed to be performed with other command line tools, e.g. gnuplot. Using the Microsoft .NET 2.0 platform and Visual Studio 2005, we have created a graphical software package to analyze data from microarray whole genome association studies, both for a DNA-pooling based approach as well as regular single sample data. Part of this package was made to integrate with GenePool 0.8.2, a previously existing software suite for GNU/Linux systems, which we have modified to run in a Microsoft Windows environment. Further modifications cause it to generate some additional data. This enables GenePool to interact with the .NET parts created by us. The programs we developed are GPFrontend, a graphical user interface and frontend to use GenePool and create metadata files for it, and GPGraphics, a program to further analyze and graphically evaluate output of different WGA analysis programs, among them also GenePool. Our programs enable regular MS Windows users without much experience in bioinformatics to easily visualize whole genome data from a variety of sources.

Graphite Web: web tool for gene set analysis exploiting pathway topology

PubMed Central

Sales, Gabriele; Calura, Enrica; Martini, Paolo; Romualdi, Chiara

2013-01-01

Graphite web is a novel web tool for pathway analyses and network visualization for gene expression data of both microarray and RNA-seq experiments. Several pathway analyses have been proposed either in the univariate or in the global and multivariate context to tackle the complexity and the interpretation of expression results. These methods can be further divided into ‘topological’ and ‘non-topological’ methods according to their ability to gain power from pathway topology. Biological pathways are, in fact, not only gene lists but can be represented through a network where genes and connections are, respectively, nodes and edges. To this day, the most used approaches are non-topological and univariate although they miss the relationship among genes. On the contrary, topological and multivariate approaches are more powerful, but difficult to be used by researchers without bioinformatic skills. Here we present Graphite web, the first public web server for pathway analysis on gene expression data that combines topological and multivariate pathway analyses with an efficient system of interactive network visualizations for easy results interpretation. Specifically, Graphite web implements five different gene set analyses on three model organisms and two pathway databases. Graphite Web is freely available at http://graphiteweb.bio.unipd.it/. PMID:23666626

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

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

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.

Integrative missing value estimation for microarray data.

PubMed

Hu, Jianjun; Li, Haifeng; Waterman, Michael S; Zhou, Xianghong Jasmine

2006-10-12

Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (LLS) imputation algorithm by up to 15% improvement in our benchmark tests. We demonstrated that the order-statistics-based integrative imputation algorithms can achieve significant improvements over the state-of-the-art missing value estimation approaches such as LLS and is especially good for imputing microarray datasets with a limited number of samples, high rates of missing data, or very noisy measurements. With the rapid accumulation of microarray datasets, the performance of our approach can be further improved by incorporating larger and more appropriate reference datasets.

MALDI-TOF mass spectrometry for quantitative gene expression analysis of acid responses in Staphylococcus aureus.

PubMed

Rode, Tone Mari; Berget, Ingunn; Langsrud, Solveig; Møretrø, Trond; Holck, Askild

2009-07-01

Microorganisms are constantly exposed to new and altered growth conditions, and respond by changing gene expression patterns. Several methods for studying gene expression exist. During the last decade, the analysis of microarrays has been one of the most common approaches applied for large scale gene expression studies. A relatively new method for gene expression analysis is MassARRAY, which combines real competitive-PCR and MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry. In contrast to microarray methods, MassARRAY technology is suitable for analysing a larger number of samples, though for a smaller set of genes. In this study we compare the results from MassARRAY with microarrays on gene expression responses of Staphylococcus aureus exposed to acid stress at pH 4.5. RNA isolated from the same stress experiments was analysed using both the MassARRAY and the microarray methods. The MassARRAY and microarray methods showed good correlation. Both MassARRAY and microarray estimated somewhat lower fold changes compared with quantitative real-time PCR (qRT-PCR). The results confirmed the up-regulation of the urease genes in acidic environments, and also indicated the importance of metal ion regulation. This study shows that the MassARRAY technology is suitable for gene expression analysis in prokaryotes, and has advantages when a set of genes is being analysed for an organism exposed to many different environmental conditions.

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

The XBabelPhish MAGE-ML and XML translator.

PubMed

Maier, Don; Wymore, Farrell; Sherlock, Gavin; Ball, Catherine A

2008-01-18

MAGE-ML has been promoted as a standard format for describing microarray experiments and the data they produce. Two characteristics of the MAGE-ML format compromise its use as a universal standard: First, MAGE-ML files are exceptionally large - too large to be easily read by most people, and often too large to be read by most software programs. Second, the MAGE-ML standard permits many ways of representing the same information. As a result, different producers of MAGE-ML create different documents describing the same experiment and its data. Recognizing all the variants is an unwieldy software engineering task, resulting in software packages that can read and process MAGE-ML from some, but not all producers. This Tower of MAGE-ML Babel bars the unencumbered exchange of microarray experiment descriptions couched in MAGE-ML. We have developed XBabelPhish - an XQuery-based technology for translating one MAGE-ML variant into another. XBabelPhish's use is not restricted to translating MAGE-ML documents. It can transform XML files independent of their DTD, XML schema, or semantic content. Moreover, it is designed to work on very large (> 200 Mb.) files, which are common in the world of MAGE-ML. XBabelPhish provides a way to inter-translate MAGE-ML variants for improved interchange of microarray experiment information. More generally, it can be used to transform most XML files, including very large ones that exceed the capacity of most XML tools.

Applying dynamic Bayesian networks to perturbed gene expression data.

PubMed

Dojer, Norbert; Gambin, Anna; Mizera, Andrzej; Wilczyński, Bartek; Tiuryn, Jerzy

2006-05-08

A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments. We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed. We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

MADGE: scalable distributed data management software for cDNA microarrays.

PubMed

McIndoe, Richard A; Lanzen, Aaron; Hurtz, Kimberly

2003-01-01

The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments. We have developed a distributable software package designed to track and store the various pieces of data generated by a cDNA microarray facility. This includes the clone collection storage data, annotation data, workflow queues, microarray data, data repositories, sample submission information, and project/investigator information. This application was designed using a 3-tier client server model. The data access layer (1st tier) contains the relational database system tuned to support a large number of transactions. The data services layer (2nd tier) is a distributed COM server with full database transaction support. The application layer (3rd tier) is an internet based user interface that contains both client and server side code for dynamic interactions with the user. This software is freely available to academic institutions and non-profit organizations at http://www.genomics.mcg.edu/niddkbtc.

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.

Metadata management and semantics in microarray repositories.

PubMed

Kocabaş, F; Can, T; Baykal, N

2011-12-01

The number of microarray and other high-throughput experiments on primary repositories keeps increasing as do the size and complexity of the results in response to biomedical investigations. Initiatives have been started on standardization of content, object model, exchange format and ontology. However, there are backlogs and inability to exchange data between microarray repositories, which indicate that there is a great need for a standard format and data management. We have introduced a metadata framework that includes a metadata card and semantic nets that make experimental results visible, understandable and usable. These are encoded in syntax encoding schemes and represented in RDF (Resource Description Frame-word), can be integrated with other metadata cards and semantic nets, and can be exchanged, shared and queried. We demonstrated the performance and potential benefits through a case study on a selected microarray repository. We concluded that the backlogs can be reduced and that exchange of information and asking of knowledge discovery questions can become possible with the use of this metadata framework.

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

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.

"It wasn't a disaster or anything": Parents' experiences of their child's uncertain chromosomal microarray result.

PubMed

Wilkins, Ella J; Archibald, Alison D; Sahhar, Margaret A; White, Susan M

2016-11-01

Chromosomal microarray is an increasingly utilized diagnostic test, particularly in the pediatric setting. However, the clinical significance of copy number variants detected by this technology is not always understood, creating uncertainties in interpreting and communicating results. The aim of this study was to explore parents' experiences of an uncertain microarray result for their child. This research utilized a qualitative approach with a phenomenological methodology. Semi-structured interviews were conducted with nine parents of eight children who received an uncertain microarray result for their child, either a 16p11.2 microdeletion or 15q13.3 microdeletion. Interviews were transcribed verbatim and thematic analysis was used to identify themes within the data. Participants were unprepared for the abnormal test result. They had a complex perception of the extent of their child's condition and a mixed understanding of the clinical relevance of the result, but were accepting of the limitations of medical knowledge, and appeared to have adapted to the result. The test result was empowering for parents in terms of access to medical and educational services; however, they articulated significant unmet support needs. Participants expressed hope for the future, in particular that more information would become available over time. This research has demonstrated that parents of children who have an uncertain microarray result appeared to adapt to uncertainty and limited availability of information and valued honesty and empathic ongoing support from health professionals. Genetic health professionals are well positioned to provide such support and aid patients' and families' adaptation to their situation as well as promote empowerment. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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.

CoPub: a literature-based keyword enrichment tool for microarray data analysis.

PubMed

Frijters, Raoul; Heupers, Bart; van Beek, Pieter; Bouwhuis, Maurice; van Schaik, René; de Vlieg, Jacob; Polman, Jan; Alkema, Wynand

2008-07-01

Medline is a rich information source, from which links between genes and keywords describing biological processes, pathways, drugs, pathologies and diseases can be extracted. We developed a publicly available tool called CoPub that uses the information in the Medline database for the biological interpretation of microarray data. CoPub allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs. CoPub is freely accessible at http://services.nbic.nl/cgi-bin/copub/CoPub.pl.

Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments.

PubMed

Duan, Fenghai; Xu, Ye

2017-01-01

To analyze a microarray experiment to identify the genes with expressions varying after the diagnosis of breast cancer. A total of 44 928 probe sets in an Affymetrix microarray data publicly available on Gene Expression Omnibus from 249 patients with breast cancer were analyzed by the nonparametric multivariate adaptive splines. Then, the identified genes with turning points were grouped by K-means clustering, and their network relationship was subsequently analyzed by the Ingenuity Pathway Analysis. In total, 1640 probe sets (genes) were reliably identified to have turning points along with the age at diagnosis in their expression profiling, of which 927 expressed lower after turning points and 713 expressed higher after the turning points. K-means clustered them into 3 groups with turning points centering at 54, 62.5, and 72, respectively. The pathway analysis showed that the identified genes were actively involved in various cancer-related functions or networks. In this article, we applied the nonparametric multivariate adaptive splines method to a publicly available gene expression data and successfully identified genes with expressions varying before and after breast cancer diagnosis.

Framework for Parallel Preprocessing of Microarray Data Using Hadoop

PubMed Central

2018-01-01

Nowadays, microarray technology has become one of the popular ways to study gene expression and diagnosis of disease. National Center for Biology Information (NCBI) hosts public databases containing large volumes of biological data required to be preprocessed, since they carry high levels of noise and bias. Robust Multiarray Average (RMA) is one of the standard and popular methods that is utilized to preprocess the data and remove the noises. Most of the preprocessing algorithms are time-consuming and not able to handle a large number of datasets with thousands of experiments. Parallel processing can be used to address the above-mentioned issues. Hadoop is a well-known and ideal distributed file system framework that provides a parallel environment to run the experiment. In this research, for the first time, the capability of Hadoop and statistical power of R have been leveraged to parallelize the available preprocessing algorithm called RMA to efficiently process microarray data. The experiment has been run on cluster containing 5 nodes, while each node has 16 cores and 16 GB memory. It compares efficiency and the performance of parallelized RMA using Hadoop with parallelized RMA using affyPara package as well as sequential RMA. The result shows the speed-up rate of the proposed approach outperforms the sequential approach and affyPara approach. PMID:29796018

Microgravity elicits reproducible alterations in cytoskeletal and metabolic gene and protein expression in space-flown Caenorhabditis elegans

PubMed Central

Higashibata, Akira; Hashizume, Toko; Nemoto, Kanako; Higashitani, Nahoko; Etheridge, Timothy; Mori, Chihiro; Harada, Shunsuke; Sugimoto, Tomoko; Szewczyk, Nathaniel J; Baba, Shoji A; Mogami, Yoshihiro; Fukui, Keiji; Higashitani, Atsushi

2016-01-01

Although muscle atrophy is a serious problem during spaceflight, little is known about the sequence of molecular events leading to atrophy in response to microgravity. We carried out a spaceflight experiment using Caenorhabditis elegans onboard the Japanese Experiment Module of the International Space Station. Worms were synchronously cultured in liquid media with bacterial food for 4 days under microgravity or on a 1-G centrifuge. Worms were visually observed for health and movement and then frozen. Upon return, we analyzed global gene and protein expression using DNA microarrays and mass spectrometry. Body length and fat accumulation were also analyzed. We found that in worms grown from the L1 larval stage to adulthood under microgravity, both gene and protein expression levels for muscular thick filaments, cytoskeletal elements, and mitochondrial metabolic enzymes decreased relative to parallel cultures on the 1-G centrifuge (95% confidence interval (P⩽0.05)). In addition, altered movement and decreased body length and fat accumulation were observed in the microgravity-cultured worms relative to the 1-G cultured worms. These results suggest protein expression changes that may account for the progressive muscular atrophy observed in astronauts. PMID:28725720

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis.

PubMed

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-11-07

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s).

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis

PubMed Central

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-01-01

Background To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. Results We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. Conclusion PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s). PMID:14604444

Deformation and Breakup of a Stretching Liquid Bridge

NASA Astrophysics Data System (ADS)

Franses, Elias I.; Liao, Ying-Chih; Basaran, Osman

2004-11-01

Surfactants are routinely used to control the breakup of drops and jets in applications as diverse as ink jet printing, crop spraying, and microarraying. While highly accurate algorithms for studying the breakup of surfactant-free drops and jets are well documented and a great deal of information is now available in such situations, little is known about the closely related problem of interface rupture when surfactant effects cannot be neglected. Here we analyze the deformation and breakup of a stretching liquid bridge whose surface is covered with an insoluble surfactant monolayer by means of a two-dimensional (2-d) finite element algorithm using elliptic mesh generation. That the predictions made with the 2-d algorithm are faithful to the physics is confirmed by demonstrating that the computed results accord well with our new high-speed visualization experiments and existing scaling theories. Comparisons are also made to computations made with a one-dimensional (1-d) algorithm based on the slender-jet equations.

Hidden discriminative features extraction for supervised high-order time series modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2016-11-01

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

In situ hybridization analysis of the expression of futsch, tau, and MESK2 homologues in the brain of the European honeybee (Apis mellifera L.).

PubMed

Kaneko, Kumi; Hori, Sayaka; Morimoto, Mai M; Nakaoka, Takayoshi; Paul, Rajib Kumar; Fujiyuki, Tomoko; Shirai, Kenichi; Wakamoto, Akiko; Tsuboko, Satomi; Takeuchi, Hideaki; Kubo, Takeo

2010-02-16

The importance of visual sense in Hymenopteran social behavior is suggested by the existence of a Hymenopteran insect-specific neural circuit related to visual processing and the fact that worker honeybee brain changes morphologically according to its foraging experience. To analyze molecular and neural bases that underlie the visual abilities of the honeybees, we used a cDNA microarray to search for gene(s) expressed in a neural cell-type preferential manner in a visual center of the honeybee brain, the optic lobes (OLs). Expression analysis of candidate genes using in situ hybridization revealed two genes expressed in a neural cell-type preferential manner in the OLs. One is a homologue of Drosophila futsch, which encodes a microtubule-associated protein and is preferentially expressed in the monopolar cells in the lamina of the OLs. The gene for another microtubule-associated protein, tau, which functionally overlaps with futsch, was also preferentially expressed in the monopolar cells, strongly suggesting the functional importance of these two microtubule-associated proteins in monopolar cells. The other gene encoded a homologue of Misexpression Suppressor of Dominant-negative Kinase Suppressor of Ras 2 (MESK2), which might activate Ras/MAPK-signaling in Drosophila. MESK2 was expressed preferentially in a subclass of neurons located in the ventral region between the lamina and medulla neuropil in the OLs, suggesting that this subclass is a novel OL neuron type characterized by MESK2-expression. These three genes exhibited similar expression patterns in the worker, drone, and queen brains, suggesting that they function similarly irrespective of the honeybee sex or caste. Here we identified genes that are expressed in a monopolar cell (Amfutsch and Amtau) or ventral medulla-preferential manner (AmMESK2) in insect OLs. These genes may aid in visualizing neurites of monopolar cells and ventral medulla cells, as well as in analyzing the function of these neurons.

Microarray gene expression profiling using core biopsies of renal neoplasia.

PubMed

Rogers, Craig G; Ditlev, Jonathon A; Tan, Min-Han; Sugimura, Jun; Qian, Chao-Nan; Cooper, Jeff; Lane, Brian; Jewett, Michael A; Kahnoski, Richard J; Kort, Eric J; Teh, Bin T

2009-01-01

We investigate the feasibility of using microarray gene expression profiling technology to analyze core biopsies of renal tumors for classification of tumor histology. Core biopsies were obtained ex-vivo from 7 renal tumors-comprised of four histological subtypes-following radical nephrectomy using 18-gauge biopsy needles. RNA was isolated from these samples and, in the case of biopsy samples, amplified by in vitro transcription. Microarray analysis was then used to quantify the mRNA expression patterns in these samples relative to non-diseased renal tissue mRNA. Genes with significant variation across all non-biopsy tumor samples were identified, and the relationship between tumor and biopsy samples in terms of expression levels of these genes was then quantified in terms of Euclidean distance, and visualized by complete linkage clustering. Final pathologic assessment of kidney tumors demonstrated clear cell renal cell carcinoma (4), oncocytoma (1), angiomyolipoma (1) and adrenalcortical carcinoma (1). Five of the seven biopsy samples were most similar in terms of gene expression to the resected tumors from which they were derived in terms of Euclidean distance. All seven biopsies were assigned to the correct histological class by hierarchical clustering. We demonstrate the feasibility of gene expression profiling of core biopsies of renal tumors to classify tumor histology.

Microarray gene expression profiling using core biopsies of renal neoplasia

PubMed Central

Rogers, Craig G.; Ditlev, Jonathon A.; Tan, Min-Han; Sugimura, Jun; Qian, Chao-Nan; Cooper, Jeff; Lane, Brian; Jewett, Michael A.; Kahnoski, Richard J.; Kort, Eric J.; Teh, Bin T.

2009-01-01

We investigate the feasibility of using microarray gene expression profiling technology to analyze core biopsies of renal tumors for classification of tumor histology. Core biopsies were obtained ex-vivo from 7 renal tumors—comprised of four histological subtypes—following radical nephrectomy using 18-gauge biopsy needles. RNA was isolated from these samples and, in the case of biopsy samples, amplified by in vitro transcription. Microarray analysis was then used to quantify the mRNA expression patterns in these samples relative to non-diseased renal tissue mRNA. Genes with significant variation across all non-biopsy tumor samples were identified, and the relationship between tumor and biopsy samples in terms of expression levels of these genes was then quantified in terms of Euclidean distance, and visualized by complete linkage clustering. Final pathologic assessment of kidney tumors demonstrated clear cell renal cell carcinoma (4), oncocytoma (1), angiomyolipoma (1) and adrenalcortical carcinoma (1). Five of the seven biopsy samples were most similar in terms of gene expression to the resected tumors from which they were derived in terms of Euclidean distance. All seven biopsies were assigned to the correct histological class by hierarchical clustering. We demonstrate the feasibility of gene expression profiling of core biopsies of renal tumors to classify tumor histology. PMID:19966938

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

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.

Mayday - integrative analytics for expression data

PubMed Central

2010-01-01

Background DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files. Results We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved. Conclusions We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at http://microarray-analysis.org. PMID:20214778

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

Bayesian hierarchical modeling for subject-level response classification in peptide microarray immunoassays

PubMed Central

Imholte, Gregory; Gottardo, Raphael

2017-01-01

Summary The peptide microarray immunoassay simultaneously screens sample serum against thousands of peptides, determining the presence of antibodies bound to array probes. Peptide microarrays tiling immunogenic regions of pathogens (e.g. envelope proteins of a virus) are an important high throughput tool for querying and mapping antibody binding. Because of the assay’s many steps, from probe synthesis to incubation, peptide microarray data can be noisy with extreme outliers. In addition, subjects may produce different antibody profiles in response to an identical vaccine stimulus or infection, due to variability among subjects’ immune systems. We present a robust Bayesian hierarchical model for peptide microarray experiments, pepBayes, to estimate the probability of antibody response for each subject/peptide combination. Heavy-tailed error distributions accommodate outliers and extreme responses, and tailored random effect terms automatically incorporate technical effects prevalent in the assay. We apply our model to two vaccine trial datasets to demonstrate model performance. Our approach enjoys high sensitivity and specificity when detecting vaccine induced antibody responses. A simulation study shows an adaptive thresholding classification method has appropriate false discovery rate control with high sensitivity, and receiver operating characteristics generated on vaccine trial data suggest that pepBayes clearly separates responses from non-responses. PMID:27061097

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.

Translating standards into practice - one Semantic Web API for Gene Expression.

PubMed

Deus, Helena F; Prud'hommeaux, Eric; Miller, Michael; Zhao, Jun; Malone, James; Adamusiak, Tomasz; McCusker, Jim; Das, Sudeshna; Rocca Serra, Philippe; Fox, Ronan; Marshall, M Scott

2012-08-01

Sharing and describing experimental results unambiguously with sufficient detail to enable replication of results is a fundamental tenet of scientific research. In today's cluttered world of "-omics" sciences, data standards and standardized use of terminologies and ontologies for biomedical informatics play an important role in reporting high-throughput experiment results in formats that can be interpreted by both researchers and analytical tools. Increasing adoption of Semantic Web and Linked Data technologies for the integration of heterogeneous and distributed health care and life sciences (HCLSs) datasets has made the reuse of standards even more pressing; dynamic semantic query federation can be used for integrative bioinformatics when ontologies and identifiers are reused across data instances. We present here a methodology to integrate the results and experimental context of three different representations of microarray-based transcriptomic experiments: the Gene Expression Atlas, the W3C BioRDF task force approach to reporting Provenance of Microarray Experiments, and the HSCI blood genomics project. Our approach does not attempt to improve the expressivity of existing standards for genomics but, instead, to enable integration of existing datasets published from microarray-based transcriptomic experiments. SPARQL Construct is used to create a posteriori mappings of concepts and properties and linking rules that match entities based on query constraints. We discuss how our integrative approach can encourage reuse of the Experimental Factor Ontology (EFO) and the Ontology for Biomedical Investigations (OBIs) for the reporting of experimental context and results of gene expression studies. Copyright © 2012 Elsevier Inc. All rights reserved.

Methods for processing microarray data.

PubMed

Ares, Manuel

2014-02-01

Quality control must be maintained at every step of a microarray experiment, from RNA isolation through statistical evaluation. Here we provide suggestions for analyzing microarray data. Because the utility of the results depends directly on the design of the experiment, the first critical step is to ensure that the experiment can be properly analyzed and interpreted. What is the biological question? What is the best way to perform the experiment? How many replicates will be required to obtain the desired statistical resolution? Next, the samples must be prepared, pass quality controls for integrity and representation, and be hybridized and scanned. Also, slides with defects, missing data, high background, or weak signal must be rejected. Data from individual slides must be normalized and combined so that the data are as free of systematic bias as possible. The third phase is to apply statistical filters and tests to the data to determine genes (1) expressed above background, (2) whose expression level changes in different samples, and (3) whose RNA-processing patterns or protein associations change. Next, a subset of the data should be validated by an alternative method, such as reverse transcription-polymerase chain reaction (RT-PCR). Provided that this endorses the general conclusions of the array analysis, gene sets whose expression, splicing, polyadenylation, protein binding, etc. change in different samples can be classified with respect to function, sequence motif properties, as well as other categories to extract hypotheses for their biological roles and regulatory logic.

geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification.

PubMed

Reboiro-Jato, Miguel; Arrais, Joel P; Oliveira, José Luis; Fdez-Riverola, Florentino

2014-01-30

The diagnosis and prognosis of several diseases can be shortened through the use of different large-scale genome experiments. In this context, microarrays can generate expression data for a huge set of genes. However, to obtain solid statistical evidence from the resulting data, it is necessary to train and to validate many classification techniques in order to find the best discriminative method. This is a time-consuming process that normally depends on intricate statistical tools. geneCommittee is a web-based interactive tool for routinely evaluating the discriminative classification power of custom hypothesis in the form of biologically relevant gene sets. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. Provided with a straightforward and intuitive interface, geneCommittee is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research. geneCommittee allows the enrichment of microarrays raw data with gene functional annotations, producing integrated datasets that simplify the construction of better discriminative hypothesis, and allows the creation of a set of complementary classifiers. The trained committees can then be used for clinical research and diagnosis. Full documentation including common use cases and guided analysis workflows is freely available at http://sing.ei.uvigo.es/GC/.

The Innate Immune Database (IIDB)

PubMed Central

Korb, Martin; Rust, Aistair G; Thorsson, Vesteinn; Battail, Christophe; Li, Bin; Hwang, Daehee; Kennedy, Kathleen A; Roach, Jared C; Rosenberger, Carrie M; Gilchrist, Mark; Zak, Daniel; Johnson, Carrie; Marzolf, Bruz; Aderem, Alan; Shmulevich, Ilya; Bolouri, Hamid

2008-01-01

Background As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site . Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens. Description We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser. Conclusion We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at . PMID:18321385

Weighted analysis of paired microarray experiments.

PubMed

Kristiansson, Erik; Sjögren, Anders; Rudemo, Mats; Nerman, Olle

2005-01-01

In microarray experiments quality often varies, for example between samples and between arrays. The need for quality control is therefore strong. A statistical model and a corresponding analysis method is suggested for experiments with pairing, including designs with individuals observed before and after treatment and many experiments with two-colour spotted arrays. The model is of mixed type with some parameters estimated by an empirical Bayes method. Differences in quality are modelled by individual variances and correlations between repetitions. The method is applied to three real and several simulated datasets. Two of the real datasets are of Affymetrix type with patients profiled before and after treatment, and the third dataset is of two-colour spotted cDNA type. In all cases, the patients or arrays had different estimated variances, leading to distinctly unequal weights in the analysis. We suggest also plots which illustrate the variances and correlations that affect the weights computed by our analysis method. For simulated data the improvement relative to previously published methods without weighting is shown to be substantial.

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

Next generation tools for genomic data generation, distribution, and visualization

PubMed Central

2010-01-01

Background With the rapidly falling cost and availability of high throughput sequencing and microarray technologies, the bottleneck for effectively using genomic analysis in the laboratory and clinic is shifting to one of effectively managing, analyzing, and sharing genomic data. Results Here we present three open-source, platform independent, software tools for generating, analyzing, distributing, and visualizing genomic data. These include a next generation sequencing/microarray LIMS and analysis project center (GNomEx); an application for annotating and programmatically distributing genomic data using the community vetted DAS/2 data exchange protocol (GenoPub); and a standalone Java Swing application (GWrap) that makes cutting edge command line analysis tools available to those who prefer graphical user interfaces. Both GNomEx and GenoPub use the rich client Flex/Flash web browser interface to interact with Java classes and a relational database on a remote server. Both employ a public-private user-group security model enabling controlled distribution of patient and unpublished data alongside public resources. As such, they function as genomic data repositories that can be accessed manually or programmatically through DAS/2-enabled client applications such as the Integrated Genome Browser. Conclusions These tools have gained wide use in our core facilities, research laboratories and clinics and are freely available for non-profit use. See http://sourceforge.net/projects/gnomex/, http://sourceforge.net/projects/genoviz/, and http://sourceforge.net/projects/useq. PMID:20828407

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

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.

Methylation oligonucleotide microarray: a novel tool to analyze methylation patterns

NASA Astrophysics Data System (ADS)

Hou, Peng; Ji, Meiju; He, Nongyao; Lu, Zuhong

2003-04-01

A new technique to analyze methylation patterns in several adjacent CpG sites was developed and reported here. We selected a 336bp segment of the 5"-untranslated region and the first exon of the p16Ink4a gene, which include the most densely packed CpG fragment of the islands containing 32 CpG dinucleotides, as the investigated target. The probes that include all types of methylation patterns were designed to fabricate a DNA microarray to determine the methylation patterns of seven adjacent CpG dinucleotides sites. High accuracy and reproducibility were observed in several parallel experiments. The results led us to the conclusion that the methylation oligonucleotide microarray can be applied as a novel and powerful tool to map methylation patterns and changes in multiple CpG island loci in a variety of tumors.

Gene Expression Analysis: Teaching Students to Do 30,000 Experiments at Once with Microarray

ERIC Educational Resources Information Center

Carvalho, Felicia I.; Johns, Christopher; Gillespie, Marc E.

2012-01-01

Genome scale experiments routinely produce large data sets that require computational analysis, yet there are few student-based labs that illustrate the design and execution of these experiments. In order for students to understand and participate in the genomic world, teaching labs must be available where students generate and analyze large data…

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

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.

Recursive feature selection with significant variables of support vectors.

PubMed

Tsai, Chen-An; Huang, Chien-Hsun; Chang, Ching-Wei; Chen, Chun-Houh

2012-01-01

The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.

D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements

PubMed Central

2013-01-01

Background The binding of transcription factors to DNA plays an essential role in the regulation of gene expression. Numerous experiments elucidated binding sequences which subsequently have been used to derive statistical models for predicting potential transcription factor binding sites (TFBS). The rapidly increasing number of genome sequence data requires sophisticated computational approaches to manage and query experimental and predicted TFBS data in the context of other epigenetic factors and across different organisms. Results We have developed D-Light, a novel client-server software package to store and query large amounts of TFBS data for any number of genomes. Users can add small-scale data to the server database and query them in a large scale, genome-wide promoter context. The client is implemented in Java and provides simple graphical user interfaces and data visualization. Here we also performed a statistical analysis showing what a user can expect for certain parameter settings and we illustrate the usage of D-Light with the help of a microarray data set. Conclusions D-Light is an easy to use software tool to integrate, store and query annotation data for promoters. A public D-Light server, the client and server software for local installation and the source code under GNU GPL license are available at http://biwww.che.sbg.ac.at/dlight. PMID:23617301

CrossQuery: a web tool for easy associative querying of transcriptome data.

PubMed

Wagner, Toni U; Fischer, Andreas; Thoma, Eva C; Schartl, Manfred

2011-01-01

Enormous amounts of data are being generated by modern methods such as transcriptome or exome sequencing and microarray profiling. Primary analyses such as quality control, normalization, statistics and mapping are highly complex and need to be performed by specialists. Thereafter, results are handed back to biomedical researchers, who are then confronted with complicated data lists. For rather simple tasks like data filtering, sorting and cross-association there is a need for new tools which can be used by non-specialists. Here, we describe CrossQuery, a web tool that enables straight forward, simple syntax queries to be executed on transcriptome sequencing and microarray datasets. We provide deep-sequencing data sets of stem cell lines derived from the model fish Medaka and microarray data of human endothelial cells. In the example datasets provided, mRNA expression levels, gene, transcript and sample identification numbers, GO-terms and gene descriptions can be freely correlated, filtered and sorted. Queries can be saved for later reuse and results can be exported to standard formats that allow copy-and-paste to all widespread data visualization tools such as Microsoft Excel. CrossQuery enables researchers to quickly and freely work with transcriptome and microarray data sets requiring only minimal computer skills. Furthermore, CrossQuery allows growing association of multiple datasets as long as at least one common point of correlated information, such as transcript identification numbers or GO-terms, is shared between samples. For advanced users, the object-oriented plug-in and event-driven code design of both server-side and client-side scripts allow easy addition of new features, data sources and data types.

Methods for processing high-throughput RNA sequencing data.

PubMed

Ares, Manuel

2014-11-03

High-throughput sequencing (HTS) methods for analyzing RNA populations (RNA-Seq) are gaining rapid application to many experimental situations. The steps in an RNA-Seq experiment require thought and planning, especially because the expense in time and materials is currently higher and the protocols are far less routine than those used for other high-throughput methods, such as microarrays. As always, good experimental design will make analysis and interpretation easier. Having a clear biological question, an idea about the best way to do the experiment, and an understanding of the number of replicates needed will make the entire process more satisfying. Whether the goal is capturing transcriptome complexity from a tissue or identifying small fragments of RNA cross-linked to a protein of interest, conversion of the RNA to cDNA followed by direct sequencing using the latest methods is a developing practice, with new technical modifications and applications appearing every day. Even more rapid are the development and improvement of methods for analysis of the very large amounts of data that arrive at the end of an RNA-Seq experiment, making considerations regarding reproducibility, validation, visualization, and interpretation increasingly important. This introduction is designed to review and emphasize a pathway of analysis from experimental design through data presentation that is likely to be successful, with the recognition that better methods are right around the corner. © 2014 Cold Spring Harbor Laboratory Press.

The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

PubMed

Mansourian, Robert; Mutch, David M; Antille, Nicolas; Aubert, Jerome; Fogel, Paul; Le Goff, Jean-Marc; Moulin, Julie; Petrov, Anton; Rytz, Andreas; Voegel, Johannes J; Roberts, Matthew-Alan

2004-11-01

Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining as an intuitive and computationally efficient procedure. The present study describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray datasets, and was developed using an in vitro experiment that compared control and interferon-gamma treated skin cells. In this experiment, up to nine replicates were used to confidently estimate error, thereby enabling methods of different statistical power to be compared. Gene expression results of a similar absolute expression are binned, so as to enable a highly accurate local estimate of the mean squared error within conditions. The model then relates variability of gene expression in each bin to absolute expression levels and uses this in a test derived from the classical ANOVA. The GEA selection method is compared with both the classical and permutational ANOVA tests, and demonstrates an increased stability, robustness and confidence in gene selection. A subset of the selected genes were validated by real-time reverse transcription-polymerase chain reaction (RT-PCR). All these results suggest that GEA methodology is (i) suitable for selection of differentially expressed genes in microarray data, (ii) intuitive and computationally efficient and (iii) especially advantageous under conditions of low k. The GEA code for R software is freely available upon request to authors.

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

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

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification

PubMed Central

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice. PMID:25823003

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification.

PubMed

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

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

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.

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

Inkjet-assisted layer-by-layer printing of quantum dot/enzyme microarrays for highly sensitive detection of organophosphorous pesticides.

PubMed

Luan, Enxiao; Zheng, Zhaozhu; Li, Xinyu; Gu, Hongxi; Liu, Shaoqin

2016-04-15

We present a facile fabrication of layer-by-layer (LbL) microarrays of quantum dots (QDs) and acetylcholinesterase enzyme (AChE). The resulting arrays had several unique properties, such as low cost, high integration and excellent flexibility and time-saving. The presence of organophosphorous pesticides (OPs) can inhibit the AChE activity and thus changes the fluorescent intensity of QDs/AChE microscopic dot arrays. Therefore, the QDs/AChE microscopic dot arrays were used for the sensitive visual detection of OPs. Linear calibration for parathion and paraoxon was obtained in the range of 5-100 μg L(-1) under the optimized conditions with the limit of detection (LOD) of 10 μg L(-1). The arrays have been successfully used for detection of OPs in fruits and water real samples. The new array was validated by comparison with conventional high performance liquid chromatography-mass spectrometry (HPLC-MS). Copyright © 2016 Elsevier B.V. All rights reserved.

From Saccharomyces cerevisiae to human: The important gene co-expression modules.

PubMed

Liu, Wei; Li, Li; Ye, Hua; Chen, Haiwei; Shen, Weibiao; Zhong, Yuexian; Tian, Tian; He, Huaqin

2017-08-01

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae . Seventeen stable gene co-expression modules were detected from 2,814 S. cerevisiae microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.

Isolation of Microarray-Grade Total RNA, MicroRNA, and DNA from a Single PAXgene Blood RNA Tube

PubMed Central

Kruhøffer, Mogens; Dyrskjøt, Lars; Voss, Thorsten; Lindberg, Raija L.P.; Wyrich, Ralf; Thykjaer, Thomas; Orntoft, Torben F.

2007-01-01

We have developed a procedure for isolation of microRNA and genomic DNA in addition to total RNA from whole blood stabilized in PAXgene Blood RNA tubes. The procedure is based on automatic extraction on a BioRobot MDx and includes isolation of DNA from a fraction of the stabilized blood and recovery of small RNA species that are otherwise lost. The procedure presented here is suitable for large-scale experiments and is amenable to further automation. Procured total RNA and DNA was tested using Affymetrix Expression and single-nucleotide polymorphism GeneChips, respectively, and isolated microRNA was tested using spotted locked nucleic acid-based microarrays. We conclude that the yield and quality of total RNA, microRNA, and DNA from a single PAXgene blood RNA tube is sufficient for downstream microarray analysis. PMID:17690207

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

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

PubMed

Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

2016-03-01

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

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.

Impact of point-mutations on the hybridization affinity of surface-bound DNA/DNA and RNA/DNA oligonucleotide-duplexes: Comparison of single base mismatches and base bulges

PubMed Central

Naiser, Thomas; Ehler, Oliver; Kayser, Jona; Mai, Timo; Michel, Wolfgang; Ott, Albrecht

2008-01-01

Background The high binding specificity of short 10 to 30 mer oligonucleotide probes enables single base mismatch (MM) discrimination and thus provides the basis for genotyping and resequencing microarray applications. Recent experiments indicate that the underlying principles governing DNA microarray hybridization – and in particular MM discrimination – are not completely understood. Microarrays usually address complex mixtures of DNA targets. In order to reduce the level of complexity and to study the problem of surface-based hybridization with point defects in more detail, we performed array based hybridization experiments in well controlled and simple situations. Results We performed microarray hybridization experiments with short 16 to 40 mer target and probe lengths (in situations without competitive hybridization) in order to systematically investigate the impact of point-mutations – varying defect type and position – on the oligonucleotide duplex binding affinity. The influence of single base bulges and single base MMs depends predominantly on position – it is largest in the middle of the strand. The position-dependent influence of base bulges is very similar to that of single base MMs, however certain bulges give rise to an unexpectedly high binding affinity. Besides the defect (MM or bulge) type, which is the second contribution in importance to hybridization affinity, there is also a sequence dependence, which extends beyond the defect next-neighbor and which is difficult to quantify. Direct comparison between binding affinities of DNA/DNA and RNA/DNA duplexes shows, that RNA/DNA purine-purine MMs are more discriminating than corresponding DNA/DNA MMs. In DNA/DNA MM discrimination the affected base pair (C·G vs. A·T) is the pertinent parameter. We attribute these differences to the different structures of the duplexes (A vs. B form). Conclusion We have shown that DNA microarrays can resolve even subtle changes in hybridization affinity for simple target mixtures. We have further shown that the impact of point defects on oligonucleotide stability can be broken down to a hierarchy of effects. In order to explain our observations we propose DNA molecular dynamics – in form of zipping of the oligonucleotide duplex – to play an important role. PMID:18477387

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.

Galectins are human milk glycan receptors

PubMed Central

Noll, Alexander J; Gourdine, Jean-Philippe; Yu, Ying; Lasanajak, Yi; Smith, David F; Cummings, Richard D

2016-01-01

The biological recognition of human milk glycans (HMGs) is poorly understood. Because HMGs are rich in galactose we explored whether they might interact with human galectins, which bind galactose-containing glycans and are highly expressed in epithelial cells and other cell types. We screened a number of human galectins for their binding to HMGs on a shotgun glycan microarray consisting of 247 HMGs derived from human milk, as well as to a defined HMG microarray. Recombinant human galectins (hGal)-1, -3, -4, -7, -8 and -9 bound selectively to glycans, with each galectin recognizing a relatively unique binding motif; by contrast hGal-2 did not recognize HMGs, but did bind to the human blood group A Type 2 determinants on other microarrays. Unlike other galectins, hGal-7 preferentially bound to glycans expressing a terminal Type 1 (Galβ1-3GlcNAc) sequence, a motif that had eluded detection on non-HMG glycan microarrays. Interactions with HMGs were confirmed in a solution setting by isothermal titration microcalorimetry and hapten inhibition experiments. These results demonstrate that galectins selectively bind to HMGs and suggest the possibility that galectin–HMG interactions may play a role in infant immunity. PMID:26747425

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

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments.

PubMed

Kitchen, Robert R; Sabine, Vicky S; Simen, Arthur A; Dixon, J Michael; Bartlett, John M S; Sims, Andrew H

2011-12-01

Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA. A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependent upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content. The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

PubMed Central

2011-01-01

Background Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA. Results A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependant upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content. Conclusions The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies. PMID:22133085

Gene response profiles for Daphnia pulex exposed to the environmental stressor cadmium reveals novel crustacean metallothioneins.

PubMed

Shaw, Joseph R; Colbourne, John K; Davey, Jennifer C; Glaholt, Stephen P; Hampton, Thomas H; Chen, Celia Y; Folt, Carol L; Hamilton, Joshua W

2007-12-21

Genomic research tools such as microarrays are proving to be important resources to study the complex regulation of genes that respond to environmental perturbations. A first generation cDNA microarray was developed for the environmental indicator species Daphnia pulex, to identify genes whose regulation is modulated following exposure to the metal stressor cadmium. Our experiments revealed interesting changes in gene transcription that suggest their biological roles and their potentially toxicological features in responding to this important environmental contaminant. Our microarray identified genes reported in the literature to be regulated in response to cadmium exposure, suggested functional attributes for genes that share no sequence similarity to proteins in the public databases, and pointed to genes that are likely members of expanded gene families in the Daphnia genome. Genes identified on the microarray also were associated with cadmium induced phenotypes and population-level outcomes that we experimentally determined. A subset of genes regulated in response to cadmium exposure was independently validated using quantitative-realtime (Q-RT)-PCR. These microarray studies led to the discovery of three genes coding for the metal detoxication protein metallothionein (MT). The gene structures and predicted translated sequences of D. pulex MTs clearly place them in this gene family. Yet, they share little homology with previously characterized MTs. The genomic information obtained from this study represents an important first step in characterizing microarray patterns that may be diagnostic to specific environmental contaminants and give insights into their toxicological mechanisms, while also providing a practical tool for evolutionary, ecological, and toxicological functional gene discovery studies. Advances in Daphnia genomics will enable the further development of this species as a model organism for the environmental sciences.

Gene response profiles for Daphnia pulex exposed to the environmental stressor cadmium reveals novel crustacean metallothioneins

PubMed Central

Shaw, Joseph R; Colbourne, John K; Davey, Jennifer C; Glaholt, Stephen P; Hampton, Thomas H; Chen, Celia Y; Folt, Carol L; Hamilton, Joshua W

2007-01-01

Background Genomic research tools such as microarrays are proving to be important resources to study the complex regulation of genes that respond to environmental perturbations. A first generation cDNA microarray was developed for the environmental indicator species Daphnia pulex, to identify genes whose regulation is modulated following exposure to the metal stressor cadmium. Our experiments revealed interesting changes in gene transcription that suggest their biological roles and their potentially toxicological features in responding to this important environmental contaminant. Results Our microarray identified genes reported in the literature to be regulated in response to cadmium exposure, suggested functional attributes for genes that share no sequence similarity to proteins in the public databases, and pointed to genes that are likely members of expanded gene families in the Daphnia genome. Genes identified on the microarray also were associated with cadmium induced phenotypes and population-level outcomes that we experimentally determined. A subset of genes regulated in response to cadmium exposure was independently validated using quantitative-realtime (Q-RT)-PCR. These microarray studies led to the discovery of three genes coding for the metal detoxication protein metallothionein (MT). The gene structures and predicted translated sequences of D. pulex MTs clearly place them in this gene family. Yet, they share little homology with previously characterized MTs. Conclusion The genomic information obtained from this study represents an important first step in characterizing microarray patterns that may be diagnostic to specific environmental contaminants and give insights into their toxicological mechanisms, while also providing a practical tool for evolutionary, ecological, and toxicological functional gene discovery studies. Advances in Daphnia genomics will enable the further development of this species as a model organism for the environmental sciences. PMID:18154678

SeeGH--a software tool for visualization of whole genome array comparative genomic hybridization data.

PubMed

Chi, Bryan; DeLeeuw, Ronald J; Coe, Bradley P; MacAulay, Calum; Lam, Wan L

2004-02-09

Array comparative genomic hybridization (CGH) is a technique which detects copy number differences in DNA segments. Complete sequencing of the human genome and the development of an array representing a tiling set of tens of thousands of DNA segments spanning the entire human genome has made high resolution copy number analysis throughout the genome possible. Since array CGH provides signal ratio for each DNA segment, visualization would require the reassembly of individual data points into chromosome profiles. We have developed a visualization tool for displaying whole genome array CGH data in the context of chromosomal location. SeeGH is an application that translates spot signal ratio data from array CGH experiments to displays of high resolution chromosome profiles. Data is imported from a simple tab delimited text file obtained from standard microarray image analysis software. SeeGH processes the signal ratio data and graphically displays it in a conventional CGH karyotype diagram with the added features of magnification and DNA segment annotation. In this process, SeeGH imports the data into a database, calculates the average ratio and standard deviation for each replicate spot, and links them to chromosome regions for graphical display. Once the data is displayed, users have the option of hiding or flagging DNA segments based on user defined criteria, and retrieve annotation information such as clone name, NCBI sequence accession number, ratio, base pair position on the chromosome, and standard deviation. SeeGH represents a novel software tool used to view and analyze array CGH data. The software gives users the ability to view the data in an overall genomic view as well as magnify specific chromosomal regions facilitating the precise localization of genetic alterations. SeeGH is easily installed and runs on Microsoft Windows 2000 or later environments.

Gene expression responses of paper birch to elevated O3 and CO2 during leaf maturation and senescence

NASA Astrophysics Data System (ADS)

Kontunen-Soppela, S.; Parviainen, J.; Ruhanen, H.; Brosché, M.; Keinanen, M.; Thakur, R. C.; Kolehmainen, M.; Kangasjarvi, J.; Oksanen, E.; Karnosky, D. F.; Vapaavuori, E.

2009-12-01

Forest trees are exposed to increasing concentrations of O3 and CO2 simultaneously. The rise of concentration in these gases causes changes in the gene expression of trees, which can be small in acclimated trees, but yet pivotal for the metabolism of the trees. We have studied the response of paper birch (Betula papyrifera) leaf gene expression to elevated O3 and CO2 concentrations during leaf maturation and senescence. The hypotheses were:(1) Elevated O3 induces oxidative stress in leaves. During long O3-exposure repair mechanisms are activated. Because chemical defense requires energy and carbon uptake is reduced, leaf senescence is activated earlier. Alternatively, the senescence-associated processes, remobilization and storage of carbohydrates and nutrients, may not be completed. (2) In the combination of elevated CO2+O3, the O3-caused damages are not seen or they are smaller, due to closure of the stomata under elevated CO2 and decreased O3 uptake by the leaves. On the other hand, elevated CO2 may provide energy and increase defense chemicals, enabling leaves to repair the O3-caused damages. Gene expression responses of paper birch leaves to elevated O3 and CO2 were studied with microarray analyses. Samples were collected from the long-term O3 and CO2 fumigation experiment Aspen FACE in Rhinelander, WI, USA (http://aspenface.mtu.edu/). The site contains 12 FACE rings receiving CO2, O3, CO2+O3, and ambient air (controls). Birches have been exposed to elevated CO2 (550ppm) and O3 (1.5X ambient) since 1998. Leaf samples were collected in July, August and September 2004. The cDNA-microarrays used for hybridizations consisted of Populus euphratica ESTs representing ca 6500 different genes. In order to detect similar gene expression patterns within samplings and treatments, the microarray data was analyzed with multivariate methods; clustering with Self-Organizing Map, finding optimal cluster grouping by K-means clustering and visualizing the results with Sammon's mapping. Most of the alterations in the gene expression in comparison to ambient rings were caused by O3, alone and in combination with elevated CO2. O3 reduced photosynthesis and carbon assimilation and induced defense to oxidative stress resulting in earlier leaf senescence. Transport and proteolysis gene expressions were activated, indicating that at least some remobilization of nutrients for storage was completed. The combined CO2+O3 treatment resembled the O3 treatment, indicating that elevated CO2 is not able to totally alleviate the harmful effects of elevated O3. Some specific gene expression changes in the combined O3+CO2 treatment showed that experiments with O3 or CO2-exposure alone are not sufficient to predict plant responses to these gases together, and that field experiments with multiple variables are essential in order to understand responses to future environmental conditions.

Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases

PubMed Central

2011-01-01

Background Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions. Methods An application-specific ontology was developed for managing TMA and DNA microarray databases by semantic web technologies. Data were represented as Resource Description Framework (RDF) according to the framework of the ontology. Applications for hypothesis testing (Xperanto-RDF) for TMA data were designed and implemented by (1) formulating the syntactic and semantic structures of the hypotheses derived from TMA experiments, (2) formulating SPARQLs to reflect the semantic structures of the hypotheses, and (3) performing statistical test with the result sets returned by the SPARQLs. Results When a user designs a hypothesis in Xperanto-RDF and submits it, the hypothesis can be tested against TMA experimental data stored in Xperanto-RDF. When we evaluated four previously validated hypotheses as an illustration, all the hypotheses were supported by Xperanto-RDF. Conclusions We demonstrated the utility of high throughput biological hypothesis testing. We believe that preliminary investigation before performing highly controlled experiment can be benefited. PMID:21342584

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.

[DNA microarray reveals changes in gene expression of endothelial cells under shear stress].

PubMed

Cheng, Min; Zhang, Wensheng; Chen, Huaiqing; Wu, Wenchao; Huang, Hua

2004-04-01

cDNA microarray technology is used as a powerful tool for rapid, comprehensive, and quantitative analysis of gene profiles of cultured human umbilical vein endothelial cells(HUVECs) in the normal static group and the shear stressed (4.20 dyne/cm2, 2 h) group. The total RNA from normal static cultured HUVECs was labeled by Cy3-dCTP, and total RNA of HUVECs from the paired shear stressed experiment was labeled by Cy5-dCTP. The expression ratios reported are the average from the two separate experiments. After bioinformatics analysis, we identified a total of 108 genes (approximately 0.026%) revealing differential expression. Of these 53 genes expressions were up-regulated, the most enhanced ones being human homolog of yeast IPP isomerase, human low density lipoprotein receptor gene, Squalene epoxidase gene, 7-dehydrocholesterol reductase, and 55 were down-regulated, the most decreased ones being heat shock 70 kD protein 1, TCB gene encoding cytosolic thyroid hormone-binding protein in HUVECs exposed to low shear stress. These results indicate that the cDNA microarray technique is effective in screening the differentially expressed genes in endothelial cells induced by various experimental conditions and the data may serve as stimuli to further researches.

Dye bias correction in dual-labeled cDNA microarray gene expression measurements.

PubMed Central

Rosenzweig, Barry A; Pine, P Scott; Domon, Olen E; Morris, Suzanne M; Chen, James J; Sistare, Frank D

2004-01-01

A significant limitation to the analytical accuracy and precision of dual-labeled spotted cDNA microarrays is the signal error due to dye bias. Transcript-dependent dye bias may be due to gene-specific differences of incorporation of two distinctly different chemical dyes and the resultant differential hybridization efficiencies of these two chemically different targets for the same probe. Several approaches were used to assess and minimize the effects of dye bias on fluorescent hybridization signals and maximize the experimental design efficiency of a cell culture experiment. Dye bias was measured at the individual transcript level within each batch of simultaneously processed arrays by replicate dual-labeled split-control sample hybridizations and accounted for a significant component of fluorescent signal differences. This transcript-dependent dye bias alone could introduce unacceptably high numbers of both false-positive and false-negative signals. We found that within a given set of concurrently processed hybridizations, the bias is remarkably consistent and therefore measurable and correctable. The additional microarrays and reagents required for paired technical replicate dye-swap corrections commonly performed to control for dye bias could be costly to end users. Incorporating split-control microarrays within a set of concurrently processed hybridizations to specifically measure dye bias can eliminate the need for technical dye swap replicates and reduce microarray and reagent costs while maintaining experimental accuracy and technical precision. These data support a practical and more efficient experimental design to measure and mathematically correct for dye bias. PMID:15033598

sigReannot: an oligo-set re-annotation pipeline based on similarities with the Ensembl transcripts and Unigene clusters.

PubMed

Casel, Pierrot; Moreews, François; Lagarrigue, Sandrine; Klopp, Christophe

2009-07-16

Microarray is a powerful technology enabling to monitor tens of thousands of genes in a single experiment. Most microarrays are now using oligo-sets. The design of the oligo-nucleotides is time consuming and error prone. Genome wide microarray oligo-sets are designed using as large a set of transcripts as possible in order to monitor as many genes as possible. Depending on the genome sequencing state and on the assembly state the knowledge of the existing transcripts can be very different. This knowledge evolves with the different genome builds and gene builds. Once the design is done the microarrays are often used for several years. The biologists working in EADGENE expressed the need of up-to-dated annotation files for the oligo-sets they share including information about the orthologous genes of model species, the Gene Ontology, the corresponding pathways and the chromosomal location. The results of SigReannot on a chicken micro-array used in the EADGENE project compared to the initial annotations show that 23% of the oligo-nucleotide gene annotations were not confirmed, 2% were modified and 1% were added. The interest of this up-to-date annotation procedure is demonstrated through the analysis of real data previously published. SigReannot uses the oligo-nucleotide design procedure criteria to validate the probe-gene link and the Ensembl transcripts as reference for annotation. It therefore produces a high quality annotation based on reference gene sets.

Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles.

PubMed

Kitchen, Robert R; Sabine, Vicky S; Sims, Andrew H; Macaskill, E Jane; Renshaw, Lorna; Thomas, Jeremy S; van Hemert, Jano I; Dixon, J Michael; Bartlett, John M S

2010-02-24

Microarray technology is a popular means of producing whole genome transcriptional profiles, however high cost and scarcity of mRNA has led many studies to be conducted based on the analysis of single samples. We exploit the design of the Illumina platform, specifically multiple arrays on each chip, to evaluate intra-experiment technical variation using repeated hybridisations of universal human reference RNA (UHRR) and duplicate hybridisations of primary breast tumour samples from a clinical study. A clear batch-specific bias was detected in the measured expressions of both the UHRR and clinical samples. This bias was found to persist following standard microarray normalisation techniques. However, when mean-centering or empirical Bayes batch-correction methods (ComBat) were applied to the data, inter-batch variation in the UHRR and clinical samples were greatly reduced. Correlation between replicate UHRR samples improved by two orders of magnitude following batch-correction using ComBat (ranging from 0.9833-0.9991 to 0.9997-0.9999) and increased the consistency of the gene-lists from the duplicate clinical samples, from 11.6% in quantile normalised data to 66.4% in batch-corrected data. The use of UHRR as an inter-batch calibrator provided a small additional benefit when used in conjunction with ComBat, further increasing the agreement between the two gene-lists, up to 74.1%. In the interests of practicalities and cost, these results suggest that single samples can generate reliable data, but only after careful compensation for technical bias in the experiment. We recommend that investigators appreciate the propensity for such variation in the design stages of a microarray experiment and that the use of suitable correction methods become routine during the statistical analysis of the data.

Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles

PubMed Central

2010-01-01

Background Microarray technology is a popular means of producing whole genome transcriptional profiles, however high cost and scarcity of mRNA has led many studies to be conducted based on the analysis of single samples. We exploit the design of the Illumina platform, specifically multiple arrays on each chip, to evaluate intra-experiment technical variation using repeated hybridisations of universal human reference RNA (UHRR) and duplicate hybridisations of primary breast tumour samples from a clinical study. Results A clear batch-specific bias was detected in the measured expressions of both the UHRR and clinical samples. This bias was found to persist following standard microarray normalisation techniques. However, when mean-centering or empirical Bayes batch-correction methods (ComBat) were applied to the data, inter-batch variation in the UHRR and clinical samples were greatly reduced. Correlation between replicate UHRR samples improved by two orders of magnitude following batch-correction using ComBat (ranging from 0.9833-0.9991 to 0.9997-0.9999) and increased the consistency of the gene-lists from the duplicate clinical samples, from 11.6% in quantile normalised data to 66.4% in batch-corrected data. The use of UHRR as an inter-batch calibrator provided a small additional benefit when used in conjunction with ComBat, further increasing the agreement between the two gene-lists, up to 74.1%. Conclusion In the interests of practicalities and cost, these results suggest that single samples can generate reliable data, but only after careful compensation for technical bias in the experiment. We recommend that investigators appreciate the propensity for such variation in the design stages of a microarray experiment and that the use of suitable correction methods become routine during the statistical analysis of the data. PMID:20181233

Microarray missing data imputation based on a set theoretic framework and biological knowledge.

PubMed

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2006-01-01

Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods.

MCAW-DB: A glycan profile database capturing the ambiguity of glycan recognition patterns.

PubMed

Hosoda, Masae; Takahashi, Yushi; Shiota, Masaaki; Shinmachi, Daisuke; Inomoto, Renji; Higashimoto, Shinichi; Aoki-Kinoshita, Kiyoko F

2018-05-11

Glycan-binding protein (GBP) interaction experiments, such as glycan microarrays, are often used to understand glycan recognition patterns. However, oftentimes the interpretation of glycan array experimental data makes it difficult to identify discrete GBP binding patterns due to their ambiguity. It is known that lectins, for example, are non-specific in their binding affinities; the same lectin can bind to different monosaccharides or even different glycan structures. In bioinformatics, several tools to mine the data generated from these sorts of experiments have been developed. These tools take a library of predefined motifs, which are commonly-found glycan patterns such as sialyl-Lewis X, and attempt to identify the motif(s) that are specific to the GBP being analyzed. In our previous work, as opposed to using predefined motifs, we developed the Multiple Carbohydrate Alignment with Weights (MCAW) tool to visualize the state of the glycans being recognized by the GBP under analysis. We previously reported on the effectiveness of our tool and algorithm by analyzing several glycan array datasets from the Consortium of Functional Glycomics (CFG). In this work, we report on our analysis of 1081 data sets which we collected from the CFG, the results of which we have made publicly and freely available as a database called MCAW-DB. We introduce this database, its usage and describe several analysis results. We show how MCAW-DB can be used to analyze glycan-binding patterns of GBPs amidst their ambiguity. For example, the visualization of glycan-binding patterns in MCAW-DB show how they correlate with the concentrations of the samples used in the array experiments. Using MCAW-DB, the patterns of glycans found to bind to various GBP-glycan binding proteins are visualized, indicating the binding "environment" of the glycans. Thus, the ambiguity of glycan recognition is numerically represented, along with the patterns of monosaccharides surrounding the binding region. The profiles in MCAW-DB could potentially be used as predictors of affinity of unknown or novel glycans to particular GBPs by comparing how well they match the existing profiles for those GBPs. Moreover, as the glycan profiles of diseased tissues become available, glycan alignments could also be used to identify glycan biomarkers unique to that tissue. Databases of these alignments may be of great use for drug discovery. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Unraveling transcriptional control and cis-regulatory codes using the software suite GeneACT

PubMed Central

Cheung, Tom Hiu; Kwan, Yin Lam; Hamady, Micah; Liu, Xuedong

2006-01-01

Deciphering gene regulatory networks requires the systematic identification of functional cis-acting regulatory elements. We present a suite of web-based bioinformatics tools, called GeneACT , that can rapidly detect evolutionarily conserved transcription factor binding sites or microRNA target sites that are either unique or over-represented in differentially expressed genes from DNA microarray data. GeneACT provides graphic visualization and extraction of common regulatory sequence elements in the promoters and 3'-untranslated regions that are conserved across multiple mammalian species. PMID:17064417

Targeted exploration and analysis of large cross-platform human transcriptomic compendia

PubMed Central

Zhu, Qian; Wong, Aaron K; Krishnan, Arjun; Aure, Miriam R; Tadych, Alicja; Zhang, Ran; Corney, David C; Greene, Casey S; Bongo, Lars A; Kristensen, Vessela N; Charikar, Moses; Li, Kai; Troyanskaya, Olga G.

2016-01-01

We present SEEK (http://seek.princeton.edu), a query-based search engine across very large transcriptomic data collections, including thousands of human data sets from almost 50 microarray and next-generation sequencing platforms. SEEK uses a novel query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify query-coregulated genes, pathways, and processes. SEEK provides cross-platform handling, multi-gene query search, iterative metadata-based search refinement, and extensive visualization-based analysis options. PMID:25581801

Spectral embedding finds meaningful (relevant) structure in image and microarray data

PubMed Central

Higgs, Brandon W; Weller, Jennifer; Solka, Jeffrey L

2006-01-01

Background Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. Results We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. Conclusion Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology. PMID:16483359

Defining the interaction of human soluble lectin ZG16p and mycobacterial phosphatidylinositol mannosides

PubMed Central

Ikeda, Akemi; Kojima-Aikawa, Kyoko; Taniguchi, Naoyuki; Varón Silva, Daniel; Feizi, Ten; Seeberger, Peter H.; Yamaguchi, Yoshiki

2018-01-01

ZG16p is a soluble mammalian lectin that interacts with mannose and heparan sulfate. Here we describe detailed analyses of the interactions of human ZG16p with mycobacterial phosphatidylinositol mannosides (PIMs), using glycan microarray and NMR. Pathogen-related glycan microarray analysis identified phosphatidylinositol mono- and di-mannosides (PIM1 and PIM2) as novel ligand candidates of ZG16p. Saturation Transfer Difference (STD) NMR and transferred NOE experiments with chemically synthesized PIM glycans indicate that PIMs preferentially interacts with ZG16p using the mannose residues. Binding site of PIMs is identified by chemical shift perturbation experiments using uniformly 15N-labeled ZG16p. NMR results with docking simulations suggest a binding mode of ZG16p and PIM glycan, which would help to consider the physiological role of ZG16p. PMID:25919894

MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data

PubMed Central

Gutiérrez-Avilés, David; Rubio-Escudero, Cristina

2015-01-01

Microarray technology is highly used in biological research environments due to its ability to monitor the RNA concentration levels. The analysis of the data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior. Biclustering relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. Triclustering appears for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. These triclusters provide hidden information in the form of behavior patterns from temporal experiments with microarrays relating subsets of genes, experimental conditions, and time points. We present an evaluation measure for triclusters called Multi Slope Measure, based on the similarity among the angles of the slopes formed by each profile formed by the genes, conditions, and times of the tricluster. PMID:26124630

TIPMaP: a web server to establish transcript isoform profiles from reliable microarray probes.

PubMed

Chitturi, Neelima; Balagannavar, Govindkumar; Chandrashekar, Darshan S; Abinaya, Sadashivam; Srini, Vasan S; Acharya, Kshitish K

2013-12-27

Standard 3' Affymetrix gene expression arrays have contributed a significantly higher volume of existing gene expression data than other microarray platforms. These arrays were designed to identify differentially expressed genes, but not their alternatively spliced transcript forms. No resource can currently identify expression pattern of specific mRNA forms using these microarray data, even though it is possible to do this. We report a web server for expression profiling of alternatively spliced transcripts using microarray data sets from 31 standard 3' Affymetrix arrays for human, mouse and rat species. The tool has been experimentally validated for mRNAs transcribed or not-detected in a human disease condition (non-obstructive azoospermia, a male infertility condition). About 4000 gene expression datasets were downloaded from a public repository. 'Good probes' with complete coverage and identity to latest reference transcript sequences were first identified. Using them, 'Transcript specific probe-clusters' were derived for each platform and used to identify expression status of possible transcripts. The web server can lead the user to datasets corresponding to specific tissues, conditions via identifiers of the microarray studies or hybridizations, keywords, official gene symbols or reference transcript identifiers. It can identify, in the tissues and conditions of interest, about 40% of known transcripts as 'transcribed', 'not-detected' or 'differentially regulated'. Corresponding additional information for probes, genes, transcripts and proteins can be viewed too. We identified the expression of transcripts in a specific clinical condition and validated a few of these transcripts by experiments (using reverse transcription followed by polymerase chain reaction). The experimental observations indicated higher agreements with the web server results, than contradictions. The tool is accessible at http://resource.ibab.ac.in/TIPMaP. The newly developed online tool forms a reliable means for identification of alternatively spliced transcript-isoforms that may be differentially expressed in various tissues, cell types or physiological conditions. Thus, by making better use of existing data, TIPMaP avoids the dependence on precious tissue-samples, in experiments with a goal to establish expression profiles of alternative splice forms--at least in some cases.

Dielectrophoretic manipulation and separation of microparticles using microarray dot electrodes.

PubMed

Yafouz, Bashar; Kadri, Nahrizul Adib; Ibrahim, Fatimah

2014-04-03

This paper introduces a dielectrophoretic system for the manipulation and separation of microparticles. The system is composed of five layers and utilizes microarray dot electrodes. We validated our system by conducting size-dependent manipulation and separation experiments on 1, 5 and 15 μm polystyrene particles. Our findings confirm the capability of the proposed device to rapidly and efficiently manipulate and separate microparticles of various dimensions, utilizing positive and negative dielectrophoresis (DEP) effects. Larger size particles were repelled and concentrated in the center of the dot by negative DEP, while the smaller sizes were attracted and collected by the edge of the dot by positive DEP.

Sequence verification as quality-control step for production of cDNA microarrays.

PubMed

Taylor, E; Cogdell, D; Coombes, K; Hu, L; Ramdas, L; Tabor, A; Hamilton, S; Zhang, W

2001-07-01

To generate cDNA arrays in our core laboratory, we amplified about 2300 PCR products from a human, sequence-verified cDNA clone library. As a quality-control step, we sequenced the PCR products immediately before printing. The sequence information was used to search the GenBank database to confirm the identities. Although these clones were previously sequence verified by the company, we found that only 79% of the clones matched the original database after handling. Our experience strongly indicates the necessity to sequence verify the clones at the final stage before printing on microarray slides and to modify the gene list accordingly.

Comparative transcriptional profiling of human Merkel cells and Merkel cell carcinoma.

PubMed

Mouchet, Nicolas; Coquart, Nolwenn; Lebonvallet, Nicolas; Le Gall-Ianotto, Christelle; Mogha, Ariane; Fautrel, Alain; Boulais, Nicholas; Dréno, Brigitte; Martin, Ludovic; Hu, Weiguo; Galibert, Marie-Dominique; Misery, Laurent

2014-12-01

Merkel cell carcinoma is believed to be derived from Merkel cells after infection by Merkel cell polyomavirus (MCPyV) and other poorly understood events. Transcriptional profiling using cDNA microarrays was performed on cells from MCPy-negative and MCPy-positive Merkel cell carcinomas and isolated normal Merkel cells. This microarray revealed numerous significantly upregulated genes and some downregulated genes. The extensive list of genes that were identified in these experiments provides a large body of potentially valuable information of Merkel cell carcinoma carcinogenesis and could represent a source of potential targets for cancer therapy. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Spotting effect in microarray experiments

PubMed Central

Mary-Huard, Tristan; Daudin, Jean-Jacques; Robin, Stéphane; Bitton, Frédérique; Cabannes, Eric; Hilson, Pierre

2004-01-01

Background Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects data obtained with Cy3/Cy5 spotted glass arrays. It yields a periodic pattern altering both signal (Cy3/Cy5 ratio) and intensity across the array. Results Using the variogram, a geostatistical tool, we characterized the observed variability, called here the spotting effect because it most probably arises during steps in the array printing procedure. Conclusions The spotting effect is not appropriately corrected by current normalization methods, even by those addressing spatial variability. Importantly, the spotting effect may alter differential and clustering analysis. PMID:15151695

MobilomeFINDER: web-based tools for in silico and experimental discovery of bacterial genomic islands

PubMed Central

Ou, Hong-Yu; He, Xinyi; Harrison, Ewan M.; Kulasekara, Bridget R.; Thani, Ali Bin; Kadioglu, Aras; Lory, Stephen; Hinton, Jay C. D.; Barer, Michael R.; Rajakumar, Kumar

2007-01-01

MobilomeFINDER (http://mml.sjtu.edu.cn/MobilomeFINDER) is an interactive online tool that facilitates bacterial genomic island or ‘mobile genome’ (mobilome) discovery; it integrates the ArrayOme and tRNAcc software packages. ArrayOme utilizes a microarray-derived comparative genomic hybridization input data set to generate ‘inferred contigs’ produced by merging adjacent genes classified as ‘present’. Collectively these ‘fragments’ represent a hypothetical ‘microarray-visualized genome (MVG)’. ArrayOme permits recognition of discordances between physical genome and MVG sizes, thereby enabling identification of strains rich in microarray-elusive novel genes. Individual tRNAcc tools facilitate automated identification of genomic islands by comparative analysis of the contents and contexts of tRNA sites and other integration hotspots in closely related sequenced genomes. Accessory tools facilitate design of hotspot-flanking primers for in silico and/or wet-science-based interrogation of cognate loci in unsequenced strains and analysis of islands for features suggestive of foreign origins; island-specific and genome-contextual features are tabulated and represented in schematic and graphical forms. To date we have used MobilomeFINDER to analyse several Enterobacteriaceae, Pseudomonas aeruginosa and Streptococcus suis genomes. MobilomeFINDER enables high-throughput island identification and characterization through increased exploitation of emerging sequence data and PCR-based profiling of unsequenced test strains; subsequent targeted yeast recombination-based capture permits full-length sequencing and detailed functional studies of novel genomic islands. PMID:17537813

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

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

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).

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.

Development of a Sensitive Microarray Platform for the Ranking of Galectin Inhibitors: Identification of a Selective Galectin-3 Inhibitor.

PubMed

Dion, Johann; Advedissian, Tamara; Storozhylova, Nataliya; Dahbi, Samir; Lambert, Annie; Deshayes, Frédérique; Viguier, Mireille; Tellier, Charles; Poirier, Françoise; Téletchéa, Stéphane; Dussouy, Christophe; Tateno, Hiroaki; Hirabayashi, Jun; Grandjean, Cyrille

2017-12-14

Glycan microarrays are useful tools for lectin glycan profiling. The use of a glycan microarray based on evanescent-field fluorescence detection was herein further extended to the screening of lectin inhibitors in competitive experiments. The efficacy of this approach was tested with 2/3'-mono- and 2,3'-diaromatic type II lactosamine derivatives and galectins as targets and was validated by comparison with fluorescence anisotropy proposed as an orthogonal protein interaction measurement technique. We showed that subtle differences in the architecture of the inhibitor could be sensed that pointed out the preference of galectin-3 for 2'-arylamido derivatives over ureas, thioureas, and amines and that of galectin-7 for derivatives bearing an α substituent at the anomeric position of glucosamine. We eventually identified a diaromatic oxazoline as a highly specific inhibitor of galectin-3 versus galectin-1 and galectin-7. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Use of Bayesian Networks to Probabilistically Model and Improve the Likelihood of Validation of Microarray Findings by RT-PCR

PubMed Central

English, Sangeeta B.; Shih, Shou-Ching; Ramoni, Marco F.; Smith, Lois E.; Butte, Atul J.

2014-01-01

Though genome-wide technologies, such as microarrays, are widely used, data from these methods are considered noisy; there is still varied success in downstream biological validation. We report a method that increases the likelihood of successfully validating microarray findings using real time RT-PCR, including genes at low expression levels and with small differences. We use a Bayesian network to identify the most relevant sources of noise based on the successes and failures in validation for an initial set of selected genes, and then improve our subsequent selection of genes for validation based on eliminating these sources of noise. The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene. We show how the method can significantly increase validation success rates. In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation. PMID:18790084

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

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.

PubMed

Vaas, Lea A I; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed '-omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics

PubMed Central

Vaas, Lea A. I.; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

Background The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. Methodology The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. Conclusions We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data. PMID:22536335

A revised design for microarray experiments to account for experimental noise and uncertainty of probe response.

PubMed

Pozhitkov, Alex E; Noble, Peter A; Bryk, Jarosław; Tautz, Diethard

2014-01-01

Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.

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.

MIPHENO: Data normalization for high throughput metabolic analysis.

EPA Science Inventory

High throughput methodologies such as microarrays, mass spectrometry and plate-based small molecule screens are increasingly used to facilitate discoveries from gene function to drug candidate identification. These large-scale experiments are typically carried out over the course...

A NASBA on microgel-tethered molecular-beacon microarray for real-time microbial molecular diagnostics.

PubMed

Ma, Y; Dai, X; Hong, T; Munk, G B; Libera, M

2016-12-19

Despite their many advantages and successes, molecular beacon (MB) hybridization probes have not been extensively used in microarray formats because of the complicating probe-substrate interactions that increase the background intensity. We have previously shown that tethering to surface-patterned microgels is an effective means for localizing MB probes to specific surface locations in a microarray format while simultaneously maintaining them in as water-like an environment as possible and minimizing probe-surface interactions. Here we extend this approach to include both real-time detection together with integrated NASBA amplification. We fabricate small (∼250 μm × 250 μm) simplex, duplex, and five-plex assays with microarray spots of controllable size (∼20 μm diameter), position, and shape to detect bacteria and fungi in a bloodstream-infection model. The targets, primers, and microgel-tethered probes can be combined in a single isothermal reaction chamber with no post-amplification labelling. We extract total RNA from clinical blood samples and differentiate between Gram-positive and Gram-negative bloodstream infection in a duplex assay to detect RNA- amplicons. The sensitivity based on our current protocols in a simplex assay to detect specific ribosomal RNA sequences within total RNA extracted from S. aureus and E. coli cultures corresponds to tens of bacteria per ml. We furthermore show that the platform can detect RNA- amplicons from synthetic target DNA with 1 fM sensitivity in sample volumes that contain about 12 000 DNA molecules. These experiments demonstrate an alternative approach that can enable rapid and real-time microarray-based molecular diagnostics.

Dynamic, electronically switchable surfaces for membrane protein microarrays.

PubMed

Tang, C S; Dusseiller, M; Makohliso, S; Heuschkel, M; Sharma, S; Keller, B; Vörös, J

2006-02-01

Microarray technology is a powerful tool that provides a high throughput of bioanalytical information within a single experiment. These miniaturized and parallelized binding assays are highly sensitive and have found widespread popularity especially during the genomic era. However, as drug diagnostics studies are often targeted at membrane proteins, the current arraying technologies are ill-equipped to handle the fragile nature of the protein molecules. In addition, to understand the complex structure and functions of proteins, different strategies to immobilize the probe molecules selectively onto a platform for protein microarray are required. We propose a novel approach to create a (membrane) protein microarray by using an indium tin oxide (ITO) microelectrode array with an electronic multiplexing capability. A polycationic, protein- and vesicle-resistant copolymer, poly(l-lysine)-grafted-poly(ethylene glycol) (PLL-g-PEG), is exposed to and adsorbed uniformly onto the microelectrode array, as a passivating adlayer. An electronic stimulation is then applied onto the individual ITO microelectrodes resulting in the localized release of the polymer thus revealing a bare ITO surface. Different polymer and biological moieties are specifically immobilized onto the activated ITO microelectrodes while the other regions remain protein-resistant as they are unaffected by the induced electrical potential. The desorption process of the PLL-g-PEG is observed to be highly selective, rapid, and reversible without compromising on the integrity and performance of the conductive ITO microelectrodes. As such, we have successfully created a stable and heterogeneous microarray of biomolecules by using selective electronic addressing on ITO microelectrodes. Both pharmaceutical diagnostics and biomedical technology are expected to benefit directly from this unique method.

Calling Biomarkers in Milk Using a Protein Microarray on Your Smartphone

PubMed Central

Ludwig, Susann K. J.; Tokarski, Christian; Lang, Stefan N.; van Ginkel, Leendert A.; Zhu, Hongying; Ozcan, Aydogan; Nielen, Michel W. F.

2015-01-01

Here we present the concept of a protein microarray-based fluorescence immunoassay for multiple biomarker detection in milk extracts by an ordinary smartphone. A multiplex immunoassay was designed on a microarray chip, having built-in positive and negative quality controls. After the immunoassay procedure, the 48 microspots were labelled with Quantum Dots (QD) depending on the protein biomarker levels in the sample. QD-fluorescence was subsequently detected by the smartphone camera under UV light excitation from LEDs embedded in a simple 3D-printed opto-mechanical smartphone attachment. The somewhat aberrant images obtained under such conditions, were corrected by newly developed Android-based software on the same smartphone, and protein biomarker profiles were calculated. The indirect detection of recombinant bovine somatotropin (rbST) in milk extracts based on altered biomarker profile of anti-rbST antibodies was selected as a real-life challenge. RbST-treated and untreated cows clearly showed reproducible treatment-dependent biomarker profiles in milk, in excellent agreement with results from a flow cytometer reference method. In a pilot experiment, anti-rbST antibody detection was multiplexed with the detection of another rbST-dependent biomarker, insulin-like growth factor 1 (IGF-1). Milk extract IGF-1 levels were found to be increased after rbST treatment and correlated with the results obtained from the reference method. These data clearly demonstrate the potential of the portable protein microarray concept towards simultaneous detection of multiple biomarkers. We envisage broad application of this ‘protein microarray on a smartphone’-concept for on-site testing, e.g., in food safety, environment and health monitoring. PMID:26308444

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

Extraction and labeling methods for microarrays using small amounts of plant tissue.

PubMed

Stimpson, Alexander J; Pereira, Rhea S; Kiss, John Z; Correll, Melanie J

2009-03-01

Procedures were developed to maximize the yield of high-quality RNA from small amounts of plant biomass for microarrays. Two disruption techniques (bead milling and pestle and mortar) were compared for the yield and the quality of RNA extracted from 1-week-old Arabidopsis thaliana seedlings (approximately 0.5-30 mg total biomass). The pestle and mortar method of extraction showed enhanced RNA quality at the smaller biomass samples compared with the bead milling technique, although the quality in the bead milling could be improved with additional cooling steps. The RNA extracted from the pestle and mortar technique was further tested to determine if the small quantity of RNA (500 ng-7 microg) was appropriate for microarray analyses. A new method of low-quantity RNA labeling for microarrays (NuGEN Technologies, Inc.) was used on five 7-day-old seedlings (approximately 2.5 mg fresh weight total) of Arabidopsis that were grown in the dark and exposed to 1 h of red light or continued dark. Microarray analyses were performed on a small plant sample (five seedlings; approximately 2.5 mg) using these methods and compared with extractions performed with larger biomass samples (approximately 500 roots). Many well-known light-regulated genes between the small plant samples and the larger biomass samples overlapped in expression changes, and the relative expression levels of selected genes were confirmed with quantitative real-time polymerase chain reaction, suggesting that these methods can be used for plant experiments where the biomass is extremely limited (i.e. spaceflight studies).

Transcriptome sequencing and microarray development for the woodrat (Neotoma spp.): custom genetic tools for exploring herbivore ecology.

PubMed

Malenke, J R; Milash, B; Miller, A W; Dearing, M D

2013-07-01

Massively parallel sequencing has enabled the creation of novel, in-depth genetic tools for nonmodel, ecologically important organisms. We present the de novo transcriptome sequencing, analysis and microarray development for a vertebrate herbivore, the woodrat (Neotoma spp.). This genus is of ecological and evolutionary interest, especially with respect to ingestion and hepatic metabolism of potentially toxic plant secondary compounds. We generated a liver transcriptome of the desert woodrat (Neotoma lepida) using the Roche 454 platform. The assembled contigs were well annotated using rodent references (99.7% annotation), and biotransformation function was reflected in the gene ontology. The transcriptome was used to develop a custom microarray (eArray, Agilent). We tested the microarray with three experiments: one across species with similar habitat (thus, dietary) niches, one across species with different habitat niches and one across populations within a species. The resulting one-colour arrays had high technical and biological quality. Probes designed from the woodrat transcriptome performed significantly better than functionally similar probes from the Norway rat (Rattus norvegicus). There were a multitude of expression differences across the woodrat treatments, many of which related to biotransformation processes and activities. The pattern and function of the differences indicate shared ecological pressures, and not merely phylogenetic distance, play an important role in shaping gene expression profiles of woodrat species and populations. The quality and functionality of the woodrat transcriptome and custom microarray suggest these tools will be valuable for expanding the scope of herbivore biology, as well as the exploration of conceptual topics in ecology. © 2013 John Wiley & Sons Ltd.

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

'FloraArray' for screening of specific DNA probes representing the characteristics of a certain microbial community.

PubMed

Yokoi, Takahide; Kaku, Yoshiko; Suzuki, Hiroyuki; Ohta, Masayuki; Ikuta, Hajime; Isaka, Kazuichi; Sumino, Tatsuo; Wagatsuma, Masako

2007-08-01

To investigate uncharacterized microbial communities, a custom DNA microarray named 'FloraArray' was developed for screening specific probes that would represent the characteristics of a microbial community. The array was prepared by spotting 2000 plasmid DNAs from a genomic shotgun library of a sludge sample on a DNA microarray. By comparative hybridization of the array with two different samples of genomic DNA, one from the activated sludge and the other from a nonactivated sludge sample of an anaerobic ammonium oxidation (anammox) bacterial community, specific spots were visualized as a definite fluctuating profile in an MA (differential intensity ratio vs. spot intensity) plot. About 300 spots of the array accounted for the candidate probes to represent anammox reaction of the activated sludge. After sequence analysis of the probes and examination of the results of blastn searches against the reported anammox reference sequence, complete matches were found for 161 probes (58.3%) and >90% matches were found for 242 probes (87.1%). These results demonstrate that 'FloraArray' could be a useful tool for screening specific DNA molecules of unknown microbial communities.

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.

Relevance of TNBS-Colitis in Rats: A Methodological Study with Endoscopic, Histologic and Transcriptomic Characterization and Correlation to IBD

PubMed Central

Brenna, Øystein; Furnes, Marianne W.; Drozdov, Ignat; van Beelen Granlund, Atle; Flatberg, Arnar; Sandvik, Arne K.; Zwiggelaar, Rosalie T. M.; Mårvik, Ronald; Nordrum, Ivar S.; Kidd, Mark; Gustafsson, Björn I.

2013-01-01

Background Rectal instillation of trinitrobenzene sulphonic acid (TNBS) in ethanol is an established model for inflammatory bowel disease (IBD). We aimed to 1) set up a TNBS-colitis protocol resulting in an endoscopic and histologic picture resembling IBD, 2) study the correlation between endoscopic, histologic and gene expression alterations at different time points after colitis induction, and 3) compare rat and human IBD mucosal transcriptomic data to evaluate whether TNBS-colitis is an appropriate model of IBD. Methodology/Principal Findings Five female Sprague Daley rats received TNBS diluted in 50% ethanol (18 mg/0.6 ml) rectally. The rats underwent colonoscopy with biopsy at different time points. RNA was extracted from rat biopsies and microarray was performed. PCR and in situ hybridization (ISH) were done for validation of microarray results. Rat microarray profiles were compared to human IBD expression profiles (25 ulcerative colitis Endoscopic score demonstrated mild to moderate colitis after three and seven days, but declined after twelve days. Histologic changes corresponded with the endoscopic appearance. Over-represented Gene Ontology Biological Processes included: Cell Adhesion, Immune Response, Lipid Metabolic Process, and Tissue Regeneration. IL-1α, IL-1β, TLR2, TLR4, PRNP were all significantly up-regulated, while PPARγ was significantly down-regulated. Among genes with highest fold change (FC) were SPINK4, LBP, ADA, RETNLB and IL-1α. The highest concordance in differential expression between TNBS and IBD transcriptomes was three days after colitis induction. ISH and PCR results corresponded with the microarray data. The most concordantly expressed biologically relevant pathways included TNF signaling, Cell junction organization, and Interleukin-1 processing. Conclusions/Significance Endoscopy with biopsies in TNBS-colitis is useful to follow temporal changes of inflammation visually and histologically, and to acquire tissue for gene expression analyses. TNBS-colitis is an appropriate model to study specific biological processes in IBD. PMID:23382912

Reconstructing the temporal ordering of biological samples using microarray data.

PubMed

Magwene, Paul M; Lizardi, Paul; Kim, Junhyong

2003-05-01

Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.

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.

Gene expression profiles in the testis associated with testis-ova in adult Japanese medaka (Oryziaslatipes) exposed to 17α-ethinylestradiol.

PubMed

Hirakawa, Ikumi; Miyagawa, Shinichi; Katsu, Yoshinao; Kagami, Yoshihiro; Tatarazako, Norihisa; Kobayashi, Tohru; Kusano, Teruhiko; Mizutani, Takeshi; Ogino, Yukiko; Takeuchi, Takashi; Ohta, Yasuhiko; Iguchi, Taisen

2012-05-01

The occurrence of oocytes in the testis (testis-ova) of several fish species is often associated with exposure of estrogenic chemicals. However, induction mechanisms of the testis-ova remain to be elucidated. To develop marker genes for detecting testis-ova in the testis, adult male medaka were exposed to nominal concentration of 100 ng L(-1) of 17α-ethinylestradiol (EE2) for 3-5 weeks, and 800 ng estradiol benzoate (EB) for 3 weeks (experiment I), and a measured concentration of 20 ng L(-1) EE2 for 1-6 weeks (experiment II). Histological analysis was performed for the testis, and microarray analyses were performed for the testis, liver and brain. Microarray analysis in the estrogen-exposed medaka liver showed vitellogenin and choriogenin as estrogen responsive genes. Testis-ova were induced in the testis after 4 weeks of exposure to 100 ng L(-1) EE2, 3 weeks of exposure to 800 ng EB, and 6 weeks of exposure to 20 ng L(-1) EE2. Microarray analysis of estrogen-exposed testes revealed up-regulation of genes related to zona pellucida (ZP) and the oocytes marker gene, 42Sp50. Using quantitative RT-PCR we confirmed that Zpc5 gene can be used as a marker for the detection of testis-ova in male medaka. Copyright © 2011 Elsevier Ltd. All rights reserved.

The illusion of specific capture: surface and solution studies of suboptimal oligonucleotide hybridization

PubMed Central

2013-01-01

Background Hybridization based assays and capture systems depend on the specificity of hybridization between a probe and its intended target. A common guideline in the construction of DNA microarrays, for instance, is that avoiding complementary stretches of more than 15 nucleic acids in a 50 or 60-mer probe will eliminate sequence specific cross-hybridization reactions. Here we present a study of the behavior of partially matched oligonucleotide pairs with complementary stretches starting well below this threshold complementarity length – in silico, in solution, and at the microarray surface. The modeled behavior of pairs of oligonucleotide probes and their targets suggests that even a complementary stretch of sequence 12 nt in length would give rise to specific cross-hybridization. We designed a set of binding partners to a 50-mer oligonucleotide containing complementary stretches from 6 nt to 21 nt in length. Results Solution melting experiments demonstrate that stable partial duplexes can form when only 12 bp of complementary sequence are present; surface hybridization experiments confirm that a signal close in magnitude to full-strength signal can be obtained from hybridization of a 12 bp duplex within a 50mer oligonucleotide. Conclusions Microarray and other molecular capture strategies that rely on a 15 nt lower complementarity bound for eliminating specific cross-hybridization may not be sufficiently conservative. PMID:23445545

The Genome-Wide Expression Profile of Saussurea lappa Extract on House Dust Mite-Induced Atopic Dermatitis in Nc/Nga Mice.

PubMed

Lim, Hye-Sun; Ha, Hyekyung; Shin, Hyeun-Kyoo; Jeong, Soo-Jin

2015-09-01

Saussurea lappa has been reported to possess anti-atopic properties. In this study, we have confirmed the S. lappa's anti-atopic properties in Nc/Nga mice and investigated the candidate gene related with its properties using microarray. We determined the target gene using real time PCR in in vitro experiment. S. lappa showed the significant reduction in atopic dermatitis (AD) score and immunoglobulin E compared with the AD induced Nc/Nga mice. In the results of microarray using back skin obtained from animals, we found that S. lappa's properties are closely associated with cytokine-cytokine receptor interaction and the JAK-STAT signaling pathway. Consistent with the microarray data, real-time RT-PCR confirmed these modulation at the mRNA level in skin tissues from S. lappa-treated mice. Among these genes, PI3Kca and IL20Rβ were significantly downregulated by S. lappa treatment in Nc/Nga mouse model. In in vitro experiment using HaCaT cells, we found that the S. lappa components, including alantolactone, caryophyllene, costic acid, costunolide and dehydrocostus lactone significantly decreased the expression of PI3Kca but not IL20Rβ in vitro. Therefore, our study suggests that PI3Kca-related signaling is closely related with the protective effects of S. lappa against the development of atopic-dermatitis.

Temporal Influence on Awareness

DTIC Science & Technology

1995-12-01

43 38. Test Setup Timing: Measured vs Expected Modal Delays (in ms) ............. 46 39. Experiment I: visual and auditory stimuli...presented simultaneously; visual- auditory delay=Oms, visual-visual delay=0ms ....... .......................... 47 40. Experiment II: visual and auditory ...stimuli presented in order; visual- auditory de- lay=Oms, visual-visual delay=variable ................................ 48 41. Experiment II: visual and

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.

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

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

PubMed Central

McArt, Darragh G.; Dunne, Philip D.; Blayney, Jaine K.; Salto-Tellez, Manuel; Van Schaeybroeck, Sandra; Hamilton, Peter W.; Zhang, Shu-Dong

2013-01-01

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. PMID:23840550

COMPARISON OF COMPARATIVE GENOMIC HYBRIDIZATIONS TECHNOLOGIES ACROSS MICROARRAY PLATFORMS

EPA Science Inventory

Comparative Genomic Hybridization (CGH) measures DNA copy number differences between a reference genome and a test genome. The DNA samples are differentially labeled and hybridized to an immobilized substrate. In early CGH experiments, the DNA targets were hybridized to metaphase...

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

PubMed Central

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. PMID:25961028

Identification of Escherichia coli O157 by Using a Novel Colorimetric Detection Method with DNA Microarrays

PubMed Central

Swimley, Michelle S.; Taylor, Amber W.; Dawson, Erica D.

2011-01-01

Abstract Shiga toxin–producing Escherichia coli O157 is a leading cause of foodborne illness worldwide. To evaluate better methods to rapidly detect and genotype E. coli O157 strains, the present study evaluated the use of ampliPHOX, a novel colorimetric detection method based on photopolymerization, for pathogen identification with DNA microarrays. A low-density DNA oligonucleotide microarray was designed to target stx1 and stx2 genes encoding Shiga toxin production, the eae gene coding for adherence membrane protein, and the per gene encoding the O157-antigen perosamine synthetase. Results from the validation experiments demonstrated that the use of ampliPHOX allowed the accurate genotyping of the tested E. coli strains, and positive hybridization signals were observed for only probes targeting virulence genes present in the reference strains. Quantification showed that the average signal-to-noise ratio values ranged from 47.73 ± 7.12 to 76.71 ± 8.33, whereas average signal-to-noise ratio values below 2.5 were determined for probes where no polymer was formed due to lack of specific hybridization. Sensitivity tests demonstrated that the sensitivity threshold for E. coli O157 detection was 100–1000 CFU/mL. Thus, the use of DNA microarrays in combination with photopolymerization allowed the rapid and accurate genotyping of E. coli O157 strains. PMID:21288130

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

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

PubMed

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

Regulation of Cell Division, Biofilm Formation, and Virulence by FlhC in Escherichia coli O157:H7 Grown on Meat▿†

PubMed Central

Sule, Preeti; Horne, Shelley M.; Logue, Catherine M.; Prüß, Birgit M.

2011-01-01

To understand the continuous problems that Escherichia coli O157:H7 causes as food pathogen, this study assessed global gene regulation in bacteria growing on meat. Since FlhD/FlhC of E. coli K-12 laboratory strains was previously established as a major control point in transducing signals from the environment to several cellular processes, this study compared the expression pattern of an E. coli O157:H7 parent strain to that of its isogenic flhC mutant. This was done with bacteria that had been grown on meat. Microarray experiments revealed 287 putative targets of FlhC. Real-time PCR was performed as an alternative estimate of transcription and confirmed microarray data for 13 out of 15 genes tested (87%). The confirmed genes are representative of cellular functions, such as central metabolism, cell division, biofilm formation, and pathogenicity. An additional 13 genes from the same cellular functions that had not been hypothesized as being regulated by FlhC by the microarray experiment were tested with real-time PCR and also exhibited higher expression levels in the flhC mutant than in the parent strain. Physiological experiments were performed and confirmed that FlhC reduced the cell division rate, the amount of biofilm biomass, and pathogenicity in a chicken embryo lethality model. Altogether, this study provides valuable insight into the complex regulatory network of the pathogen that enables its survival under various environmental conditions. This information may be used to develop strategies that could be used to reduce the number of cells or pathogenicity of E. coli O157:H7 on meat by interfering with the signal transduction pathways. PMID:21498760

Transcriptomic responses to wounding: meta-analysis of gene expression microarray data.

PubMed

Sass, Piotr Andrzej; Dąbrowski, Michał; Charzyńska, Agata; Sachadyn, Paweł

2017-11-07

A vast amount of microarray data on transcriptomic response to injury has been collected so far. We designed the analysis in order to identify the genes displaying significant changes in expression after wounding in different organisms and tissues. This meta-analysis is the first study to compare gene expression profiles in response to wounding in as different tissues as heart, liver, skin, bones, and spinal cord, and species, including rat, mouse and human. We collected available microarray transcriptomic profiles obtained from different tissue injury experiments and selected the genes showing a minimum twofold change in expression in response to wounding in prevailing number of experiments for each of five wound healing stages we distinguished: haemostasis & early inflammation, inflammation, early repair, late repair and remodelling. During the initial phases after wounding, haemostasis & early inflammation and inflammation, the transcriptomic responses showed little consistency between different tissues and experiments. For the later phases, wound repair and remodelling, we identified a number of genes displaying similar transcriptional responses in all examined tissues. As revealed by ontological analyses, activation of certain pathways was rather specific for selected phases of wound healing, such as e.g. responses to vitamin D pronounced during inflammation. Conversely, we observed induction of genes encoding inflammatory agents and extracellular matrix proteins in all wound healing phases. Further, we selected several genes differentially upregulated throughout different stages of wound response, including established factors of wound healing in addition to those previously unreported  in this context such as PTPRC and AQP4. We found that transcriptomic responses to wounding showed similar traits in a diverse selection of tissues including skin, muscles, internal organs and nervous system. Notably, we distinguished transcriptional induction of inflammatory genes not only in the initial response to wounding, but also later, during wound repair and tissue remodelling.

Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis

PubMed Central

Holloway, Andrew J; Oshlack, Alicia; Diyagama, Dileepa S; Bowtell, David DL; Smyth, Gordon K

2006-01-01

Background Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis. Results A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability. Conclusion The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome. PMID:17118209

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.

Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays

NASA Astrophysics Data System (ADS)

Khosravi, Farhad; Trainor, Patrick; Rai, Shesh N.; Kloecker, Goetz; Wickstrom, Eric; Panchapakesan, Balaji

2016-04-01

We demonstrate the rapid and label-free capture of breast cancer cells spiked in buffy coats using nanotube-antibody micro-arrays. Single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (EpCAM) antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester functionalization method. Following functionalization, plain buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the nanotube device and electrical signatures were recorded for differences in interaction between samples. A statistical classifier for the ‘liquid biopsy’ was developed to create a predictive model based on dynamic time warping to classify device electrical signals that corresponded to plain (control) or spiked buffy coats (case). In training test, the device electrical signals originating from buffy versus spiked buffy samples were classified with ˜100% sensitivity, ˜91% specificity and ˜96% accuracy. In the blinded test, the signals were classified with ˜91% sensitivity, ˜82% specificity and ˜86% accuracy. A heatmap was generated to visually capture the relationship between electrical signatures and the sample condition. Confocal microscopic analysis of devices that were classified as spiked buffy coats based on their electrical signatures confirmed the presence of cancer cells, their attachment to the device and overexpression of EpCAM receptors. The cell numbers were counted to be ˜1-17 cells per 5 μl per device suggesting single cell sensitivity in spiked buffy coats that is scalable to higher volumes using the micro-arrays.

Joint mapping of genes and conditions via multidimensional unfolding analysis

PubMed Central

Van Deun, Katrijn; Marchal, Kathleen; Heiser, Willem J; Engelen, Kristof; Van Mechelen, Iven

2007-01-01

Background Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. Results We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. Conclusion Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data. PMID:17550582

Expression profile of circular RNAs in human gastric cancer tissues

PubMed Central

Huang, You-Sheng; Jie, Na; Zou, Ke-Jian; Weng, Yang

2017-01-01

Circular RNAs (circRNAs) represent a newly identified class of non-coding RNA molecules, which interfere with gene transcription by adsorbing microRNAs (miRNAs). CircRNAs serve important roles in disease development and have the potential to serve as a novel class of biomarkers for clinical diagnosis. However, the role of circRNAs in the occurrence and development of gastric cancer (GC) remains unclear. In the present study, the expression profiles of circRNAs were compared between GC and adjacent normal tissues using a circRNA microarray, following which quantitative polymerase chain reaction (qPCR) was used to confirm the results of the circRNA microarray. Compared with the adjacent, normal mucosal tissues, 16 circRNAs were upregulated and 84 circRNAs were downregulated in GC. A total of 10 circRNAs were selected for validation in three pairs of GC and adjacent noncancerous tissues. The qPCR results were consistent with the findings of the microarray-based expression analysis. Of the circRNAs studied, only circRNA-0026 (hsa_circ_0000026) exhibited significantly different expression in GC (2.8-fold, P=0.001). Furthermore, online Database for Annotation, Visualization and Integrated Discovery annotation was used to predict circRNA-targeted miRNA-gene interactions. The analysis revealed that circRNA-0026 may regulate RNA transcription, RNA metabolism, gene expression, gene silencing and other biological functions in GC. In conclusion, differential expression of circRNAs may be associated with GC tumorigenesis, and circRNA-0026 is a promising biomarker for GC diagnosis and targeted therapy. PMID:28737829

Microarray Я US: a user-friendly graphical interface to Bioconductor tools that enables accurate microarray data analysis and expedites comprehensive functional analysis of microarray results.

PubMed

Dai, Yilin; Guo, Ling; Li, Meng; Chen, Yi-Bu

2012-06-08

Microarray data analysis presents a significant challenge to researchers who are unable to use the powerful Bioconductor and its numerous tools due to their lack of knowledge of R language. Among the few existing software programs that offer a graphic user interface to Bioconductor packages, none have implemented a comprehensive strategy to address the accuracy and reliability issue of microarray data analysis due to the well known probe design problems associated with many widely used microarray chips. There is also a lack of tools that would expedite the functional analysis of microarray results. We present Microarray Я US, an R-based graphical user interface that implements over a dozen popular Bioconductor packages to offer researchers a streamlined workflow for routine differential microarray expression data analysis without the need to learn R language. In order to enable a more accurate analysis and interpretation of microarray data, we incorporated the latest custom probe re-definition and re-annotation for Affymetrix and Illumina chips. A versatile microarray results output utility tool was also implemented for easy and fast generation of input files for over 20 of the most widely used functional analysis software programs. Coupled with a well-designed user interface, Microarray Я US leverages cutting edge Bioconductor packages for researchers with no knowledge in R language. It also enables a more reliable and accurate microarray data analysis and expedites downstream functional analysis of microarray results.

Final Scientific/Technical Report, DE-FG02-06ER64171, Integrated Nucleic Acid System for In-Field Monitoring of Microbial Community Dynamics and Metabolic Activity – Subproject to Co-PI Eric E. Roden

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

Eric E. Roden

2009-07-08

This report summarizes research conducted in conjunction with a project entitled “Integrated Nucleic Acid System for In-Field Monitoring of Microbial Community Dynamics and Metabolic Activity”, which was funded through the Integrative Studies Element of the former NABIR Program (now the Environmental Remediation Sciences Program) within the Office of Biological and Environmental Research. Dr. Darrell Chandler (originally at Argonne National Laboratory, now with Akonni Biosystems) was the overall PI/PD for the project. The overall project goals were to (1) apply a model iron-reducer and sulfate-reducer microarray and instrumentation systems to sediment and groundwater samples from the Scheibe et al. FRC Areamore » 2 field site, UMTRA sediments, and other DOE contaminated sites; (2) continue development and expansion of a 16S rRNA/rDNA¬-targeted probe suite for microbial community dynamics as new sequences are obtained from DOE-relevant sites; and (3) address the fundamental molecular biology and analytical chemistry associated with the extraction, purification and analysis of functional genes and mRNA in environmental samples. Work on the UW subproject focused on conducting detailed batch and semicontinuous culture reactor experiments with uranium-contaminated FRC Area 2 sediment. The reactor experiments were designed to provide coherent geochemical and microbiological data in support of microarray analyses of microbial communities in Area 2 sediments undergoing biostimulation with ethanol. A total of four major experiments were conducted (one batch and three semicontinuous culture), three of which (the batch and two semicontinuous culture) provided samples for DNA microarray analysis. A variety of other molecular analyses (clone libraries, 16S PhyloChip, RT-PCR, and T-RFLP) were conducted on parallel samples from the various experiments in order to provide independent information on microbial community response to biostimulation.« less

ArrayInitiative - a tool that simplifies creating custom Affymetrix CDFs

PubMed Central

2011-01-01

Background Probes on a microarray represent a frozen view of a genome and are quickly outdated when new sequencing studies extend our knowledge, resulting in significant measurement error when analyzing any microarray experiment. There are several bioinformatics approaches to improve probe assignments, but without in-house programming expertise, standardizing these custom array specifications as a usable file (e.g. as Affymetrix CDFs) is difficult, owing mostly to the complexity of the specification file format. However, without correctly standardized files there is a significant barrier for testing competing analysis approaches since this file is one of the required inputs for many commonly used algorithms. The need to test combinations of probe assignments and analysis algorithms led us to develop ArrayInitiative, a tool for creating and managing custom array specifications. Results ArrayInitiative is a standalone, cross-platform, rich client desktop application for creating correctly formatted, custom versions of manufacturer-provided (default) array specifications, requiring only minimal knowledge of the array specification rules and file formats. Users can import default array specifications, import probe sequences for a default array specification, design and import a custom array specification, export any array specification to multiple output formats, export the probe sequences for any array specification and browse high-level information about the microarray, such as version and number of probes. The initial release of ArrayInitiative supports the Affymetrix 3' IVT expression arrays we currently analyze, but as an open source application, we hope that others will contribute modules for other platforms. Conclusions ArrayInitiative allows researchers to create new array specifications, in a standard format, based upon their own requirements. This makes it easier to test competing design and analysis strategies that depend on probe definitions. Since the custom array specifications are easily exported to the manufacturer's standard format, researchers can analyze these customized microarray experiments using established software tools, such as those available in Bioconductor. PMID:21548938

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.

Stannous Fluoride Effects on Gene Expression of Streptococcus mutans and Actinomyces viscosus.

PubMed

Shi, Y; Li, R; White, D J; Biesbrock, A R

2018-02-01

A genome-wide transcriptional analysis was performed to elucidate the bacterial cellular response of Streptococcus mutans and Actinomyces viscosus to NaF and SnF 2 . The minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of SnF 2 were predetermined before microarray study. Gene expression profiling microarray experiments were carried out in the absence (control) and presence (experimental) of 10 ppm and 100 ppm Sn 2+ (in the form of SnF 2 ) and fluoride controls for 10-min exposures (4 biological replicates/treatment). These Sn 2+ levels and treatment time were chosen because they have been shown to slow bacterial growth of S. mutans (10 ppm) and A. viscosus (100 ppm) without affecting cell viability. All data generated by microarray experiments were analyzed with bioinformatics tools by applying the following criteria: 1) a q value should be ≤0.05, and 2) an absolute fold change in transcript level should be ≥1.5. Microarray results showed SnF 2 significantly inhibited several genes encoding enzymes of the galactose pathway upon a 10-min exposure versus a negative control: lacA and lacB (A and B subunits of the galactose-6-P isomerase), lacC (tagatose-6-P kinase), lacD (tagatose-1,6-bP adolase), galK (galactokinase), galT (galactose-1-phosphate uridylyltransferase), and galE (UDP-glucose 4-epimerase). A gene fruK encoding fructose-1-phosphate kinase in the fructose pathway was also significantly inhibited. Several genes encoding fructose/mannose-specific enzyme IIABC components in the phosphotransferase system (PTS) were also downregulated, as was ldh encoding lactate dehydrogenase, a key enzyme involved in lactic acid synthesis. SnF 2 downregulated the transcription of most key enzyme genes involved in the galactose pathway and also suppressed several key genes involved in the PTS, which transports sugars into the cell in the first step of glycolysis.

NCBI GEO: archive for high-throughput functional genomic data.

PubMed

Barrett, Tanya; Troup, Dennis B; Wilhite, Stephen E; Ledoux, Pierre; Rudnev, Dmitry; Evangelista, Carlos; Kim, Irene F; Soboleva, Alexandra; Tomashevsky, Maxim; Marshall, Kimberly A; Phillippy, Katherine H; Sherman, Patti M; Muertter, Rolf N; Edgar, Ron

2009-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest public repository for high-throughput gene expression data. Additionally, GEO hosts other categories of high-throughput functional genomic data, including those that examine genome copy number variations, chromatin structure, methylation status and transcription factor binding. These data are generated by the research community using high-throughput technologies like microarrays and, more recently, next-generation sequencing. The database has a flexible infrastructure that can capture fully annotated raw and processed data, enabling compliance with major community-derived scientific reporting standards such as 'Minimum Information About a Microarray Experiment' (MIAME). In addition to serving as a centralized data storage hub, GEO offers many tools and features that allow users to effectively explore, analyze and download expression data from both gene-centric and experiment-centric perspectives. This article summarizes the GEO repository structure, content and operating procedures, as well as recently introduced data mining features. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.

Overcoming confounded controls in the analysis of gene expression data from microarray experiments.

PubMed

Bhattacharya, Soumyaroop; Long, Dang; Lyons-Weiler, James

2003-01-01

A potential limitation of data from microarray experiments exists when improper control samples are used. In cancer research, comparisons of tumour expression profiles to those from normal samples is challenging due to tissue heterogeneity (mixed cell populations). A specific example exists in a published colon cancer dataset, in which tissue heterogeneity was reported among the normal samples. In this paper, we show how to overcome or avoid the problem of using normal samples that do not derive from the same tissue of origin as the tumour. We advocate an exploratory unsupervised bootstrap analysis that can reveal unexpected and undesired, but strongly supported, clusters of samples that reflect tissue differences instead of tumour versus normal differences. All of the algorithms used in the analysis, including the maximum difference subset algorithm, unsupervised bootstrap analysis, pooled variance t-test for finding differentially expressed genes and the jackknife to reduce false positives, are incorporated into our online Gene Expression Data Analyzer ( http:// bioinformatics.upmc.edu/GE2/GEDA.html ).

Linear model for fast background subtraction in oligonucleotide microarrays.

PubMed

Kroll, K Myriam; Barkema, Gerard T; Carlon, Enrico

2009-11-16

One important preprocessing step in the analysis of microarray data is background subtraction. In high-density oligonucleotide arrays this is recognized as a crucial step for the global performance of the data analysis from raw intensities to expression values. We propose here an algorithm for background estimation based on a model in which the cost function is quadratic in a set of fitting parameters such that minimization can be performed through linear algebra. The model incorporates two effects: 1) Correlated intensities between neighboring features in the chip and 2) sequence-dependent affinities for non-specific hybridization fitted by an extended nearest-neighbor model. The algorithm has been tested on 360 GeneChips from publicly available data of recent expression experiments. The algorithm is fast and accurate. Strong correlations between the fitted values for different experiments as well as between the free-energy parameters and their counterparts in aqueous solution indicate that the model captures a significant part of the underlying physical chemistry.

Fast gene ontology based clustering for microarray experiments.

PubMed

Ovaska, Kristian; Laakso, Marko; Hautaniemi, Sampsa

2008-11-21

Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

Probabilistic segmentation and intensity estimation for microarray images.

PubMed

Gottardo, Raphael; Besag, Julian; Stephens, Matthew; Murua, Alejandro

2006-01-01

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.

What is the best reference RNA? And other questions regarding the design and analysis of two-color microarray experiments.

PubMed

Kerr, Kathleen F; Serikawa, Kyle A; Wei, Caimiao; Peters, Mette A; Bumgarner, Roger E

2007-01-01

The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness of pre-processing methods for two-color arrays. Our results favor using intensity normalization and foregoing background subtraction. Finally, we evaluate the sensitivity and specificity of data quality filters, and we propose a new filter that can be applied to any experimental design and does not rely on replicate hybridizations.

RNA sequencing: current and prospective uses in metabolic research.

PubMed

Vikman, Petter; Fadista, Joao; Oskolkov, Nikolay

2014-10-01

Previous global RNA analysis was restricted to known transcripts in species with a defined transcriptome. Next generation sequencing has transformed transcriptomics by making it possible to analyse expressed genes with an exon level resolution from any tissue in any species without any a priori knowledge of which genes that are being expressed, splice patterns or their nucleotide sequence. In addition, RNA sequencing is a more sensitive technique compared with microarrays with a larger dynamic range, and it also allows for investigation of imprinting and allele-specific expression. This can be done for a cost that is able to compete with that of a microarray, making RNA sequencing a technique available to most researchers. Therefore RNA sequencing has recently become the state of the art with regards to large-scale RNA investigations and has to a large extent replaced microarrays. The only drawback is the large data amounts produced, which together with the complexity of the data can make a researcher spend far more time on analysis than performing the actual experiment. © 2014 Society for Endocrinology.

Development of an electro-responsive platform for the controlled transfection of mammalian cells

NASA Astrophysics Data System (ADS)

Hook, Andrew L.; Thissen, Helmut W.; Hayes, Jason P.; Voelcker, Nicolas H.

2005-02-01

The recent development of living microarrays as novel tools for the analysis of gene expression in an in-situ environment promises to unravel gene function within living organisms. In order to significantly enhance microarray performance, we are working towards electro-responsive DNA transfection chips. This study focuses on the control of DNA adsorption and desorption by appropriate surface modification of highly doped p++ silicon. Silicon was modified by plasma polymerisation of allylamine (ALAPP), a non-toxic surface that sustains cell growth. Subsequent high surface density grafting of poly(ethylene oxide) formed a layer resistant to biomolecule adsorption and cell attachment. Spatially controlled excimer laser ablation of the surface produced micron resolution patterns of re-exposed plasma polymer whilst the rest of the surface remained non-fouling. We observed electro-stimulated preferential adsorption of DNA to the ALAPP surface and subsequent desorption by the application of a negative bias. Cell culture experiments with HEK 293 cells demonstrated efficient and controlled transfection of cells using the expression of green fluorescent protein as a reporter. Thus, these chemically patterned surfaces are promising platforms for use as living microarrays.

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

Experience and information loss in auditory and visual memory.

PubMed

Gloede, Michele E; Paulauskas, Emily E; Gregg, Melissa K

2017-07-01

Recent studies show that recognition memory for sounds is inferior to memory for pictures. Four experiments were conducted to examine the nature of auditory and visual memory. Experiments 1-3 were conducted to evaluate the role of experience in auditory and visual memory. Participants received a study phase with pictures/sounds, followed by a recognition memory test. Participants then completed auditory training with each of the sounds, followed by a second memory test. Despite auditory training in Experiments 1 and 2, visual memory was superior to auditory memory. In Experiment 3, we found that it is possible to improve auditory memory, but only after 3 days of specific auditory training and 3 days of visual memory decay. We examined the time course of information loss in auditory and visual memory in Experiment 4 and found a trade-off between visual and auditory recognition memory: Visual memory appears to have a larger capacity, while auditory memory is more enduring. Our results indicate that visual and auditory memory are inherently different memory systems and that differences in visual and auditory recognition memory performance may be due to the different amounts of experience with visual and auditory information, as well as structurally different neural circuitry specialized for information retention.

Transcriptome analysis of the human T lymphocyte cell line Jurkat and human peripheral blood mononuclear cells exposed to deoxynivalenol (DON): New mechanistic insights

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

Katika, Madhumohan R.; Department of Health Risk Analysis and Toxicology, Maastricht University; Netherlands Toxicogenomics Centre

Deoxynivalenol (DON) or vomitoxin is a commonly encountered type-B trichothecene mycotoxin, produced by Fusarium species predominantly found in cereals and grains. DON is known to exert toxic effects on the gastrointestinal, reproductive and neuroendocrine systems, and particularly on the immune system. Depending on dose and exposure time, it can either stimulate or suppress immune function. The main objective of this study was to obtain a deeper insight into DON-induced effects on lymphoid cells. For this, we exposed the human T-lymphocyte cell line Jurkat and human peripheral blood mononuclear cells (PBMCs) to various concentrations of DON for various times and examinedmore » gene expression changes by DNA microarray analysis. Jurkat cells were exposed to 0.25 and 0.5 μM DON for 3, 6 and 24 h. Biological interpretation of the microarray data indicated that DON affects various processes in these cells: It upregulates genes involved in ribosome structure and function, RNA/protein synthesis and processing, endoplasmic reticulum (ER) stress, calcium-mediated signaling, mitochondrial function, oxidative stress, the NFAT and NF-κB/TNF-α pathways, T cell activation and apoptosis. The effects of DON on the expression of genes involved in ER stress, NFAT activation and apoptosis were confirmed by qRT-PCR. Other biochemical experiments confirmed that DON activates calcium-dependent proteins such as calcineurin and M-calpain that are known to be involved in T cell activation and apoptosis. Induction of T cell activation was also confirmed by demonstrating that DON activates NFATC1 and induces its translocation from the cytoplasm to the nucleus. For the gene expression profiling of PBMCs, cells were exposed to 2 and 4 μM DON for 6 and 24 h. Comparison of the Jurkat microarray data with those obtained with PBMCs showed that most of the processes affected by DON in the Jurkat cell line were also affected in the PBMCs. -- Highlights: ► The human T cell line Jurkat and human PBMCs were exposed to DON. ► Whole-genome microarray experiments were performed. ► Microarray data indicates that DON affects ribosome and RNA/protein synthesis. ► DON treatment induces ER stress, calcium mediated signaling, NFAT and NF-κB. ► Exposure to DON induces T cell activation, oxidative stress and apoptosis.« less

caCORRECT2: Improving the accuracy and reliability of microarray data in the presence of artifacts

PubMed Central

2011-01-01

Background In previous work, we reported the development of caCORRECT, a novel microarray quality control system built to identify and correct spatial artifacts commonly found on Affymetrix arrays. We have made recent improvements to caCORRECT, including the development of a model-based data-replacement strategy and integration with typical microarray workflows via caCORRECT's web portal and caBIG grid services. In this report, we demonstrate that caCORRECT improves the reproducibility and reliability of experimental results across several common Affymetrix microarray platforms. caCORRECT represents an advance over state-of-art quality control methods such as Harshlighting, and acts to improve gene expression calculation techniques such as PLIER, RMA and MAS5.0, because it incorporates spatial information into outlier detection as well as outlier information into probe normalization. The ability of caCORRECT to recover accurate gene expressions from low quality probe intensity data is assessed using a combination of real and synthetic artifacts with PCR follow-up confirmation and the affycomp spike in data. The caCORRECT tool can be accessed at the website: http://cacorrect.bme.gatech.edu. Results We demonstrate that (1) caCORRECT's artifact-aware normalization avoids the undesirable global data warping that happens when any damaged chips are processed without caCORRECT; (2) When used upstream of RMA, PLIER, or MAS5.0, the data imputation of caCORRECT generally improves the accuracy of microarray gene expression in the presence of artifacts more than using Harshlighting or not using any quality control; (3) Biomarkers selected from artifactual microarray data which have undergone the quality control procedures of caCORRECT are more likely to be reliable, as shown by both spike in and PCR validation experiments. Finally, we present a case study of the use of caCORRECT to reliably identify biomarkers for renal cell carcinoma, yielding two diagnostic biomarkers with potential clinical utility, PRKAB1 and NNMT. Conclusions caCORRECT is shown to improve the accuracy of gene expression, and the reproducibility of experimental results in clinical application. This study suggests that caCORRECT will be useful to clean up possible artifacts in new as well as archived microarray data. PMID:21957981

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

The 'PUCE CAFE' Project: the First 15K Coffee Microarray, a New Tool for Discovering Candidate Genes correlated to Agronomic and Quality Traits

PubMed Central

2011-01-01

Background Understanding the genetic elements that contribute to key aspects of coffee biology will have an impact on future agronomical improvements for this economically important tree. During the past years, EST collections were generated in Coffee, opening the possibility to create new tools for functional genomics. Results The "PUCE CAFE" Project, organized by the scientific consortium NESTLE/IRD/CIRAD, has developed an oligo-based microarray using 15,721 unigenes derived from published coffee EST sequences mostly obtained from different stages of fruit development and leaves in Coffea Canephora (Robusta). Hybridizations for two independent experiments served to compare global gene expression profiles in three types of tissue matter (mature beans, leaves and flowers) in C. canephora as well as in the leaves of three different coffee species (C. canephora, C. eugenoides and C. arabica). Microarray construction, statistical analyses and validation by Q-PCR analysis are presented in this study. Conclusion We have generated the first 15 K coffee array during this PUCE CAFE project, granted by Génoplante (the French consortium for plant genomics). This new tool will help study functional genomics in a wide range of experiments on various plant tissues, such as analyzing bean maturation or resistance to pathogens or drought. Furthermore, the use of this array has proven to be valid in different coffee species (diploid or tetraploid), drastically enlarging its impact for high-throughput gene expression in the community of coffee research. PMID:21208403

DNA microarray unravels rapid changes in transcriptome of MK-801 treated rat brain

PubMed Central

Kobayashi, Yuka; Kulikova, Sofya P; Shibato, Junko; Rakwal, Randeep; Satoh, Hiroyuki; Pinault, Didier; Masuo, Yoshinori

2015-01-01

AIM: To investigate the impact of MK-801 on gene expression patterns genome wide in rat brain regions. METHODS: Rats were treated with an intraperitoneal injection of MK-801 [0.08 (low-dose) and 0.16 (high-dose) mg/kg] or NaCl (vehicle control). In a first series of experiment, the frontoparietal electrocorticogram was recorded 15 min before and 60 min after injection. In a second series of experiments, the whole brain of each animal was rapidly removed at 40 min post-injection, and different regions were separated: amygdala, cerebral cortex, hippocampus, hypothalamus, midbrain and ventral striatum on ice followed by DNA microarray (4 × 44 K whole rat genome chip) analysis. RESULTS: Spectral analysis revealed that a single systemic injection of MK-801 significantly and selectively augmented the power of baseline gamma frequency (30-80 Hz) oscillations in the frontoparietal electroencephalogram. DNA microarray analysis showed the largest number (up- and down- regulations) of gene expressions in the cerebral cortex (378), midbrain (376), hippocampus (375), ventral striatum (353), amygdala (301), and hypothalamus (201) under low-dose (0.08 mg/kg) of MK-801. Under high-dose (0.16 mg/kg), ventral striatum (811) showed the largest number of gene expression changes. Gene expression changes were functionally categorized to reveal expression of genes and function varies with each brain region. CONCLUSION: Acute MK-801 treatment increases synchrony of baseline gamma oscillations, and causes very early changes in gene expressions in six individual rat brain regions, a first report. PMID:26629322

The 'PUCE CAFE' Project: the first 15K coffee microarray, a new tool for discovering candidate genes correlated to agronomic and quality traits.

PubMed

Privat, Isabelle; Bardil, Amélie; Gomez, Aureliano Bombarely; Severac, Dany; Dantec, Christelle; Fuentes, Ivanna; Mueller, Lukas; Joët, Thierry; Pot, David; Foucrier, Séverine; Dussert, Stéphane; Leroy, Thierry; Journot, Laurent; de Kochko, Alexandre; Campa, Claudine; Combes, Marie-Christine; Lashermes, Philippe; Bertrand, Benoit

2011-01-05

Understanding the genetic elements that contribute to key aspects of coffee biology will have an impact on future agronomical improvements for this economically important tree. During the past years, EST collections were generated in Coffee, opening the possibility to create new tools for functional genomics. The "PUCE CAFE" Project, organized by the scientific consortium NESTLE/IRD/CIRAD, has developed an oligo-based microarray using 15,721 unigenes derived from published coffee EST sequences mostly obtained from different stages of fruit development and leaves in Coffea Canephora (Robusta). Hybridizations for two independent experiments served to compare global gene expression profiles in three types of tissue matter (mature beans, leaves and flowers) in C. canephora as well as in the leaves of three different coffee species (C. canephora, C. eugenoides and C. arabica). Microarray construction, statistical analyses and validation by Q-PCR analysis are presented in this study. We have generated the first 15 K coffee array during this PUCE CAFE project, granted by Génoplante (the French consortium for plant genomics). This new tool will help study functional genomics in a wide range of experiments on various plant tissues, such as analyzing bean maturation or resistance to pathogens or drought. Furthermore, the use of this array has proven to be valid in different coffee species (diploid or tetraploid), drastically enlarging its impact for high-throughput gene expression in the community of coffee research.

2008 Microarray Research Group (MARG Survey): Sensing the State of Microarray Technology

EPA Science Inventory

Over the past several years, the field of microarrays has grown and evolved drastically. In its continued efforts to track this evolution and transformation, the ABRF-MARG has once again conducted a survey of international microarray facilities and individual microarray users. Th...

Detection of biomarkers for Hepatocellular Carcinoma using a hybrid univariate gene selection methods

PubMed Central

2012-01-01

Background Discovering new biomarkers has a great role in improving early diagnosis of Hepatocellular carcinoma (HCC). The experimental determination of biomarkers needs a lot of time and money. This motivates this work to use in-silico prediction of biomarkers to reduce the number of experiments required for detecting new ones. This is achieved by extracting the most representative genes in microarrays of HCC. Results In this work, we provide a method for extracting the differential expressed genes, up regulated ones, that can be considered candidate biomarkers in high throughput microarrays of HCC. We examine the power of several gene selection methods (such as Pearson’s correlation coefficient, Cosine coefficient, Euclidean distance, Mutual information and Entropy with different estimators) in selecting informative genes. A biological interpretation of the highly ranked genes is done using KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, ENTREZ and DAVID (Database for Annotation, Visualization, and Integrated Discovery) databases. The top ten genes selected using Pearson’s correlation coefficient and Cosine coefficient contained six genes that have been implicated in cancer (often multiple cancers) genesis in previous studies. A fewer number of genes were obtained by the other methods (4 genes using Mutual information, 3genes using Euclidean distance and only one gene using Entropy). A better result was obtained by the utilization of a hybrid approach based on intersecting the highly ranked genes in the output of all investigated methods. This hybrid combination yielded seven genes (2 genes for HCC and 5 genes in different types of cancer) in the top ten genes of the list of intersected genes. Conclusions To strengthen the effectiveness of the univariate selection methods, we propose a hybrid approach by intersecting several of these methods in a cascaded manner. This approach surpasses all of univariate selection methods when used individually according to biological interpretation and the examination of gene expression signal profiles. PMID:22867264

THE ABRF-MARG MICROARRAY SURVEY 2004: TAKING THE PULSE OF THE MICROARRAY FIELD

EPA Science Inventory

Over the past several years, the field of microarrays has grown and evolved drastically. In its continued efforts to track this evolution, the ABRF-MARG has once again conducted a survey of international microarray facilities and individual microarray users. The goal of the surve...

Contributions to Statistical Problems Related to Microarray Data

ERIC Educational Resources Information Center

Hong, Feng

2009-01-01

Microarray is a high throughput technology to measure the gene expression. Analysis of microarray data brings many interesting and challenging problems. This thesis consists three studies related to microarray data. First, we propose a Bayesian model for microarray data and use Bayes Factors to identify differentially expressed genes. Second, we…

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.

Vitamin D Receptor gene (VDR) transcripts in bone, cartilage, muscles and blood and microarray analysis of vitamin D responsive genes expression in paravertebral muscles of Juvenile and Adolescent Idiopathic Scoliosis patients

PubMed Central

2012-01-01

Background VDR may be considered as a candidate gene potentially related to Idiopathic Scoliosis susceptibility and natural history. Transcriptional profile of VDR mRNA isoforms might be changed in the structural tissues of the scoliotic spine and potentially influence the expression of VDR responsive genes. The purpose of the study was to determine differences in mRNA abundance of VDR isoforms in bone, cartilage and paravertebral muscles between tissues from curve concavity and convexity, between JIS and AIS and to identify VDR responsive genes differentiating Juvenile and Adolescent Idiopathic Scoliosis in paravertebral muscles. Methods In a group of 29 patients with JIS and AIS, specimens of bone, cartilage, paravertebral muscles were harvested at the both sides of the curve apex together with peripheral blood samples. Extracted total RNA served as a matrix for VDRs and VDRl mRNA quantification by QRT PCR. Subsequent microarray analysis of paravertebral muscular tissue samples was performed with HG U133A chips (Affymetrix). Quantitative data were compared by a nonparametric Mann Whitney U test. Microarray results were analyzed with GeneSpring 11GX application. Matrix plot of normalized log-intensities visualized the degree of differentiation between muscular tissue transcriptomes of JIS and AIS group. Fold Change Analysis with cutoff of Fold Change ≥2 identified differentially expressed VDR responsive genes in paravertebral muscles of JIS and AIS. Results No significant differences in transcript abundance of VDR isoforms between tissues of the curve concavity and convexity were found. Statistically significant difference between JIS and AIS group in mRNA abundance of VDRl isoform was found in paravertebral muscles of curve concavity. Higher degree of muscular transcriptome differentiation between curve concavity and convexity was visualized in JIS group. In paravertebral muscles Tob2 and MED13 were selected as genes differentially expressed in JIS and AIS group. Conclusions In Idiopathic Scolioses transcriptional activity and alternative splicing of VDR mRNA in osseous, cartilaginous, and paravertebral muscular tissues are tissue specific and equal on both sides of the curve. The number of mRNA copies of VDRl izoform in concave paravertebral muscles might be one of the factors differentiating JIS and AIS. In paravertebral muscles Tob2 and Med13 genes differentiate Adolescent and Juvenile type of Idiopathic Scoliosis. PMID:23259508

The WeIzmann Supercooled Droplets Observation on a Microarray (WISDOM) and application for ambient dust

NASA Astrophysics Data System (ADS)

Reicher, Naama; Segev, Lior; Rudich, Yinon

2018-01-01

The WeIzmann Supercooled Droplets Observation on Microarray (WISDOM) is a new setup for studying ice nucleation in an array of monodisperse droplets for atmospheric implications. WISDOM combines microfluidics techniques for droplets production and a cryo-optic stage for observation and characterization of freezing events of individual droplets. This setup is designed to explore heterogeneous ice nucleation in the immersion freezing mode, down to the homogeneous freezing of water (235 K) in various cooling rates (typically 0.1-10 K min-1). It can also be used for studying homogeneous freezing of aqueous solutions in colder temperatures. Frozen fraction, ice nucleation active surface site densities and freezing kinetics can be obtained from WISDOM measurements for hundreds of individual droplets in a single freezing experiment. Calibration experiments using eutectic solutions and previously studied materials are described. WISDOM also allows repeatable cycles of cooling and heating for the same array of droplets. This paper describes the WISDOM setup, its temperature calibration, validation experiments and measurement uncertainties. Finally, application of WISDOM to study the ice nucleating particle (INP) properties of size-selected ambient Saharan dust particles is presented.

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification

PubMed Central

Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible. PMID:29666661

Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

PubMed

Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

2018-01-01

The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis

PubMed Central

Yang, Jing; Zhang, Wei; Sun, Jian; Xi, Zhiqin; Qiao, Zusha; Zhang, Jinyu; Wang, Yan; Ji, Ying; Feng, Wenli

2017-01-01

The aim of the present study was to investigate the potential genes involved in drug resistance of Candida albicans (C. albicans) by performing microarray analysis. The gene expression profile of GSE65396 was downloaded from the Gene Expression Omnibus, including a control, 15-min and 45-min macrocyclic compound RF59-treated group with three repeats for each. Following preprocessing using RAM, the differentially expressed genes (DEGs) were screened using the Limma package. Subsequently, the Kyoto Encyclopedia of Genes and Genomes pathways of these genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Based on interactions estimated by the Search Tool for Retrieval of Interacting Gene, the protein-protein interaction (PPI) network was visualized using Cytoscape. Subnetwork analysis was performed using ReactomeFI. A total of 154 upregulated and 27 downregulated DEGs were identified in the 15-min treated group, compared with the control, and 235 upregulated and 233 downregulated DEGs were identified in the 45-min treated group, compared with the control. The upregulated DEGs were significantly enriched in the ribosome pathway. Based on the PPI network, PRP5, RCL1, NOP13, NOP4 and MRT4 were the top five nodes in the 15-min treated comparison. GIS2, URA3, NOP58, ELP3 and PLP7 were the top five nodes in the 45-min treated comparison, and its subnetwork was significantly enriched in the ribosome pathway. The macrocyclic compound RF59 had a notable effect on the ribosome and its associated pathways of C. albicans. RCL1, NOP4, MRT4, GIS2 and NOP58 may be important in RF59-resistance. PMID:28944888

Developing Verbal and Visual Literacy through Experiences in the Visual Arts: 25 Tips for Teachers

ERIC Educational Resources Information Center

Johnson, Margaret H.

2008-01-01

Including talk about art--conversing with children about artwork, their own and others'--as a component of visual art activities extends children's experiences in and understanding of visual messages. Johnson discusses practices that help children develop visual and verbal expression through active experiences with the visual arts. She offers 25…

Chemiluminescence microarrays in analytical chemistry: a critical review.

PubMed

Seidel, Michael; Niessner, Reinhard

2014-09-01

Multi-analyte immunoassays on microarrays and on multiplex DNA microarrays have been described for quantitative analysis of small organic molecules (e.g., antibiotics, drugs of abuse, small molecule toxins), proteins (e.g., antibodies or protein toxins), and microorganisms, viruses, and eukaryotic cells. In analytical chemistry, multi-analyte detection by use of analytical microarrays has become an innovative research topic because of the possibility of generating several sets of quantitative data for different analyte classes in a short time. Chemiluminescence (CL) microarrays are powerful tools for rapid multiplex analysis of complex matrices. A wide range of applications for CL microarrays is described in the literature dealing with analytical microarrays. The motivation for this review is to summarize the current state of CL-based analytical microarrays. Combining analysis of different compound classes on CL microarrays reduces analysis time, cost of reagents, and use of laboratory space. Applications are discussed, with examples from food safety, water safety, environmental monitoring, diagnostics, forensics, toxicology, and biosecurity. The potential and limitations of research on multiplex analysis by use of CL microarrays are discussed in this review.

Experience-dependent plasticity from eye opening enables lasting, visual cortex-dependent enhancement of motion vision.

PubMed

Prusky, Glen T; Silver, Byron D; Tschetter, Wayne W; Alam, Nazia M; Douglas, Robert M

2008-09-24

Developmentally regulated plasticity of vision has generally been associated with "sensitive" or "critical" periods in juvenile life, wherein visual deprivation leads to loss of visual function. Here we report an enabling form of visual plasticity that commences in infant rats from eye opening, in which daily threshold testing of optokinetic tracking, amid otherwise normal visual experience, stimulates enduring, visual cortex-dependent enhancement (>60%) of the spatial frequency threshold for tracking. The perceptual ability to use spatial frequency in discriminating between moving visual stimuli is also improved by the testing experience. The capacity for inducing enhancement is transitory and effectively limited to infancy; however, enhanced responses are not consolidated and maintained unless in-kind testing experience continues uninterrupted into juvenile life. The data show that selective visual experience from infancy can alone enable visual function. They also indicate that plasticity associated with visual deprivation may not be the only cause of developmental visual dysfunction, because we found that experientially inducing enhancement in late infancy, without subsequent reinforcement of the experience in early juvenile life, can lead to enduring loss of function.

Analysis of High-Throughput ELISA Microarray Data

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

White, Amanda M.; Daly, Don S.; Zangar, Richard C.

Our research group develops analytical methods and software for the high-throughput analysis of quantitative enzyme-linked immunosorbent assay (ELISA) microarrays. ELISA microarrays differ from DNA microarrays in several fundamental aspects and most algorithms for analysis of DNA microarray data are not applicable to ELISA microarrays. In this review, we provide an overview of the steps involved in ELISA microarray data analysis and how the statistically sound algorithms we have developed provide an integrated software suite to address the needs of each data-processing step. The algorithms discussed are available in a set of open-source software tools (http://www.pnl.gov/statistics/ProMAT).

Experience improves feature extraction in Drosophila.

PubMed

Peng, Yueqing; Xi, Wang; Zhang, Wei; Zhang, Ke; Guo, Aike

2007-05-09

Previous exposure to a pattern in the visual scene can enhance subsequent recognition of that pattern in many species from honeybees to humans. However, whether previous experience with a visual feature of an object, such as color or shape, can also facilitate later recognition of that particular feature from multiple visual features is largely unknown. Visual feature extraction is the ability to select the key component from multiple visual features. Using a visual flight simulator, we designed a novel protocol for visual feature extraction to investigate the effects of previous experience on visual reinforcement learning in Drosophila. We found that, after conditioning with a visual feature of objects among combinatorial shape-color features, wild-type flies exhibited poor ability to extract the correct visual feature. However, the ability for visual feature extraction was greatly enhanced in flies trained previously with that visual feature alone. Moreover, we demonstrated that flies might possess the ability to extract the abstract category of "shape" but not a particular shape. Finally, this experience-dependent feature extraction is absent in flies with defective MBs, one of the central brain structures in Drosophila. Our results indicate that previous experience can enhance visual feature extraction in Drosophila and that MBs are required for this experience-dependent visual cognition.

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 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

Big Results from Small Samples: Evaluation of Amplification Protocols for Gene Expression Profiling

EPA Science Inventory

Microarrays have revolutionized many areas of biology due to our technical ability to quantify tens of thousands of transcripts within a single experiment. However, there are still many areas that cannot benefit from this technology due to the amount of biological material needed...

Waveguide-excited fluorescence microarray

NASA Astrophysics Data System (ADS)

Sagarzazu, Gabriel; Bedu, Mélanie; Martinelli, Lucio; Ha, Khoi-Nguyen; Pelletier, Nicolas; Safarov, Viatcheslav I.; Weisbuch, Claude; Gacoin, Thierry; Benisty, Henri

2008-04-01

Signal-to-noise ratio is a crucial issue in microarray fluorescence read-out. Several strategies are proposed for its improvement. First, light collection in conventional microarrays scanners is quite limited. It was recently shown that almost full collection can be achieved in an integrated lens-free biosensor, with labelled species hybridizing practically on the surface of a sensitive silicon detector [L. Martinelli et al. Appl. Phys. Lett. 91, 083901 (2007)]. However, even with such an improvement, the ultimate goal of real-time measurements during hybridization is challenging: the detector is dazzled by the large fluorescence of labelled species in the solution. In the present paper we show that this unwanted signal can effectively be reduced if the excitation light is confined in a waveguide. Moreover, the concentration of excitation light in a waveguide results in a huge signal gain. In our experiment we realized a structure consisting of a high index sol-gel waveguide deposited on a low-index substrate. The fluorescent molecules deposited on the surface of the waveguide were excited by the evanescent part of a wave travelling in the guide. The comparison with free-space excitation schemes confirms a huge gain (by several orders of magnitude) in favour of waveguide-based excitation. An optical guide deposited onto an integrated biosensor thus combines both advantages of ideal light collection and enhanced surface localized excitation without compromising the imaging properties. Modelling predicts a negligible penalty from spatial cross-talk in practical applications. We believe that such a system would bring microarrays to hitherto unattained sensitivities.

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

Living Cell Microarrays: An Overview of Concepts

PubMed Central

Jonczyk, Rebecca; Kurth, Tracy; Lavrentieva, Antonina; Walter, Johanna-Gabriela; Scheper, Thomas; Stahl, Frank

2016-01-01

Living cell microarrays are a highly efficient cellular screening system. Due to the low number of cells required per spot, cell microarrays enable the use of primary and stem cells and provide resolution close to the single-cell level. Apart from a variety of conventional static designs, microfluidic microarray systems have also been established. An alternative format is a microarray consisting of three-dimensional cell constructs ranging from cell spheroids to cells encapsulated in hydrogel. These systems provide an in vivo-like microenvironment and are preferably used for the investigation of cellular physiology, cytotoxicity, and drug screening. Thus, many different high-tech microarray platforms are currently available. Disadvantages of many systems include their high cost, the requirement of specialized equipment for their manufacture, and the poor comparability of results between different platforms. In this article, we provide an overview of static, microfluidic, and 3D cell microarrays. In addition, we describe a simple method for the printing of living cell microarrays on modified microscope glass slides using standard DNA microarray equipment available in most laboratories. Applications in research and diagnostics are discussed, e.g., the selective and sensitive detection of biomarkers. Finally, we highlight current limitations and the future prospects of living cell microarrays. PMID:27600077

SpliceCenter: A suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies

PubMed Central

Ryan, Michael C; Zeeberg, Barry R; Caplen, Natasha J; Cleland, James A; Kahn, Ari B; Liu, Hongfang; Weinstein, John N

2008-01-01

Background Over 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant. Results A number of contemporary technologies are used for analyzing transcripts or proteins. To enable investigation of the impact of splice variation on the interpretation of data derived from those technologies, we have developed SpliceCenter. SpliceCenter is a suite of user-friendly, web-based applications that includes programs for analysis of RT-PCR primer/probe sets, effectors of RNAi, microarrays, and protein-targeting technologies. Both interactive and high-throughput implementations of the tools are provided. The interactive versions of SpliceCenter tools provide visualizations of a gene's alternative transcripts and probe target positions, enabling the user to identify which splice variants are or are not targeted. The high-throughput batch versions accept user query files and provide results in tabular form. When, for example, we used SpliceCenter's batch siRNA-Check to process the Cancer Genome Anatomy Project's large-scale shRNA library, we found that only 59% of the 50,766 shRNAs in the library target all known splice variants of the target gene, 32% target some but not all, and 9% do not target any currently annotated transcript. Conclusion SpliceCenter provides unique, user-friendly applications for assessing the impact of transcript variation on the design and interpretation of RT-PCR, RNAi, gene expression microarrays, antibody-based detection, and mass spectrometry proteomics. The tools are intended for use by bench biologists as well as bioinformaticists. PMID:18638396

Association of tumor-infiltrating T-cell density with molecular subtype, racial ancestry and clinical outcomes in prostate cancer.

PubMed

Kaur, Harsimar B; Guedes, Liana B; Lu, Jiayun; Maldonado, Laneisha; Reitz, Logan; Barber, John R; De Marzo, Angelo M; Tosoian, Jeffrey J; Tomlins, Scott A; Schaeffer, Edward M; Joshu, Corinne E; Sfanos, Karen S; Lotan, Tamara L

2018-05-30

The inflammatory microenvironment plays an important role in the pathogenesis and progression of tumors and may be associated with somatic genomic alterations. We examined the association of tumor-infiltrating T-cell density with clinical-pathologic variables, tumor molecular subtype, and oncologic outcomes in surgically treated primary prostate cancer occurring in patients of European-American or African-American ancestry. We evaluated 312 primary prostate tumors, enriched for patients with African-American ancestry and high grade disease. Tissue microarrays were immunostained for CD3, CD8, and FOXP3 and were previously immunostained for ERG and PTEN using genetically validated protocols. Image analysis for quantification of T-cell density in tissue microarray tumor spots was performed. Automated quantification of T-cell densities in tumor-containing regions of tissue microarray spots and standard histologic sections were correlated (r = 0.73, p < 0.00001) and there was good agreement between visual and automated T-cell density counts on tissue microarray spots (r = 0.93, p < 0.00001). There was a significant correlation between CD3+, CD8+, and FOXP3+ T-cell densities (p < 0.00001), but these were not associated with most clinical or pathologic variables. Increased T-cell density was significantly associated with ERG positivity (median 309 vs. 188 CD3+ T cells/mm 2 ; p = 0.0004) and also with PTEN loss (median 317 vs. 192 CD3+ T cells/mm 2 ; p = 0.001) in the combined cohort of matched European-American and African-American ancestry patients. The same association or a similar trend was present in patients of both ancestries when analyzed separately. When the African-American patients from the matched race set were combined with a separate high grade set of African-American cases, there was a weak association of increased FOXP3+ T-cell densities with increased risk of metastasis in multivariable analysis. Though high T-cell density is associated with specific molecular subclasses of prostate cancer, we did not find an association of T-cell density with racial ancestry.

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

Eureka-DMA: an easy-to-operate graphical user interface for fast comprehensive investigation and analysis of DNA microarray data.

PubMed

Abelson, Sagi

2014-02-24

In the past decade, the field of molecular biology has become increasingly quantitative; rapid development of new technologies enables researchers to investigate and address fundamental issues quickly and in an efficient manner which were once impossible. Among these technologies, DNA microarray provides methodology for many applications such as gene discovery, diseases diagnosis, drug development and toxicological research and it has been used increasingly since it first emerged. Multiple tools have been developed to interpret the high-throughput data produced by microarrays. However, many times, less consideration has been given to the fact that an extensive and effective interpretation requires close interplay between the bioinformaticians who analyze the data and the biologists who generate it. To bridge this gap and to simplify the usability of such tools we developed Eureka-DMA - an easy-to-operate graphical user interface that allows bioinformaticians and bench-biologists alike to initiate analyses as well as to investigate the data produced by DNA microarrays. In this paper, we describe Eureka-DMA, a user-friendly software that comprises a set of methods for the interpretation of gene expression arrays. Eureka-DMA includes methods for the identification of genes with differential expression between conditions; it searches for enriched pathways and gene ontology terms and combines them with other relevant features. It thus enables the full understanding of the data for following testing as well as generating new hypotheses. Here we show two analyses, demonstrating examples of how Eureka-DMA can be used and its capability to produce relevant and reliable results. We have integrated several elementary expression analysis tools to provide a unified interface for their implementation. Eureka-DMA's simple graphical user interface provides effective and efficient framework in which the investigator has the full set of tools for the visualization and interpretation of the data with the option of exporting the analysis results for later use in other platforms. Eureka-DMA is freely available for academic users and can be downloaded at http://blue-meduza.org/Eureka-DMA.

NCBI GEO: archive for functional genomics data sets--update.

PubMed

Barrett, Tanya; Wilhite, Stephen E; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F; Tomashevsky, Maxim; Marshall, Kimberly A; Phillippy, Katherine H; Sherman, Patti M; Holko, Michelle; Yefanov, Andrey; Lee, Hyeseung; Zhang, Naigong; Robertson, Cynthia L; Serova, Nadezhda; Davis, Sean; Soboleva, Alexandra

2013-01-01

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.

Differential co-expression analysis of rheumatoid arthritis with microarray data.

PubMed

Wang, Kunpeng; Zhao, Liqiang; Liu, Xuefeng; Hao, Zhenyong; Zhou, Yong; Yang, Chuandong; Li, Hongqiang

2014-11-01

The aim of the present study was to investigate the underlying molecular mechanisms of rheumatoid arthritis (RA) using microarray expression profiles from osteoarthritis and RA patients, to improve diagnosis and treatment strategies for the condition. The gene expression profile of GSE27390 was downloaded from Gene Expression Omnibus, including 19 samples from patients with RA (n=9) or osteoarthritis (n=10). Firstly, the differentially expressed genes (DEGs) were obtained with the thresholds of |logFC|>1.0 and P<0.05, using the t‑test method in LIMMA package. Then, differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) were screened with q<0.25 by the differential coexpression analysis and differential regulation analysis of gene expression microarray data package. Secondly, pathway enrichment analysis for DCGs was performed by the Database for Annotation, Visualization and Integrated Discovery and the DCLs associated with RA were selected by comparing the obtained DCLs with known transcription factor (TF)-targets in the TRANSFAC database. Finally, the obtained TFs were mapped to the known TF-targets to construct the network using cytoscape software. A total of 1755 DEGs, 457 DCGs and 101988 DCLs were achieved and there were 20 TFs in the obtained six TF-target relations (STAT3-TNF, PBX1‑PLAU, SOCS3-STAT3, GATA1-ETS2, ETS1-ICAM4 and CEBPE‑GATA1) and 457 DCGs. A number of TF-target relations in the constructed network were not within DCLs when the TF and target gene were DCGs. The identified TFs may have an important role in the pathogenesis of RA and have the potential to be used as biomarkers for the development of novel diagnostic and therapeutic strategies for RA.

How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals.

PubMed

Wang, Xiaoying; Peelen, Marius V; Han, Zaizhu; He, Chenxi; Caramazza, Alfonso; Bi, Yanchao

2015-09-09

Classical animal visual deprivation studies and human neuroimaging studies have shown that visual experience plays a critical role in shaping the functionality and connectivity of the visual cortex. Interestingly, recent studies have additionally reported circumscribed regions in the visual cortex in which functional selectivity was remarkably similar in individuals with and without visual experience. Here, by directly comparing resting-state and task-based fMRI data in congenitally blind and sighted human subjects, we obtained large-scale continuous maps of the degree to which connectional and functional "fingerprints" of ventral visual cortex depend on visual experience. We found a close agreement between connectional and functional maps, pointing to a strong interdependence of connectivity and function. Visual experience (or the absence thereof) had a pronounced effect on the resting-state connectivity and functional response profile of occipital cortex and the posterior lateral fusiform gyrus. By contrast, connectional and functional fingerprints in the anterior medial and posterior lateral parts of the ventral visual cortex were statistically indistinguishable between blind and sighted individuals. These results provide a large-scale mapping of the influence of visual experience on the development of both functional and connectivity properties of visual cortex, which serves as a basis for the formulation of new hypotheses regarding the functionality and plasticity of specific subregions. Significance statement: How is the functionality and connectivity of the visual cortex shaped by visual experience? By directly comparing resting-state and task-based fMRI data in congenitally blind and sighted subjects, we obtained large-scale continuous maps of the degree to which connectional and functional "fingerprints" of ventral visual cortex depend on visual experience. In addition to revealing regions that are strongly dependent on visual experience (early visual cortex and posterior fusiform gyrus), our results showed regions in which connectional and functional patterns are highly similar in blind and sighted individuals (anterior medial and posterior lateral ventral occipital temporal cortex). These results serve as a basis for the formulation of new hypotheses regarding the functionality and plasticity of specific subregions of the visual cortex. Copyright © 2015 the authors 0270-6474/15/3512545-15$15.00/0.

A reference guide for tree analysis and visualization

PubMed Central

2010-01-01

The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis. PMID:20175922

Statistical methodology for the analysis of dye-switch microarray experiments

PubMed Central

Mary-Huard, Tristan; Aubert, Julie; Mansouri-Attia, Nadera; Sandra, Olivier; Daudin, Jean-Jacques

2008-01-01

Background In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with Cy3 and once with Cy5. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis. Results We present two original statistical procedures for the statistical analysis of individually balanced designs. These procedures are compared with the usual ML and REML mixed model procedures proposed in most statistical toolboxes, on both simulated and real data. Conclusion The UP procedure we propose as an alternative to usual mixed model procedures is more efficient and significantly faster to compute. This result provides some useful guidelines for the analysis of complex designs. PMID:18271965

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays.

PubMed

McLachlan, G J; Bean, R W; Jones, L Ben-Tovim

2006-07-01

An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

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

PubMed

Hatazawa, Yukino; Minami, Kimiko; Yoshimura, Ryoji; Onishi, Takumi; Manio, Mark Christian; Inoue, Kazuo; Sawada, Naoki; Suzuki, Osamu; Miura, Shinji; Kamei, Yasutomi

2016-12-09

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 with 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. Copyright © 2016 Elsevier Inc. All rights reserved.

Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.

PubMed

Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E; Allen, Peter J; Sempere, Lorenzo F; Haab, Brian B

2015-10-06

Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu's method for selected images. SFT promises to advance the goal of full automation in image analysis.

Power enhancement via multivariate outlier testing with gene expression arrays.

PubMed

Asare, Adam L; Gao, Zhong; Carey, Vincent J; Wang, Richard; Seyfert-Margolis, Vicki

2009-01-01

As the use of microarrays in human studies continues to increase, stringent quality assurance is necessary to ensure accurate experimental interpretation. We present a formal approach for microarray quality assessment that is based on dimension reduction of established measures of signal and noise components of expression followed by parametric multivariate outlier testing. We applied our approach to several data resources. First, as a negative control, we found that the Affymetrix and Illumina contributions to MAQC data were free from outliers at a nominal outlier flagging rate of alpha=0.01. Second, we created a tunable framework for artificially corrupting intensity data from the Affymetrix Latin Square spike-in experiment to allow investigation of sensitivity and specificity of quality assurance (QA) criteria. Third, we applied the procedure to 507 Affymetrix microarray GeneChips processed with RNA from human peripheral blood samples. We show that exclusion of arrays by this approach substantially increases inferential power, or the ability to detect differential expression, in large clinical studies. http://bioconductor.org/packages/2.3/bioc/html/arrayMvout.html and http://bioconductor.org/packages/2.3/bioc/html/affyContam.html affyContam (credentials: readonly/readonly)

Comparing transformation methods for DNA microarray data

PubMed Central

Thygesen, Helene H; Zwinderman, Aeilko H

2004-01-01

Background When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer. Results We used the ratio between biological variance and measurement variance (which is an F-like statistic) as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters. Conclusions The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method. PMID:15202953

Comparing transformation methods for DNA microarray data.

PubMed

Thygesen, Helene H; Zwinderman, Aeilko H

2004-06-17

When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer. We used the ratio between biological variance and measurement variance (which is an F-like statistic) as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters. The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method.

Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data

PubMed Central

Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M.; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E.; Allen, Peter J.; Sempere, Lorenzo F.; Haab, Brian B.

2016-01-01

Certain experiments involve the high-throughput quantification of image data, thus requiring algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multi-color, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu’s method for selected images. SFT promises to advance the goal of full automation in image analysis. PMID:26339978

Expert Knowledge Influences Decision-Making for Couples Receiving Positive Prenatal Chromosomal Microarray Testing Results.

PubMed

Rubel, M A; Werner-Lin, A; Barg, F K; Bernhardt, B A

2017-09-01

To assess how participants receiving abnormal prenatal genetic testing results seek information and understand the implications of results, 27 US female patients and 12 of their male partners receiving positive prenatal microarray testing results completed semi-structured phone interviews. These interviews documented participant experiences with chromosomal microarray testing, understanding of and emotional response to receiving results, factors affecting decision-making about testing and pregnancy termination, and psychosocial needs throughout the testing process. Interview data were analyzed using a modified grounded theory approach. In the absence of certainty about the implications of results, understanding of results is shaped by biomedical expert knowledge (BEK) and cultural expert knowledge (CEK). When there is a dearth of BEK, as in the case of receiving results of uncertain significance, participants rely on CEK, including religious/spiritual beliefs, "gut instinct," embodied knowledge, and social network informants. CEK is a powerful platform to guide understanding of prenatal genetic testing results. The utility of culturally situated expert knowledge during testing uncertainty emphasizes that decision-making occurs within discourses beyond the biomedical domain. These forms of "knowing" may be integrated into clinical consideration of efficacious patient assessment and counseling.

Rational design of DNA sequences for nanotechnology, microarrays and molecular computers using Eulerian graphs.

PubMed

Pancoska, Petr; Moravek, Zdenek; Moll, Ute M

2004-01-01

Nucleic acids are molecules of choice for both established and emerging nanoscale technologies. These technologies benefit from large functional densities of 'DNA processing elements' that can be readily manufactured. To achieve the desired functionality, polynucleotide sequences are currently designed by a process that involves tedious and laborious filtering of potential candidates against a series of requirements and parameters. Here, we present a complete novel methodology for the rapid rational design of large sets of DNA sequences. This method allows for the direct implementation of very complex and detailed requirements for the generated sequences, thus avoiding 'brute force' filtering. At the same time, these sequences have narrow distributions of melting temperatures. The molecular part of the design process can be done without computer assistance, using an efficient 'human engineering' approach by drawing a single blueprint graph that represents all generated sequences. Moreover, the method eliminates the necessity for extensive thermodynamic calculations. Melting temperature can be calculated only once (or not at all). In addition, the isostability of the sequences is independent of the selection of a particular set of thermodynamic parameters. Applications are presented for DNA sequence designs for microarrays, universal microarray zip sequences and electron transfer experiments.

Microarrays for the evaluation of cell-biomaterial surface interactions

NASA Astrophysics Data System (ADS)

Thissen, H.; Johnson, G.; McFarland, G.; Verbiest, B. C. H.; Gengenbach, T.; Voelcker, N. H.

2007-01-01

The evaluation of cell-material surface interactions is important for the design of novel biomaterials which are used in a variety of biomedical applications. While traditional in vitro test methods have routinely used samples of relatively large size, microarrays representing different biomaterials offer many advantages, including high throughput and reduced sample handling. Here, we describe the simultaneous cell-based testing of matrices of polymeric biomaterials, arrayed on glass slides with a low cell-attachment background coating. Arrays were constructed using a microarray robot at 6 fold redundancy with solid pins having a diameter of 375 μm. Printed solutions contained at least one monomer, an initiator and a bifunctional crosslinker. After subsequent UV polymerisation, the arrays were washed and characterised by X-ray photoelectron spectroscopy. Cell culture experiments were carried out over 24 hours using HeLa cells. After labelling with CellTracker ® Green for the final hour of incubation and subsequent fixation, the arrays were scanned. In addition, individual spots were also viewed by fluorescence microscopy. The evaluation of cell-surface interactions in high-throughput assays as demonstrated here is a key enabling technology for the effective development of future biomaterials.

The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data

PubMed Central

Kang, Hyunseok P.; Borromeo, Charles D.; Berman, Jules J.; Becich, Michael J.

2010-01-01

Background: Tissue microarrays (TMAs) are enormously useful tools for translational research, but incompatibilities in database systems between various researchers and institutions prevent the efficient sharing of data that could help realize their full potential. Resource Description Framework (RDF) provides a flexible method to represent knowledge in triples, which take the form Subject-Predicate-Object. All data resources are described using Uniform Resource Identifiers (URIs), which are global in scope. We present an OWL (Web Ontology Language) schema that expands upon the TMA data exchange specification to address this issue and assist in data sharing and integration. Methods: A minimal OWL schema was designed containing only concepts specific to TMA experiments. More general data elements were incorporated from predefined ontologies such as the NCI thesaurus. URIs were assigned using the Linked Data format. Results: We present examples of files utilizing the schema and conversion of XML data (similar to the TMA DES) to OWL. Conclusion: By utilizing predefined ontologies and global unique identifiers, this OWL schema provides a solution to the limitations of XML, which represents concepts defined in a localized setting. This will help increase the utilization of tissue resources, facilitating collaborative translational research efforts. PMID:20805954

A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.

PubMed

Bennet, Jaison; Ganaprakasam, Chilambuchelvan Arul; Arputharaj, Kannan

2014-01-01

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

cDNA microarray analysis of esophageal cancer: discoveries and prospects.

PubMed

Shimada, Yutaka; Sato, Fumiaki; Shimizu, Kazuharu; Tsujimoto, Gozoh; Tsukada, Kazuhiro

2009-07-01

Recent progress in molecular biology has revealed many genetic and epigenetic alterations that are involved in the development and progression of esophageal cancer. Microarray analysis has also revealed several genetic networks that are involved in esophageal cancer. However, clinical application of microarray techniques and use of microarray data have not yet occurred. In this review, we focus on the recent developments and problems with microarray analysis of esophageal cancer.

Manufacturing of microarrays.

PubMed

Petersen, David W; Kawasaki, Ernest S

2007-01-01

DNA microarray technology has become a powerful tool in the arsenal of the molecular biologist. Capitalizing on high precision robotics and the wealth of DNA sequences annotated from the genomes of a large number of organisms, the manufacture of microarrays is now possible for the average academic laboratory with the funds and motivation. Microarray production requires attention to both biological and physical resources, including DNA libraries, robotics, and qualified personnel. While the fabrication of microarrays is a very labor-intensive process, production of quality microarrays individually tailored on a project-by-project basis will help researchers shed light on future scientific questions.

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

The two-visual-systems hypothesis and the perspectival features of visual experience.

PubMed

Foley, Robert T; Whitwell, Robert L; Goodale, Melvyn A

2015-09-01

Some critics of the two-visual-systems hypothesis (TVSH) argue that it is incompatible with the fundamentally egocentric nature of visual experience (what we call the 'perspectival account'). The TVSH proposes that the ventral stream, which delivers up our visual experience of the world, works in an allocentric frame of reference, whereas the dorsal stream, which mediates the visual control of action, uses egocentric frames of reference. Given that the TVSH is also committed to the claim that dorsal-stream processing does not contribute to the contents of visual experience, it has been argued that the TVSH cannot account for the egocentric features of our visual experience. This argument, however, rests on a misunderstanding about how the operations mediating action and the operations mediating perception are specified in the TVSH. In this article, we emphasize the importance of the 'outputs' of the two-systems to the specification of their respective operations. We argue that once this point is appreciated, it becomes evident that the TVSH is entirely compatible with a perspectival account of visual experience. Copyright © 2015 Elsevier Inc. All rights reserved.

Chromosomal microarray analysis of consecutive individuals with autism spectrum disorders or learning disability presenting for genetic services.

PubMed

Roberts, Jennifer L; Hovanes, Karine; Dasouki, Majed; Manzardo, Ann M; Butler, Merlin G

2014-02-01

Chromosomal microarray analysis is now commonly used in clinical practice to identify copy number variants (CNVs) in the human genome. We report our experience with the use of the 105 K and 180K oligonucleotide microarrays in 215 consecutive patients referred with either autism or autism spectrum disorders (ASD) or developmental delay/learning disability for genetic services at the University of Kansas Medical Center during the past 4 years (2009-2012). Of the 215 patients [140 males and 75 females (male/female ratio=1.87); 65 with ASD and 150 with learning disability], abnormal microarray results were seen in 45 individuals (21%) with a total of 49 CNVs. Of these findings, 32 represented a known diagnostic CNV contributing to the clinical presentation and 17 represented non-diagnostic CNVs (variants of unknown significance). Thirteen patients with ASD had a total of 14 CNVs, 6 CNVs recognized as diagnostic and 8 as non-diagnostic. The most common chromosome involved in the ASD group was chromosome 15. For those with a learning disability, 32 patients had a total of 35 CNVs. Twenty-six of the 35 CNVs were classified as a known diagnostic CNV, usually a deletion (n=20). Nine CNVs were classified as an unknown non-diagnostic CNV, usually a duplication (n=8). For the learning disability subgroup, chromosomes 2 and 22 were most involved. Thirteen out of 65 patients (20%) with ASD had a CNV compared with 32 out of 150 patients (21%) with a learning disability. The frequency of chromosomal microarray abnormalities compared by subject group or gender was not statistically different. A higher percentage of individuals with a learning disability had clinical findings of seizures, dysmorphic features and microcephaly, but not statistically significant. While both groups contained more males than females, a significantly higher percentage of males were present in the ASD group. © 2013 Elsevier B.V. All rights reserved.

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

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/.

Use of lectin microarray to differentiate gastric cancer from gastric ulcer

PubMed Central

Huang, Wei-Li; Li, Yang-Guang; Lv, Yong-Chen; Guan, Xiao-Hui; Ji, Hui-Fan; Chi, Bao-Rong

2014-01-01

AIM: To investigate the feasibility of lectin microarray for differentiating gastric cancer from gastric ulcer. METHODS: Twenty cases of human gastric cancer tissue and 20 cases of human gastric ulcer tissue were collected and processed. Protein was extracted from the frozen tissues and stored. The lectins were dissolved in buffer, and the sugar-binding specificities of lectins and the layout of the lectin microarray were summarized. The median of the effective data points for each lectin was globally normalized to the sum of medians of all effective data points for each lectin in one block. Formalin-fixed paraffin-embedded gastric cancer tissues and their corresponding gastric ulcer tissues were subjected to Ag retrieval. Biotinylated lectin was used as the primary antibody and HRP-streptavidin as the secondary antibody. The glycopatterns of glycoprotein in gastric cancer and gastric ulcer specimens were determined by lectin microarray, and then validated by lectin histochemistry. Data are presented as mean ± SD for the indicated number of independent experiments. RESULTS: The glycosylation level of gastric cancer was significantly higher than that in ulcer. In gastric cancer, most of the lectin binders showed positive signals and the intensity of the signals was stronger, whereas the opposite was the case for ulcers. Significant differences in the pathological score of the two lectins were apparent between ulcer and gastric cancer tissues using the same lectin. For MPL and VVA, all types of gastric cancer detected showed stronger staining and a higher positive rate in comparison with ulcer, especially in the case of signet ring cell carcinoma and intra-mucosal carcinoma. GalNAc bound to MPL showed a significant increase. A statistically significant association between MPL and gastric cancer was observed. As with MPL, there were significant differences in VVA staining between gastric cancer and ulcer. CONCLUSION: Lectin microarray can differentiate the different glycopatterns in gastric cancer and gastric ulcer, and the lectins MPL and VVA can be used as biomarkers. PMID:24833877

SEURAT: visual analytics for the integrated analysis of microarray data.

PubMed

Gribov, Alexander; Sill, Martin; Lück, Sonja; Rücker, Frank; Döhner, Konstanze; Bullinger, Lars; Benner, Axel; Unwin, Antony

2010-06-03

In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.

Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays

PubMed Central

2011-01-01

Introduction The human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies. Methods HER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECT™, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence in situ hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested. Results The pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio). Conclusion HER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases. PMID:21943197

Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays.

PubMed

Laurinaviciene, Aida; Dasevicius, Darius; Ostapenko, Valerijus; Jarmalaite, Sonata; Lazutka, Juozas; Laurinavicius, Arvydas

2011-09-23

The human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies. HER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECT, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence in situ hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested. The pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio). HER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases.

Dynamic Visualization of Co-expression in Systems Genetics Data

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

New, Joshua Ryan; Huang, Jian; Chesler, Elissa J

2008-01-01

Biologists hope to address grand scientific challenges by exploring the abundance of data made available through modern microarray technology and other high-throughput techniques. The impact of this data, however, is limited unless researchers can effectively assimilate such complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression require novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These tools should allow biologists to develop an intuitive understanding of the structure of biologicalmore » networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.« less

THE ABRF MARG MICROARRAY SURVEY 2005: TAKING THE PULSE ON THE MICROARRAY FIELD

EPA Science Inventory

Over the past several years microarray technology has evolved into a critical component of any discovery based program. Since 1999, the Association of Biomolecular Resource Facilities (ABRF) Microarray Research Group (MARG) has conducted biennial surveys designed to generate a pr...

Development of a Digital Microarray with Interferometric Reflectance Imaging

NASA Astrophysics Data System (ADS)

Sevenler, Derin

This dissertation describes a new type of molecular assay for nucleic acids and proteins. We call this technique a digital microarray since it is conceptually similar to conventional fluorescence microarrays, yet it performs enumerative ('digital') counting of the number captured molecules. Digital microarrays are approximately 10,000-fold more sensitive than fluorescence microarrays, yet maintain all of the strengths of the platform including low cost and high multiplexing (i.e., many different tests on the same sample simultaneously). Digital microarrays use gold nanorods to label the captured target molecules. Each gold nanorod on the array is individually detected based on its light scattering, with an interferometric microscopy technique called SP-IRIS. Our optimized high-throughput version of SP-IRIS is able to scan a typical array of 500 spots in less than 10 minutes. Digital DNA microarrays may have utility in applications where sequencing is prohibitively expensive or slow. As an example, we describe a digital microarray assay for gene expression markers of bacterial drug resistance.

Protein microarray with horseradish peroxidase chemiluminescence for quantification of serum α-fetoprotein.

PubMed

Zhao, Yuanshun; Zhang, Yonghong; Lin, Dongdong; Li, Kang; Yin, Chengzeng; Liu, Xiuhong; Jin, Boxun; Sun, Libo; Liu, Jinhua; Zhang, Aiying; Li, Ning

2015-10-01

To develop and evaluate a protein microarray assay with horseradish peroxidase (HRP) chemiluminescence for quantification of α-fetoprotein (AFP) in serum from patients with hepatocellular carcinoma (HCC). A protein microarray assay for AFP was developed. Serum was collected from patients with HCC and healthy control subjects. AFP was quantified using protein microarray and enzyme-linked immunosorbent assay (ELISA). Serum AFP concentrations determined via protein microarray were positively correlated (r = 0.973) with those determined via ELISA in patients with HCC (n = 60) and healthy control subjects (n = 30). Protein microarray showed 80% sensitivity and 100% specificity for HCC diagnosis. ELISA had 83.3% sensitivity and 100% specificity. Protein microarray effectively distinguished between patients with HCC and healthy control subjects (area under ROC curve 0.974; 95% CI 0.000, 1.000). Protein microarray is a rapid, simple and low-cost alternative to ELISA for detecting AFP in human serum. © The Author(s) 2015.

Microarray and differential display identify genes involved in jasmonate-dependent anther development.

PubMed

Mandaokar, Ajin; Kumar, V Dinesh; Amway, Matt; Browse, John

2003-07-01

Jasmonate (JA) is a signaling compound essential for anther development and pollen fertility in Arabidopsis. Mutations that block the pathway of JA synthesis result into male sterility. To understand the processes of anther and pollen maturation, we used microarray and differential display approaches to compare gene expression pattern in anthers of wild-type Arabidopsis and the male-sterile mutant, opr3. Microarray experiment revealed 25 genes that were up-regulated more than 1.8-fold in wild-type anthers as compared to mutant anthers. Experiments based on differential display identified 13 additional genes up-regulated in wild-type anthers compared to opr3 for a total of 38 differentially expressed genes. Searches of the Arabidopsis and non-redundant databases disclosed known or likely functions for 28 of the 38 genes identified, while 10 genes encode proteins of unknown function. Northern blot analysis of eight representative clones as probes confirmed low expression in opr3 anthers compared with wild-type anthers. JA responsiveness of these same genes was also investigated by northern blot analysis of anther RNA isolated from wild-type and opr3 plants, In these experiments, four genes were induced in opr3 anthers within 0.5-1 h of JA treatment while the remaining genes were up-regulated only 1-8 h after JA application. None of these genes was induced by JA in anthers of the coil mutant that is deficient in JA responsiveness. The four early-induced genes in opr3 encode lipoxygenase, a putative bHLH transcription factor, epithiospecifier protein and an unknown protein. We propose that these and other early components may be involved in JA signaling and in the initiation of developmental processes. The four late genes encode an extensin-like protein, a peptide transporter and two unknown proteins, which may represent components required later in anther and pollen maturation. Transcript profiling has provided a successful approach to identify genes involved in anther and pollen maturation in Arabidopsis.

Three clinical experiences with SNP array results consistent with parental incest: a narrative with lessons learned.

PubMed

Helm, Benjamin M; Langley, Katherine; Spangler, Brooke; Vergano, Samantha

2014-08-01

Single nucleotide polymorphism microarrays have the ability to reveal parental consanguinity which may or may not be known to healthcare providers. Consanguinity can have significant implications for the health of patients and for individual and family psychosocial well-being. These results often present ethical and legal dilemmas that can have important ramifications. Unexpected consanguinity can be confounding to healthcare professionals who may be unprepared to handle these results or to communicate them to families or other appropriate representatives. There are few published accounts of experiences with consanguinity and SNP arrays. In this paper we discuss three cases where molecular evidence of parental incest was identified by SNP microarray. We hope to further highlight consanguinity as a potential incidental finding, how the cases were handled by the clinical team, and what resources were found to be most helpful. This paper aims to contribute further to professional discourse on incidental findings with genomic technology and how they were addressed clinically. These experiences may provide some guidance on how others can prepare for these findings and help improve practice. As genetic and genomic testing is utilized more by non-genetics providers, we also hope to inform about the importance of engaging with geneticists and genetic counselors when addressing these findings.

Differential Gene Expression in Primary Human Skin Keratinocytes and Fibroblasts in Response to Ionizing Radiation

PubMed Central

Warters, Raymond L.; Packard, Ann T.; Kramer, Gwen F.; Gaffney, David K.; Moos, Philip J.

2009-01-01

Although skin is usually exposed during human exposures to ionizing radiation, there have been no thorough examinations of the transcriptional response of skin fibroblasts and keratinocytes to radiation. The transcriptional response of quiescent primary fibroblasts and keratinocytes exposed to from 10 cGy to 5 Gy and collected 4 h after treatment was examined. RNA was isolated and examined by microarray analysis for changes in the levels of gene expression. Exposure to ionizing radiation altered the expression of 279 genes across both cell types. Changes in RNA expression could be arranged into three main categories: (1) changes in keratinocytes but not in fibroblasts, (2) changes in fibroblasts but not in keratinocytes, and (3) changes in both. All of these changes were primarily of p53 target genes. Similar radiation-induced changes were induced in immortalized fibroblasts or keratinocytes. In separate experiments, protein was collected and analyzed by Western blotting for expression of proteins observed in microarray experiments to be overexpressed at the mRNA level. Both Q-PCR and Western blot analysis experiments validated these transcription changes. Our results are consistent with changes in the expression of p53 target genes as indicating the magnitude of cell responses to ionizing radiation. PMID:19580510

Distributional fold change test – a statistical approach for detecting differential expression in microarray experiments

PubMed Central

2012-01-01

Background Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment. Results A new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best – on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets. Conclusions The distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set. PMID:23122055

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

PubMed

Vadigepalli, Rajanikanth; Chakravarthula, Praveen; Zak, Daniel E; Schwaber, James S; Gonye, Gregory E

2003-01-01

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

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

Normal uniform mixture differential gene expression detection for cDNA microarrays

PubMed Central

Dean, Nema; Raftery, Adrian E

2005-01-01

Background One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic. Results We propose a simple method, Normal Uniform Differential Gene Expression (NUDGE) detection for finding differentially expressed genes in cDNA microarrays. The method uses a simple univariate normal-uniform mixture model, in combination with new normalization methods for spread as well as mean that extend the lowess normalization of Dudoit, Yang, Callow and Speed (2002) [1]. It takes account of multiple testing, and gives probabilities of differential expression as part of its output. It can be applied to either single-slide or replicated experiments, and it is very fast. Three datasets are analyzed using NUDGE, and the results are compared to those given by other popular methods: unadjusted and Bonferroni-adjusted t tests, Significance Analysis of Microarrays (SAM), and Empirical Bayes for microarrays (EBarrays) with both Gamma-Gamma and Lognormal-Normal models. Conclusion The method gives a high probability of differential expression to genes known/suspected a priori to be differentially expressed and a low probability to the others. In terms of known false positives and false negatives, the method outperforms all multiple-replicate methods except for the Gamma-Gamma EBarrays method to which it offers comparable results with the added advantages of greater simplicity, speed, fewer assumptions and applicability to the single replicate case. An R package called nudge to implement the methods in this paper will be made available soon at . PMID:16011807

Overexpression of Plasminogen Activator Inhibitor-1 in Advanced Gastric Cancer with Aggressive Lymph Node Metastasis

PubMed Central

Suh, Yun-Suhk; Yu, Jieun; Kim, Byung Chul; Choi, Boram; Han, Tae-Su; Ahn, Hye Seong; Kong, Seong-Ho; Lee, Hyuk-Joon; Kim, Woo Ho; Yang, Han-Kwang

2015-01-01

Purpose The purpose of this study is to investigate differentially expressed genes using DNA microarray between advanced gastric cancer (AGC) with aggressive lymph node (LN) metastasis and that with a more advanced tumor stage but without LN metastasis. Materials and Methods Five sample pairs of gastric cancer tissue and normal gastric mucosa were taken from three patients with T3N3 stage (highN) and two with T4N0 stage (lowN). Data from triplicate DNA microarray experiments were analyzed, and candidate genes were identified using a volcano plot that showed ≥ 2-fold differential expression and were significant by Welch's t test (p < 0.05) between highN and lowN. Those selected genes were validated independently by reverse-transcriptase–polymerase chain reaction (RT-PCR) using five AGC patients, and tissue-microarray (TMA) comprising 47 AGC patients. Results CFTR, LAMC2, SERPINE2, F2R, MMP7, FN1, TIMP1, plasminogen activator inhibitor-1 (PAI-1), ITGB8, SDS, and TMPRSS4 were commonly up-regulated over 2-fold in highN. REG3A, CD24, ITLN1, and WBP5 were commonly down-regulated over 2-fold in lowN. Among these genes, overexpression of PAI-1 was validated by RT-PCR, and TMA showed 16.7% (7/42) PAI-1 expression in T3N3, but none (0/5) in T4N0 (p=0.393). Conclusion DNA microarray analysis and validation by RT-PCR and TMA showed that overexpression of PAI-1 is related to aggressive LN metastasis in AGC. PMID:25687870

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

High-throughput investigation of endothelial-to-mesenchymal transformation (EndMT) with combinatorial cellular microarrays.

PubMed

Wang, Zongjie; Calpe, Blaise; Zerdani, Jalil; Lee, Youngsang; Oh, Jonghyun; Bae, Hojae; Khademhosseini, Ali; Kim, Keekyoung

2016-07-01

In the developing heart, a specific subset of endocardium undergoes an endothelial-to-mesenchymal transformation (EndMT) thus forming nascent valve leaflets. Extracellular matrix (ECM) proteins and growth factors (GFs) play important roles in regulating EndMT but the combinatorial effect of GFs with ECM proteins is less well understood. Here we use microscale engineering techniques to create single, binary, and tertiary component microenvironments to investigate the combinatorial effects of ECM proteins and GFs on the attachment and transformation of adult ovine mitral valve endothelial cells to a mesenchymal phenotype. With the combinatorial microenvironment microarrays, we utilized 60 different combinations of ECM proteins (Fibronectin, Collagen I, II, IV, Laminin) and GFs (TGF-β1, bFGF, VEGF) and were able to identify new microenvironmental conditions capable of modulating EndMT in MVECs. Experimental results indicated that TGF-β1 significantly upregulated the EndMT while either bFGF or VEGF downregulated EndMT process markedly. Also, ECM proteins could influence both the attachment of MVECs and the response of MVECs to GFs. In terms of attachment, fibronectin is significantly better for the adhesion of MVECs among the five tested proteins. Overall collagen IV and fibronectin appeared to play important roles in promoting EndMT process. Great consistency between macroscale and microarrayed experiments and present studies demonstrates that high-throughput cellular microarrays are a promising approach to study the regulation of EndMT in valvular endothelium. Biotechnol. Bioeng. 2016;113: 1403-1412. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

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,…

Investigating the visual span in comparative search: the effects of task difficulty and divided attention.

PubMed

Pomplun, M; Reingold, E M; Shen, J

2001-09-01

In three experiments, participants' visual span was measured in a comparative visual search task in which they had to detect a local match or mismatch between two displays presented side by side. Experiment 1 manipulated the difficulty of the comparative visual search task by contrasting a mismatch detection task with a substantially more difficult match detection task. In Experiment 2, participants were tested in a single-task condition involving only the visual task and a dual-task condition in which they concurrently performed an auditory task. Finally, in Experiment 3, participants performed two dual-task conditions, which differed in the difficulty of the concurrent auditory task. Both the comparative search task difficulty (Experiment 1) and the divided attention manipulation (Experiments 2 and 3) produced strong effects on visual span size.

Repetition blindness and illusory conjunctions: errors in binding visual types with visual tokens.

PubMed

Kanwisher, N

1991-05-01

Repetition blindness (Kanwisher, 1986, 1987) has been defined as the failure to detect or recall repetitions of words presented in rapid serial visual presentation (RSVP). The experiments presented here suggest that repetition blindness (RB) is a more general visual phenomenon, and examine its relationship to feature integration theory (Treisman & Gelade, 1980). Experiment 1 shows RB for letters distributed through space, time, or both. Experiment 2 demonstrates RB for repeated colors in RSVP lists. In Experiments 3 and 4, RB was found for repeated letters and colors in spatial arrays. Experiment 5 provides evidence that the mental representations of discrete objects (called "visual tokens" here) that are necessary to detect visual repetitions (Kanwisher, 1987) are the same as the "object files" (Kahneman & Treisman, 1984) in which visual features are conjoined. In Experiment 6, repetition blindness for the second occurrence of a repeated letter resulted only when the first occurrence was attended to. The overall results suggest that a general dissociation between types and tokens in visual information processing can account for both repetition blindness and illusory conjunctions.

Words, shape, visual search and visual working memory in 3-year-old children.

PubMed

Vales, Catarina; Smith, Linda B

2015-01-01

Do words cue children's visual attention, and if so, what are the relevant mechanisms? Across four experiments, 3-year-old children (N = 163) were tested in visual search tasks in which targets were cued with only a visual preview versus a visual preview and a spoken name. The experiments were designed to determine whether labels facilitated search times and to examine one route through which labels could have their effect: By influencing the visual working memory representation of the target. The targets and distractors were pictures of instances of basic-level known categories and the labels were the common name for the target category. We predicted that the label would enhance the visual working memory representation of the target object, guiding attention to objects that better matched the target representation. Experiments 1 and 2 used conjunctive search tasks, and Experiment 3 varied shape discriminability between targets and distractors. Experiment 4 compared the effects of labels to repeated presentations of the visual target, which should also influence the working memory representation of the target. The overall pattern fits contemporary theories of how the contents of visual working memory interact with visual search and attention, and shows that even in very young children heard words affect the processing of visual information. © 2014 John Wiley & Sons Ltd.

Recent progress in making protein microarray through BioLP

NASA Astrophysics Data System (ADS)

Yang, Rusong; Wei, Lian; Feng, Ying; Li, Xiujian; Zhou, Quan

2017-02-01

Biological laser printing (BioLP) is a promising biomaterial printing technique. It has the advantage of high resolution, high bioactivity, high printing frequency and small transported liquid amount. In this paper, a set of BioLP device is design and made, and protein microarrays are printed by this device. It's found that both laser intensity and fluid layer thickness have an influence on the microarrays acquired. Besides, two kinds of the fluid layer coating methods are compared, and the results show that blade coating method is better than well-coating method in BioLP. A microarray of 0.76pL protein microarray and a "NUDT" patterned microarray are printed to testify the printing ability of BioLP.

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.

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

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...

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

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.

Ground testing of Arabidopsis preservation protocol for the microarray analysis to be used in the ISS EMCS Multigen-2 experiment

NASA Astrophysics Data System (ADS)

Kittang, Ann-Iren; Kvaløy, Brita; Winge, Per; Iversen, Tor-Henning

2010-11-01

Gene expression analysis using microarrays has proved to be an important method in life science. The opportunity to grow higher plants on the International Space Station (ISS) opens up the possibility for gene expression profiling of plants grown in microgravity. The work presented focuses on how to meet the scientific requirements of plant growth and the sample preservation, given the technical and operational constraints associated with space research. The growth chamber (Multigen-2 Science Testing Unit) and a protocol suggested to be used in the European Modular Cultivation System (EMCS) Multigen-2 experiment on the ISS to grow and later preserve Arabidopsis seedlings, were tested on ground. The results showed that most of the plants developed normally. In order to avoid high population stress the number of seedlings per growth area should be reduced. The RNAlater preservation method to be used in the space experiment was compared with a quick freeze in Liquid Nitrogen (LN2). The RNA from samples preserved in RNAlater at room temperature for 24 h was slightly more degraded than the RNA from the LN2 preserved samples (RNA integrity number, RIN: 7.7 and 8.6, respectively). However, the RNA quality and quantity was satisfactory for microarray analysis. Of the genes analysed, 74 genes (0.28%) were significantly differentially expressed, most of them showing moderate to low regulation. Among the genes induced in the RNAlater preserved samples, three salt inducible transcription factors (ZAT10, SZF1 and SZF2) were identified, suggesting that the high salt concentration in RNAlater causes salt stress before the transcription stopped. In conclusion, the Multigen-2 preservation protocol tested here will allow for the genes regulated by microgravity in the space experiment to be revealed. The results do indicate that not all the biological processes are stopped instantly by the RNAlater. The limited diffusion indirectly caused by the microgravity may potentially result in a different degree of salt stress in the test compared to the 1 × g control during the space experiment. This has to be accounted for during the evaluation of the results. Since slightly degraded RNA was observed, further optimalisation of the preservation protocol will be performed.

On-Chip Synthesis of Protein Microarrays from DNA Microarrays Via Coupled In Vitro Transcription and Translation for Surface Plasmon Resonance Imaging Biosensor Applications

PubMed Central

Seefeld, Ting H.; Halpern, Aaron R.; Corn, Robert M.

2012-01-01

Protein microarrays are fabricated from double-stranded DNA (dsDNA) microarrays by a one-step, multiplexed enzymatic synthesis in an on-chip microfluidic format and then employed for antibody biosensing measurements with surface plasmon resonance imaging (SPRI). A microarray of dsDNA elements (denoted as generator elements) that encode either a His-tagged green fluorescent protein (GFP) or a His-tagged luciferase protein is utilized to create multiple copies of messenger RNA (mRNA) in a surface RNA polymerase reaction; the mRNA transcripts are then translated into proteins by cell-free protein synthesis in a microfluidic format. The His-tagged proteins diffuse to adjacent Cu(II)-NTA microarray elements (denoted as detector elements) and are specifically adsorbed. The net result is the on-chip, cell-free synthesis of a protein microarray that can be used immediately for SPRI protein biosensing. The dual element format greatly reduces any interference from the nonspecific adsorption of enzyme or proteins. SPRI measurements for the detection of the antibodies anti-GFP and anti-luciferase were used to verify the formation of the protein microarray. This convenient on-chip protein microarray fabrication method can be implemented for multiplexed SPRI biosensing measurements in both clinical and research applications. PMID:22793370

Development and application of a fluorescence protein microarray for detecting serum alpha-fetoprotein in patients with hepatocellular carcinoma.

PubMed

Zhang, Aiying; Yin, Chengzeng; Wang, Zhenshun; Zhang, Yonghong; Zhao, Yuanshun; Li, Ang; Sun, Huanqin; Lin, Dongdong; Li, Ning

2016-12-01

Objective To develop a simple, effective, time-saving and low-cost fluorescence protein microarray method for detecting serum alpha-fetoprotein (AFP) in patients with hepatocellular carcinoma (HCC). Method Non-contact piezoelectric print techniques were applied to fluorescence protein microarray to reduce the cost of prey antibody. Serum samples from patients with HCC and healthy control subjects were collected and evaluated for the presence of AFP using a novel fluorescence protein microarray. To validate the fluorescence protein microarray, serum samples were tested for AFP using an enzyme-linked immunosorbent assay (ELISA). Results A total of 110 serum samples from patients with HCC ( n = 65) and healthy control subjects ( n = 45) were analysed. When the AFP cut-off value was set at 20 ng/ml, the fluorescence protein microarray had a sensitivity of 91.67% and a specificity of 93.24% for detecting serum AFP. Serum AFP quantified via fluorescence protein microarray had a similar diagnostic performance compared with ELISA in distinguishing patients with HCC from healthy control subjects (area under receiver operating characteristic curve: 0.906 for fluorescence protein microarray; 0.880 for ELISA). Conclusion A fluorescence protein microarray method was developed for detecting serum AFP in patients with HCC.

Development and application of a fluorescence protein microarray for detecting serum alpha-fetoprotein in patients with hepatocellular carcinoma

PubMed Central

Zhang, Aiying; Yin, Chengzeng; Wang, Zhenshun; Zhang, Yonghong; Zhao, Yuanshun; Li, Ang; Sun, Huanqin; Lin, Dongdong

2016-01-01

Objective To develop a simple, effective, time-saving and low-cost fluorescence protein microarray method for detecting serum alpha-fetoprotein (AFP) in patients with hepatocellular carcinoma (HCC). Method Non-contact piezoelectric print techniques were applied to fluorescence protein microarray to reduce the cost of prey antibody. Serum samples from patients with HCC and healthy control subjects were collected and evaluated for the presence of AFP using a novel fluorescence protein microarray. To validate the fluorescence protein microarray, serum samples were tested for AFP using an enzyme-linked immunosorbent assay (ELISA). Results A total of 110 serum samples from patients with HCC (n = 65) and healthy control subjects (n = 45) were analysed. When the AFP cut-off value was set at 20 ng/ml, the fluorescence protein microarray had a sensitivity of 91.67% and a specificity of 93.24% for detecting serum AFP. Serum AFP quantified via fluorescence protein microarray had a similar diagnostic performance compared with ELISA in distinguishing patients with HCC from healthy control subjects (area under receiver operating characteristic curve: 0.906 for fluorescence protein microarray; 0.880 for ELISA). Conclusion A fluorescence protein microarray method was developed for detecting serum AFP in patients with HCC. PMID:27885040

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

Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.

PubMed

Li, Xumeng; Feltus, Frank A; Sun, Xiaoqian; Wang, James Z; Luo, Feng

2011-10-01

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Childhood chronic inflammatory demyelinating polyneuropathy: an overview of 10 cases in the modern era.

PubMed

Ware, Tyson L; Kornberg, Andrew J; Rodriguez-Casero, M Victoria; Ryan, Monique M

2014-01-01

Chronic inflammatory demyelinating polyneuropathy is a rare condition in children. In this article, we report our experience in the management of 10 cases of childhood chronic inflammatory demyelinating polyneuropathy in a single center, in the era of contrast-enhanced magnetic resonance imaging (MRI), genetic microarray, and chronic inflammatory demyelinating polyneuropathy disease activity status. Robust neurophysiologic abnormalities were present in all cases and both MRI and lumbar puncture were useful adjuncts in diagnosis. Genetic microarray is a simple technique useful in excluding the most common hereditary demyelinating neuropathy. Intravenous immunoglobulin was an effective first-line therapy in most cases, with refractory cases responding to corticosteroids and rituximab. We found the chronic inflammatory demyelinating polyneuropathy disease activity status useful for assessing outcome at final follow-up, whereas the modified Rankin score was better for assessing peak motor disability.

Microarray-based gene expression profiling to elucidate effectiveness of fermented Codonopsis lanceolata in mice.

PubMed

Choi, Woon Yong; Kim, Ji Seon; Park, Sung Jin; Ma, Choong Je; Lee, Hyeon Yong

2014-04-08

In this study, the effect of Codonopsis lanceolata fermented by lactic acid on controlling gene expression levels related to obesity was observed in an oligonucleotide chip microarray. Among 8170 genes, 393 genes were up regulated and 760 genes were down regulated in feeding the fermented C. lanceolata (FCL). Another 374 genes were up regulated and 527 genes down regulated without feeding the sample. The genes were not affected by the FCL sample. It was interesting that among those genes, Chytochrome P450, Dmbt1, LOC76487, and thyroid hormones, etc., were mostly up or down regulated. These genes are more related to lipid synthesis. We could conclude that the FCL possibly controlled the gene expression levels related to lipid synthesis, which resulted in reducing obesity. However, more detailed protein expression experiments should be carried out.

Gene expression profiling of mucolipidosis type IV fibroblasts reveals deregulation of genes with relevant functions in lysosome physiology.

PubMed

Bozzato, Andrea; Barlati, Sergio; Borsani, Giuseppe

2008-04-01

Mucolipidosis type IV (MLIV, MIM 252650) is an autosomal recessive lysosomal storage disorder that causes mental and motor retardation as well as visual impairment. The lysosomal storage defect in MLIV is consistent with abnormalities of membrane traffic and organelle dynamics in the late endocytic pathway. MLIV is caused by mutations in the MCOLN1 gene, which codes for mucolipin-1 (MLN1), a member of the large family of transient receptor potential (TRP) cation channels. Although a number of studies have been performed on mucolipin-1, the pathological mechanisms underlying MLIV are not fully understood. To identify genes that characterize pathogenic changes in mucolipidosis type IV, we compared the expression profiles of three MLIV and three normal skin fibroblasts cell lines using oligonucleotide microarrays. Genes that were differentially expressed in patients' cells were identified. 231 genes were up-regulated, and 116 down-regulated. Real-Time RT-PCR performed on selected genes in six independent MLIV fibroblasts cell lines was generally consistent with the microarray findings. This study allowed to evidence the modulation at the transcriptional level of a discrete number of genes relevant in biological processes which are altered in the disease such as endosome/lysosome trafficking, lysosome biogenesis, organelle acidification and lipid metabolism.

MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets.

PubMed

Kim, Taehyung; Tyndel, Marc S; Huang, Haiming; Sidhu, Sachdev S; Bader, Gary D; Gfeller, David; Kim, Philip M

2012-03-01

Peptide recognition domains and transcription factors play crucial roles in cellular signaling. They bind linear stretches of amino acids or nucleotides, respectively, with high specificity. Experimental techniques that assess the binding specificity of these domains, such as microarrays or phage display, can retrieve thousands of distinct ligands, providing detailed insight into binding specificity. In particular, the advent of next-generation sequencing has recently increased the throughput of such methods by several orders of magnitude. These advances have helped reveal the presence of distinct binding specificity classes that co-exist within a set of ligands interacting with the same target. Here, we introduce a software system called MUSI that can rapidly analyze very large data sets of binding sequences to determine the relevant binding specificity patterns. Our pipeline provides two major advances. First, it can detect previously unrecognized multiple specificity patterns in any data set. Second, it offers integrated processing of very large data sets from next-generation sequencing machines. The results are visualized as multiple sequence logos describing the different binding preferences of the protein under investigation. We demonstrate the performance of MUSI by analyzing recent phage display data for human SH3 domains as well as microarray data for mouse transcription factors.

A prototype for unsupervised analysis of tissue microarrays for cancer research and diagnostics.

PubMed

Chen, Wenjin; Reiss, Michael; Foran, David J

2004-06-01

The tissue microarray (TMA) technique enables researchers to extract small cylinders of tissue from histological sections and arrange them in a matrix configuration on a recipient paraffin block such that hundreds can be analyzed simultaneously. TMA offers several advantages over traditional specimen preparation by maximizing limited tissue resources and providing a highly efficient means for visualizing molecular targets. By enabling researchers to reliably determine the protein expression profile for specific types of cancer, it may be possible to elucidate the mechanism by which healthy tissues are transformed into malignancies. Currently, the primary methods used to evaluate arrays involve the interactive review of TMA samples while they are viewed under a microscope, subjectively evaluated, and scored by a technician. This process is extremely slow, tedious, and prone to error. In order to facilitate large-scale, multi-institutional studies, a more automated and reliable means for analyzing TMAs is needed. We report here a web-based prototype which features automated imaging, registration, and distributed archiving of TMAs in multiuser network environments. The system utilizes a principal color decomposition approach to identify and characterize the predominant staining signatures of specimens in color space. This strategy was shown to be reliable for detecting and quantifying the immunohistochemical expression levels for TMAs.

GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle.

PubMed

Lardenois, Aurélie; Gattiker, Alexandre; Collin, Olivier; Chalmel, Frédéric; Primig, Michael

2010-01-01

GermOnline 4.0 is a cross-species database portal focusing on high-throughput expression data relevant for germline development, the meiotic cell cycle and mitosis in healthy versus malignant cells. It is thus a source of information for life scientists as well as clinicians who are interested in gene expression and regulatory networks. The GermOnline gateway provides unlimited access to information produced with high-density oligonucleotide microarrays (3'-UTR GeneChips), genome-wide protein-DNA binding assays and protein-protein interaction studies in the context of Ensembl genome annotation. Samples used to produce high-throughput expression data and to carry out genome-wide in vivo DNA binding assays are annotated via the MIAME-compliant Multiomics Information Management and Annotation System (MIMAS 3.0). Furthermore, the Saccharomyces Genomics Viewer (SGV) was developed and integrated into the gateway. SGV is a visualization tool that outputs genome annotation and DNA-strand specific expression data produced with high-density oligonucleotide tiling microarrays (Sc_tlg GeneChips) which cover the complete budding yeast genome on both DNA strands. It facilitates the interpretation of expression levels and transcript structures determined for various cell types cultured under different growth and differentiation conditions. Database URL: www.germonline.org/

GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle

PubMed Central

Lardenois, Aurélie; Gattiker, Alexandre; Collin, Olivier; Chalmel, Frédéric; Primig, Michael

2010-01-01

GermOnline 4.0 is a cross-species database portal focusing on high-throughput expression data relevant for germline development, the meiotic cell cycle and mitosis in healthy versus malignant cells. It is thus a source of information for life scientists as well as clinicians who are interested in gene expression and regulatory networks. The GermOnline gateway provides unlimited access to information produced with high-density oligonucleotide microarrays (3′-UTR GeneChips), genome-wide protein–DNA binding assays and protein–protein interaction studies in the context of Ensembl genome annotation. Samples used to produce high-throughput expression data and to carry out genome-wide in vivo DNA binding assays are annotated via the MIAME-compliant Multiomics Information Management and Annotation System (MIMAS 3.0). Furthermore, the Saccharomyces Genomics Viewer (SGV) was developed and integrated into the gateway. SGV is a visualization tool that outputs genome annotation and DNA-strand specific expression data produced with high-density oligonucleotide tiling microarrays (Sc_tlg GeneChips) which cover the complete budding yeast genome on both DNA strands. It facilitates the interpretation of expression levels and transcript structures determined for various cell types cultured under different growth and differentiation conditions. Database URL: www.germonline.org/ PMID:21149299

Using phylogenetic probes for quantification of stable isotope labeling and microbial community analysis

DOEpatents

Brodie, Eoin L; DeSantis, Todd Z; Karaoz, Ulas; Andersen, Gary L

2014-12-09

Herein is described methods for a high-sensitivity means to measure the incorporation of stable isotope labeled substrates into RNA following stable isotope probing experiments (SIP). RNA is hybridized to a set of probes such as phylogenetic microarrays and isotope incorporation is quantified such as by secondary ion mass spectrometer imaging (NanoSIMS).

MicroArray Facility: a laboratory information management system with extended support for Nylon based technologies.

PubMed

Honoré, Paul; Granjeaud, Samuel; Tagett, Rebecca; Deraco, Stéphane; Beaudoing, Emmanuel; Rougemont, Jacques; Debono, Stéphane; Hingamp, Pascal

2006-09-20

High throughput gene expression profiling (GEP) is becoming a routine technique in life science laboratories. With experimental designs that repeatedly span thousands of genes and hundreds of samples, relying on a dedicated database infrastructure is no longer an option.GEP technology is a fast moving target, with new approaches constantly broadening the field diversity. This technology heterogeneity, compounded by the informatics complexity of GEP databases, means that software developments have so far focused on mainstream techniques, leaving less typical yet established techniques such as Nylon microarrays at best partially supported. MAF (MicroArray Facility) is the laboratory database system we have developed for managing the design, production and hybridization of spotted microarrays. Although it can support the widely used glass microarrays and oligo-chips, MAF was designed with the specific idiosyncrasies of Nylon based microarrays in mind. Notably single channel radioactive probes, microarray stripping and reuse, vector control hybridizations and spike-in controls are all natively supported by the software suite. MicroArray Facility is MIAME supportive and dynamically provides feedback on missing annotations to help users estimate effective MIAME compliance. Genomic data such as clone identifiers and gene symbols are also directly annotated by MAF software using standard public resources. The MAGE-ML data format is implemented for full data export. Journalized database operations (audit tracking), data anonymization, material traceability and user/project level confidentiality policies are also managed by MAF. MicroArray Facility is a complete data management system for microarray producers and end-users. Particular care has been devoted to adequately model Nylon based microarrays. The MAF system, developed and implemented in both private and academic environments, has proved a robust solution for shared facilities and industry service providers alike.

MicroArray Facility: a laboratory information management system with extended support for Nylon based technologies

PubMed Central

Honoré, Paul; Granjeaud, Samuel; Tagett, Rebecca; Deraco, Stéphane; Beaudoing, Emmanuel; Rougemont, Jacques; Debono, Stéphane; Hingamp, Pascal

2006-01-01

Background High throughput gene expression profiling (GEP) is becoming a routine technique in life science laboratories. With experimental designs that repeatedly span thousands of genes and hundreds of samples, relying on a dedicated database infrastructure is no longer an option. GEP technology is a fast moving target, with new approaches constantly broadening the field diversity. This technology heterogeneity, compounded by the informatics complexity of GEP databases, means that software developments have so far focused on mainstream techniques, leaving less typical yet established techniques such as Nylon microarrays at best partially supported. Results MAF (MicroArray Facility) is the laboratory database system we have developed for managing the design, production and hybridization of spotted microarrays. Although it can support the widely used glass microarrays and oligo-chips, MAF was designed with the specific idiosyncrasies of Nylon based microarrays in mind. Notably single channel radioactive probes, microarray stripping and reuse, vector control hybridizations and spike-in controls are all natively supported by the software suite. MicroArray Facility is MIAME supportive and dynamically provides feedback on missing annotations to help users estimate effective MIAME compliance. Genomic data such as clone identifiers and gene symbols are also directly annotated by MAF software using standard public resources. The MAGE-ML data format is implemented for full data export. Journalized database operations (audit tracking), data anonymization, material traceability and user/project level confidentiality policies are also managed by MAF. Conclusion MicroArray Facility is a complete data management system for microarray producers and end-users. Particular care has been devoted to adequately model Nylon based microarrays. The MAF system, developed and implemented in both private and academic environments, has proved a robust solution for shared facilities and industry service providers alike. PMID:16987406

Visual experience sculpts whole-cortex spontaneous infraslow activity patterns through an Arc-dependent mechanism

PubMed Central

Kraft, Andrew W.; Mitra, Anish; Bauer, Adam Q.; Raichle, Marcus E.; Culver, Joseph P.; Lee, Jin-Moo

2017-01-01

Decades of work in experimental animals has established the importance of visual experience during critical periods for the development of normal sensory-evoked responses in the visual cortex. However, much less is known concerning the impact of early visual experience on the systems-level organization of spontaneous activity. Human resting-state fMRI has revealed that infraslow fluctuations in spontaneous activity are organized into stereotyped spatiotemporal patterns across the entire brain. Furthermore, the organization of spontaneous infraslow activity (ISA) is plastic in that it can be modulated by learning and experience, suggesting heightened sensitivity to change during critical periods. Here we used wide-field optical intrinsic signal imaging in mice to examine whole-cortex spontaneous ISA patterns. Using monocular or binocular visual deprivation, we examined the effects of critical period visual experience on the development of ISA correlation and latency patterns within and across cortical resting-state networks. Visual modification with monocular lid suturing reduced correlation between left and right cortices (homotopic correlation) within the visual network, but had little effect on internetwork correlation. In contrast, visual deprivation with binocular lid suturing resulted in increased visual homotopic correlation and increased anti-correlation between the visual network and several extravisual networks, suggesting cross-modal plasticity. These network-level changes were markedly attenuated in mice with genetic deletion of Arc, a gene known to be critical for activity-dependent synaptic plasticity. Taken together, our results suggest that critical period visual experience induces global changes in spontaneous ISA relationships, both within the visual network and across networks, through an Arc-dependent mechanism. PMID:29087327

NCBI GEO: archive for functional genomics data sets—update

PubMed Central

Barrett, Tanya; Wilhite, Stephen E.; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F.; Tomashevsky, Maxim; Marshall, Kimberly A.; Phillippy, Katherine H.; Sherman, Patti M.; Holko, Michelle; Yefanov, Andrey; Lee, Hyeseung; Zhang, Naigong; Robertson, Cynthia L.; Serova, Nadezhda; Davis, Sean; Soboleva, Alexandra

2013-01-01

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data. PMID:23193258

Application of phenotypic microarrays to environmental microbiology

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

Borglin, sharon; Joyner, Dominique; DeAngelis, Kristen

2012-01-01

Environmental organisms are extremely diverse and only a small fraction has been successfully cultured in the laboratory. Culture in micro wells provides a method for rapid screening of a wide variety of growth conditions and commercially available plates contain a large number of substrates, nutrient sources, and inhibitors, which can provide an assessment of the phenotype of an organism. This review describes applications of phenotype arrays to anaerobic and thermophilic microorganisms, use of the plates in stress response studies, in development of culture media for newly discovered strains, and for assessment of phenotype of environmental communities. Also discussed are considerationsmore » and challenges in data interpretation and visualization, including data normalization, statistics, and curve fitting.« less

Insensitivity of visual short-term memory to irrelevant visual information.

PubMed

Andrade, Jackie; Kemps, Eva; Werniers, Yves; May, Jon; Szmalec, Arnaud

2002-07-01

Several authors have hypothesized that visuo-spatial working memory is functionally analogous to verbal working memory. Irrelevant background speech impairs verbal short-term memory. We investigated whether irrelevant visual information has an analogous effect on visual short-term memory, using a dynamic visual noise (DVN) technique known to disrupt visual imagery (Quinn & McConnell, 1996b). Experiment I replicated the effect of DVN on pegword imagery. Experiments 2 and 3 showed no effect of DVN on recall of static matrix patterns, despite a significant effect of a concurrent spatial tapping task. Experiment 4 showed no effect of DVN on encoding or maintenance of arrays of matrix patterns, despite testing memory by a recognition procedure to encourage visual rather than spatial processing. Serial position curves showed a one-item recency effect typical of visual short-term memory. Experiment 5 showed no effect of DVN on short-term recognition of Chinese characters, despite effects of visual similarity and a concurrent colour memory task that confirmed visual processing of the characters. We conclude that irrelevant visual noise does not impair visual short-term memory. Visual working memory may not be functionally analogous to verbal working memory, and different cognitive processes may underlie visual short-term memory and visual imagery.

The contributions of visual and central attention to visual working memory.

PubMed

Souza, Alessandra S; Oberauer, Klaus

2017-10-01

We investigated the role of two kinds of attention-visual and central attention-for the maintenance of visual representations in working memory (WM). In Experiment 1 we directed attention to individual items in WM by presenting cues during the retention interval of a continuous delayed-estimation task, and instructing participants to think of the cued items. Attending to items improved recall commensurate with the frequency with which items were attended (0, 1, or 2 times). Experiments 1 and 3 further tested which kind of attention-visual or central-was involved in WM maintenance. We assessed the dual-task costs of two types of distractor tasks, one tapping sustained visual attention and one tapping central attention. Only the central attention task yielded substantial dual-task costs, implying that central attention substantially contributes to maintenance of visual information in WM. Experiment 2 confirmed that the visual-attention distractor task was demanding enough to disrupt performance in a task relying on visual attention. We combined the visual-attention and the central-attention distractor tasks with a multiple object tracking (MOT) task. Distracting visual attention, but not central attention, impaired MOT performance. Jointly, the three experiments provide a double dissociation between visual and central attention, and between visual WM and visual object tracking: Whereas tracking multiple targets across the visual filed depends on visual attention, visual WM depends mostly on central attention.

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

Optimised padlock probe ligation and microarray detection of multiple (non-authorised) GMOs in a single reaction

PubMed Central

Prins, Theo W; van Dijk, Jeroen P; Beenen, Henriek G; Van Hoef, AM Angeline; Voorhuijzen, Marleen M; Schoen, Cor D; Aarts, Henk JM; Kok, Esther J

2008-01-01

Background To maintain EU GMO regulations, producers of new GM crop varieties need to supply an event-specific method for the new variety. As a result methods are nowadays available for EU-authorised genetically modified organisms (GMOs), but only to a limited extent for EU-non-authorised GMOs (NAGs). In the last decade the diversity of genetically modified (GM) ingredients in food and feed has increased significantly. As a result of this increase GMO laboratories currently need to apply many different methods to establish to potential presence of NAGs in raw materials and complex derived products. Results In this paper we present an innovative method for detecting (approved) GMOs as well as the potential presence of NAGs in complex DNA samples containing different crop species. An optimised protocol has been developed for padlock probe ligation in combination with microarray detection (PPLMD) that can easily be scaled up. Linear padlock probes targeted against GMO-events, -elements and -species have been developed that can hybridise to their genomic target DNA and are visualised using microarray hybridisation. In a tenplex PPLMD experiment, different genomic targets in Roundup-Ready soya, MON1445 cotton and Bt176 maize were detected down to at least 1%. In single experiments, the targets were detected down to 0.1%, i.e. comparable to standard qPCR. Conclusion Compared to currently available methods this is a significant step forward towards multiplex detection in complex raw materials and derived products. It is shown that the PPLMD approach is suitable for large-scale detection of GMOs in real-life samples and provides the possibility to detect and/or identify NAGs that would otherwise remain undetected. PMID:19055784

Optimised padlock probe ligation and microarray detection of multiple (non-authorised) GMOs in a single reaction.

PubMed

Prins, Theo W; van Dijk, Jeroen P; Beenen, Henriek G; Van Hoef, Am Angeline; Voorhuijzen, Marleen M; Schoen, Cor D; Aarts, Henk J M; Kok, Esther J

2008-12-04

To maintain EU GMO regulations, producers of new GM crop varieties need to supply an event-specific method for the new variety. As a result methods are nowadays available for EU-authorised genetically modified organisms (GMOs), but only to a limited extent for EU-non-authorised GMOs (NAGs). In the last decade the diversity of genetically modified (GM) ingredients in food and feed has increased significantly. As a result of this increase GMO laboratories currently need to apply many different methods to establish to potential presence of NAGs in raw materials and complex derived products. In this paper we present an innovative method for detecting (approved) GMOs as well as the potential presence of NAGs in complex DNA samples containing different crop species. An optimised protocol has been developed for padlock probe ligation in combination with microarray detection (PPLMD) that can easily be scaled up. Linear padlock probes targeted against GMO-events, -elements and -species have been developed that can hybridise to their genomic target DNA and are visualised using microarray hybridisation.In a tenplex PPLMD experiment, different genomic targets in Roundup-Ready soya, MON1445 cotton and Bt176 maize were detected down to at least 1%. In single experiments, the targets were detected down to 0.1%, i.e. comparable to standard qPCR. Compared to currently available methods this is a significant step forward towards multiplex detection in complex raw materials and derived products. It is shown that the PPLMD approach is suitable for large-scale detection of GMOs in real-life samples and provides the possibility to detect and/or identify NAGs that would otherwise remain undetected.

Characterization of a novel Lactobacillus species closely related to Lactobacillus johnsonii using a combination of molecular and comparative genomics methods.

PubMed

Sarmiento-Rubiano, Luz-Adriana; Berger, Bernard; Moine, Déborah; Zúñiga, Manuel; Pérez-Martínez, Gaspar; Yebra, María J

2010-09-17

Comparative genomic hybridization (CGH) constitutes a powerful tool for identification and characterization of bacterial strains. In this study we have applied this technique for the characterization of a number of Lactobacillus strains isolated from the intestinal content of rats fed with a diet supplemented with sorbitol. Phylogenetic analysis based on 16S rRNA gene, recA, pheS, pyrG and tuf sequences identified five bacterial strains isolated from the intestinal content of rats as belonging to the recently described Lactobacillus taiwanensis species. DNA-DNA hybridization experiments confirmed that these five strains are distinct but closely related to Lactobacillus johnsonii and Lactobacillus gasseri. A whole genome DNA microarray designed for the probiotic L. johnsonii strain NCC533 was used for CGH analysis of L. johnsonii ATCC 33200T, L. johnsonii BL261, L. gasseri ATCC 33323T and L. taiwanensis BL263. In these experiments, the fluorescence ratio distributions obtained with L. taiwanensis and L. gasseri showed characteristic inter-species profiles. The percentage of conserved L. johnsonii NCC533 genes was about 83% in the L. johnsonii strains comparisons and decreased to 51% and 47% for L. taiwanensis and L. gasseri, respectively. These results confirmed the separate status of L. taiwanensis from L. johnsonii at the level of species, and also that L. taiwanensis is closer to L. johnsonii than L. gasseri is to L. johnsonii. Conventional taxonomic analyses and microarray-based CGH analysis have been used for the identification and characterization of the newly species L. taiwanensis. The microarray-based CGH technology has been shown as a remarkable tool for the identification and fine discrimination between phylogenetically close species, and additionally provided insight into the adaptation of the strain L. taiwanensis BL263 to its ecological niche.

The experience of reading.

PubMed

Moore, Alan Tonnies; Schwitzgebel, Eric

2018-05-03

What do people consciously experience when they read? There has been almost no rigorous research on this question, and opinions diverge radically among both philosophers and psychologists. We describe three studies of the phenomenology of reading and its relationship to memory of textual detail and general cognitive abilities. We find three main results. First, there is substantial variability in reports about reading experience, both within and between participants. Second, reported reading experience varies with passage type: passages with dialogue prompted increased reports of inner speech, while passages with vivid visual detail prompted increased reports of visual imagery. Third, reports of visual imagery experiences, inner speech experiences, and experiences of conscious visual perception of the words on the page were at best weakly related to general cognitive abilities and memory of visual and auditory details. Copyright © 2018 Elsevier Inc. All rights reserved.

Repeated Measurements on Distinct Scales With Censoring—A Bayesian Approach Applied to Microarray Analysis of Maize

PubMed Central

Love, Tanzy; Carriquiry, Alicia

2009-01-01

We analyze data collected in a somatic embryogenesis experiment carried out on Zea mays at Iowa State University. The main objective of the study was to identify the set of genes in maize that actively participate in embryo development. Embryo tissue was sampled and analyzed at various time periods and under different mediums and light conditions. As is the case in many microarray experiments, the operator scanned each slide multiple times to find the slide-specific ‘optimal’ laser and sensor settings. The multiple readings of each slide are repeated measurements on different scales with differing censoring; they cannot be considered to be replicate measurements in the traditional sense. Yet it has been shown that the choice of reading can have an impact on genetic inference. We propose a hierarchical modeling approach to estimating gene expression that combines all available readings on each spot and accounts for censoring in the observed values. We assess the statistical properties of the proposed expression estimates using a simulation experiment. As expected, combining all available scans using an approach with good statistical properties results in expression estimates with noticeably lower bias and root mean squared error relative to other approaches that have been proposed in the literature. Inferences drawn from the somatic embryogenesis experiment, which motivated this work changed drastically when data were analyzed using the standard approaches or using the methodology we propose. PMID:19960120

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.

Microarray expression profiling identifies genes with altered expression in HDL-deficient mice

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

Callow, Matthew J.; Dudoit, Sandrine; Gong, Elaine L.

2000-05-05

Based on the assumption that severe alterations in the expression of genes known to be involved in HDL metabolism may affect the expression of other genes we screened an array of over 5000 mouse expressed sequence tags (ESTs) for altered gene expression in the livers of two lines of mice with dramatic decreases in HDL plasma concentrations. Labeled cDNA from livers of apolipoprotein AI (apo AI) knockout mice, Scavenger Receptor BI (SR-BI) transgenic mice and control mice were co-hybridized to microarrays. Two-sample t-statistics were used to identify genes with altered expression levels in the knockout or transgenic mice compared withmore » the control mice. In the SR-BI group we found 9 array elements representing at least 5 genes to be significantly altered on the basis of an adjusted p value of less than 0.05. In the apo AI knockout group 8 array elements representing 4 genes were altered compared with the control group (p < 0.05). Several of the genes identified in the SR-BI transgenic suggest altered sterol metabolism and oxidative processes. These studies illustrate the use of multiple-testing methods for the identification of genes with altered expression in replicated microarray experiments of apo AI knockout and SR-BI transgenic mice.« less

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

A Novel Pan-Flavivirus Detection and Identification Assay Based on RT-qPCR and Microarray

PubMed Central

Sachse, Konrad; Ziegler, Ute; Keller, Markus

2017-01-01

The genus Flavivirus includes arthropod-borne viruses responsible for a large number of infections in humans and economically important animals. While RT-PCR protocols for specific detection of most Flavivirus species are available, there has been also a demand for a broad-range Flavivirus assay covering all members of the genus. It is particularly challenging to balance specificity at genus level with equal sensitivity towards each target species. In the present study, a novel assay combining a SYBR Green-based RT-qPCR with a low-density DNA microarray has been developed. Validation experiments confirmed that the RT-qPCR exhibited roughly equal sensitivity of detection and quantification for all flaviviruses tested. These PCR products are subjected to hybridization on a microarray carrying 84 different oligonucleotide probes that represent all known Flavivirus species. This assay has been used as a screening and confirmation tool for Flavivirus presence in laboratory and field samples, and it performed successfully in international External Quality Assessment of NAT studies. Twenty-six Flavivirus strains were tested with the assay, showing equivalent or superior characteristics compared with the original or even with species-specific RT-PCRs. As an example, test results on West Nile virus detection in a panel of 340 mosquito pool samples from Greece are presented. PMID:28626758

"If it helps..." the use of microarray technology in prenatal testing: patient and partners reflections.

PubMed

Hillman, Sarah C; Skelton, John; Quinlan-Jones, Elizabeth; Wilson, Amie; Kilby, Mark D

2013-07-01

The objective was to gain insight into the experiences of women and their partners diagnosed with a fetal abnormality on prenatal ultrasound examination and receiving genetic testing including microarray. Twenty-five semi-structured interviews were performed with women +/- their partners after receiving the results of prenatal genetic testing. Framework analysis was performed to elicit themes and subthemes. Five main themes were recognized; diagnosis, genetic testing, family and support, reflections of the treatment received and emotions. Our results showed that women recall being told about QFPCR for trisomy 13, 18, and 21 but often no further testing. Women expected the conventional karyotype and microarray result would be normal following a normal QFPCR result. There were frequent misconceptions by couples regarding aspects of counseling/testing. Communication of variants of unknown (clinical) significance (VOUS) presents a particularly difficult challenge. Good clear communication by health care professionals is paramount. When counseling women and their partners for fetal chromosomal testing it should be reinforced that although the most common, trisomy 13, 18, and 21 only account for some of the chromosomal changes resulting in abnormal scan findings. Couples should have literature to take home summarizing scan anomalies and reinforcing information about genetic testing. Copyright © 2013 Wiley Periodicals, Inc.

Prior robust empirical Bayes inference for large-scale data by conditioning on rank with application to microarray data

PubMed Central

Liao, J. G.; Mcmurry, Timothy; Berg, Arthur

2014-01-01

Empirical Bayes methods have been extensively used for microarray data analysis by modeling the large number of unknown parameters as random effects. Empirical Bayes allows borrowing information across genes and can automatically adjust for multiple testing and selection bias. However, the standard empirical Bayes model can perform poorly if the assumed working prior deviates from the true prior. This paper proposes a new rank-conditioned inference in which the shrinkage and confidence intervals are based on the distribution of the error conditioned on rank of the data. Our approach is in contrast to a Bayesian posterior, which conditions on the data themselves. The new method is almost as efficient as standard Bayesian methods when the working prior is close to the true prior, and it is much more robust when the working prior is not close. In addition, it allows a more accurate (but also more complex) non-parametric estimate of the prior to be easily incorporated, resulting in improved inference. The new method’s prior robustness is demonstrated via simulation experiments. Application to a breast cancer gene expression microarray dataset is presented. Our R package rank.Shrinkage provides a ready-to-use implementation of the proposed methodology. PMID:23934072

Shrinkage covariance matrix approach based on robust trimmed mean in gene sets detection

NASA Astrophysics Data System (ADS)

Karjanto, Suryaefiza; Ramli, Norazan Mohamed; Ghani, Nor Azura Md; Aripin, Rasimah; Yusop, Noorezatty Mohd

2015-02-01

Microarray involves of placing an orderly arrangement of thousands of gene sequences in a grid on a suitable surface. The technology has made a novelty discovery since its development and obtained an increasing attention among researchers. The widespread of microarray technology is largely due to its ability to perform simultaneous analysis of thousands of genes in a massively parallel manner in one experiment. Hence, it provides valuable knowledge on gene interaction and function. The microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints. Therefore, the sample covariance matrix in Hotelling's T2 statistic is not positive definite and become singular, thus it cannot be inverted. In this research, the Hotelling's T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets. The use of shrinkage covariance matrix overcomes the singularity problem by converting an unbiased to an improved biased estimator of covariance matrix. Robust trimmed mean is integrated into the shrinkage matrix to reduce the influence of outliers and consequently increases its efficiency. The performance of the proposed method is measured using several simulation designs. The results are expected to outperform existing techniques in many tested conditions.

The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

PubMed Central

Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

2009-01-01

We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

Plastic Polymers for Efficient DNA Microarray Hybridization: Application to Microbiological Diagnostics▿

PubMed Central

Zhao, Zhengshan; Peytavi, Régis; Diaz-Quijada, Gerardo A.; Picard, Francois J.; Huletsky, Ann; Leblanc, Éric; Frenette, Johanne; Boivin, Guy; Veres, Teodor; Dumoulin, Michel M.; Bergeron, Michel G.

2008-01-01

Fabrication of microarray devices using traditional glass slides is not easily adaptable to integration into microfluidic systems. There is thus a need for the development of polymeric materials showing a high hybridization signal-to-background ratio, enabling sensitive detection of microbial pathogens. We have developed such plastic supports suitable for highly sensitive DNA microarray hybridizations. The proof of concept of this microarray technology was done through the detection of four human respiratory viruses that were amplified and labeled with a fluorescent dye via a sensitive reverse transcriptase PCR (RT-PCR) assay. The performance of the microarray hybridization with plastic supports made of PMMA [poly(methylmethacrylate)]-VSUVT or Zeonor 1060R was compared to that with high-quality glass slide microarrays by using both passive and microfluidic hybridization systems. Specific hybridization signal-to-background ratios comparable to that obtained with high-quality commercial glass slides were achieved with both polymeric substrates. Microarray hybridizations demonstrated an analytical sensitivity equivalent to approximately 100 viral genome copies per RT-PCR, which is at least 100-fold higher than the sensitivities of previously reported DNA hybridizations on plastic supports. Testing of these plastic polymers using a microfluidic microarray hybridization platform also showed results that were comparable to those with glass supports. In conclusion, PMMA-VSUVT and Zeonor 1060R are both suitable for highly sensitive microarray hybridizations. PMID:18784318

Cortical activation during Braille reading is influenced by early visual experience in subjects with severe visual disability: a correlational fMRI study.

PubMed

Melzer, P; Morgan, V L; Pickens, D R; Price, R R; Wall, R S; Ebner, F F

2001-11-01

Functional magnetic resonance imaging was performed on blind adults resting and reading Braille. The strongest activation was found in primary somatic sensory/motor cortex on both cortical hemispheres. Additional foci of activation were situated in the parietal, temporal, and occipital lobes where visual information is processed in sighted persons. The regions were differentiated most in the correlation of their time courses of activation with resting and reading. Differences in magnitude and expanse of activation were substantially less significant. Among the traditionally visual areas, the strength of correlation was greatest in posterior parietal cortex and moderate in occipitotemporal, lateral occipital, and primary visual cortex. It was low in secondary visual cortex as well as in dorsal and ventral inferior temporal cortex and posterior middle temporal cortex. Visual experience increased the strength of correlation in all regions except dorsal inferior temporal and posterior parietal cortex. The greatest statistically significant increase, i.e., approximately 30%, was in ventral inferior temporal and posterior middle temporal cortex. In these regions, words are analyzed semantically, which may be facilitated by visual experience. In contrast, visual experience resulted in a slight, insignificant diminution of the strength of correlation in dorsal inferior temporal cortex where language is analyzed phonetically. These findings affirm that posterior temporal regions are engaged in the processing of written language. Moreover, they suggest that this function is modified by early visual experience. Furthermore, visual experience significantly strengthened the correlation of activation and Braille reading in occipital regions traditionally involved in the processing of visual features and object recognition suggesting a role for visual imagery. Copyright 2001 Wiley-Liss, Inc.

Polysaccharide Microarray Technology for the Detection of Burkholderia Pseudomallei and Burkholderia Mallei Antibodies

DTIC Science & Technology

2006-04-27

polysaccharide microarray platform was prepared by immobilizing Burkholderia pseudomallei and Burkholderia mallei polysaccharides . This... polysaccharide array was tested with success for detecting B. pseudomallei and B. mallei serum (human and animal) antibodies. The advantages of this microarray... Polysaccharide microarrays; Burkholderia pseudomallei; Burkholderia mallei; Glanders; Melioidosis1. Introduction There has been a great deal of emphasis on the

Microarray-integrated optoelectrofluidic immunoassay system

PubMed Central

Han, Dongsik

2016-01-01

A microarray-based analytical platform has been utilized as a powerful tool in biological assay fields. However, an analyte depletion problem due to the slow mass transport based on molecular diffusion causes low reaction efficiency, resulting in a limitation for practical applications. This paper presents a novel method to improve the efficiency of microarray-based immunoassay via an optically induced electrokinetic phenomenon by integrating an optoelectrofluidic device with a conventional glass slide-based microarray format. A sample droplet was loaded between the microarray slide and the optoelectrofluidic device on which a photoconductive layer was deposited. Under the application of an AC voltage, optically induced AC electroosmotic flows caused by a microarray-patterned light actively enhanced the mass transport of target molecules at the multiple assay spots of the microarray simultaneously, which reduced tedious reaction time from more than 30 min to 10 min. Based on this enhancing effect, a heterogeneous immunoassay with a tiny volume of sample (5 μl) was successfully performed in the microarray-integrated optoelectrofluidic system using immunoglobulin G (IgG) and anti-IgG, resulting in improved efficiency compared to the static environment. Furthermore, the application of multiplex assays was also demonstrated by multiple protein detection. PMID:27190571

Microarray-integrated optoelectrofluidic immunoassay system.

PubMed

Han, Dongsik; Park, Je-Kyun

2016-05-01

A microarray-based analytical platform has been utilized as a powerful tool in biological assay fields. However, an analyte depletion problem due to the slow mass transport based on molecular diffusion causes low reaction efficiency, resulting in a limitation for practical applications. This paper presents a novel method to improve the efficiency of microarray-based immunoassay via an optically induced electrokinetic phenomenon by integrating an optoelectrofluidic device with a conventional glass slide-based microarray format. A sample droplet was loaded between the microarray slide and the optoelectrofluidic device on which a photoconductive layer was deposited. Under the application of an AC voltage, optically induced AC electroosmotic flows caused by a microarray-patterned light actively enhanced the mass transport of target molecules at the multiple assay spots of the microarray simultaneously, which reduced tedious reaction time from more than 30 min to 10 min. Based on this enhancing effect, a heterogeneous immunoassay with a tiny volume of sample (5 μl) was successfully performed in the microarray-integrated optoelectrofluidic system using immunoglobulin G (IgG) and anti-IgG, resulting in improved efficiency compared to the static environment. Furthermore, the application of multiplex assays was also demonstrated by multiple protein detection.

Carbohydrate Microarray Technology Applied to High-Throughput Mapping of Plant Cell Wall Glycans Using Comprehensive Microarray Polymer Profiling (CoMPP).

PubMed

Kračun, Stjepan Krešimir; Fangel, Jonatan Ulrik; Rydahl, Maja Gro; Pedersen, Henriette Lodberg; Vidal-Melgosa, Silvia; Willats, William George Tycho

2017-01-01

Cell walls are an important feature of plant cells and a major component of the plant glycome. They have both structural and physiological functions and are critical for plant growth and development. The diversity and complexity of these structures demand advanced high-throughput techniques to answer questions about their structure, functions and roles in both fundamental and applied scientific fields. Microarray technology provides both the high-throughput and the feasibility aspects required to meet that demand. In this chapter, some of the most recent microarray-based techniques relating to plant cell walls are described together with an overview of related contemporary techniques applied to carbohydrate microarrays and their general potential in glycoscience. A detailed experimental procedure for high-throughput mapping of plant cell wall glycans using the comprehensive microarray polymer profiling (CoMPP) technique is included in the chapter and provides a good example of both the robust and high-throughput nature of microarrays as well as their applicability to plant glycomics.

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

Visual Search in ASD: Instructed Versus Spontaneous Local and Global Processing.

PubMed

Van der Hallen, Ruth; Evers, Kris; Boets, Bart; Steyaert, Jean; Noens, Ilse; Wagemans, Johan

2016-09-01

Visual search has been used extensively to investigate differences in mid-level visual processing between individuals with ASD and TD individuals. The current study employed two visual search paradigms with Gaborized stimuli to assess the impact of task distractors (Experiment 1) and task instruction (Experiment 2) on local-global visual processing in ASD versus TD children. Experiment 1 revealed both groups to be equally sensitive to the absence or presence of a distractor, regardless of the type of target or type of distractor. Experiment 2 revealed a differential effect of task instruction for ASD compared to TD, regardless of the type of target. Taken together, these results stress the importance of task factors in the study of local-global visual processing in ASD.

ExprAlign - the identification of ESTs in non-model species by alignment of cDNA microarray expression profiles

PubMed Central

2009-01-01

Background Sequence identification of ESTs from non-model species offers distinct challenges particularly when these species have duplicated genomes and when they are phylogenetically distant from sequenced model organisms. For the common carp, an environmental model of aquacultural interest, large numbers of ESTs remained unidentified using BLAST sequence alignment. We have used the expression profiles from large-scale microarray experiments to suggest gene identities. Results Expression profiles from ~700 cDNA microarrays describing responses of 7 major tissues to multiple environmental stressors were used to define a co-expression landscape. This was based on the Pearsons correlation coefficient relating each gene with all other genes, from which a network description provided clusters of highly correlated genes as 'mountains'. We show that these contain genes with known identities and genes with unknown identities, and that the correlation constitutes evidence of identity in the latter. This procedure has suggested identities to 522 of 2701 unknown carp ESTs sequences. We also discriminate several common carp genes and gene isoforms that were not discriminated by BLAST sequence alignment alone. Precision in identification was substantially improved by use of data from multiple tissues and treatments. Conclusion The detailed analysis of co-expression landscapes is a sensitive technique for suggesting an identity for the large number of BLAST unidentified cDNAs generated in EST projects. It is capable of detecting even subtle changes in expression profiles, and thereby of distinguishing genes with a common BLAST identity into different identities. It benefits from the use of multiple treatments or contrasts, and from the large-scale microarray data. PMID:19939286

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

PubMed Central

Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

2016-01-01

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

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.

Near-isogenic cotton germplasm lines that differ in fiber-bundle strength have temporal differences in fiber gene expression patterns as revealed by comparative high-throughput profiling.

PubMed

Hinchliffe, Doug J; Meredith, William R; Yeater, Kathleen M; Kim, Hee Jin; Woodward, Andrew W; Chen, Z Jeffrey; Triplett, Barbara A

2010-05-01

Gene expression profiles of developing cotton (Gossypium hirsutum L.) fibers from two near-isogenic lines (NILs) that differ in fiber-bundle strength, short-fiber content, and in fewer than two genetic loci were compared using an oligonucleotide microarray. Fiber gene expression was compared at five time points spanning fiber elongation and secondary cell wall (SCW) biosynthesis. Fiber samples were collected from field plots in a randomized, complete block design, with three spatially distinct biological replications for each NIL at each time point. Microarray hybridizations were performed in a loop experimental design that allowed comparisons of fiber gene expression profiles as a function of time between the two NILs. Overall, developmental expression patterns revealed by the microarray experiment agreed with previously reported cotton fiber gene expression patterns for specific genes. Additionally, genes expressed coordinately with the onset of SCW biosynthesis in cotton fiber correlated with gene expression patterns of other SCW-producing plant tissues. Functional classification and enrichment analysis of differentially expressed genes between the two NILs revealed that genes associated with SCW biosynthesis were significantly up-regulated in fibers of the high-fiber quality line at the transition stage of cotton fiber development. For independent corroboration of the microarray results, 15 genes were selected for quantitative reverse transcription PCR analysis of fiber gene expression. These analyses, conducted over multiple field years, confirmed the temporal difference in fiber gene expression between the two NILs. We hypothesize that the loci conferring temporal differences in fiber gene expression between the NILs are important regulatory sequences that offer the potential for more targeted manipulation of cotton fiber quality.

Words, Shape, Visual Search and Visual Working Memory in 3-Year-Old Children

ERIC Educational Resources Information Center

Vales, Catarina; Smith, Linda B.

2015-01-01

Do words cue children's visual attention, and if so, what are the relevant mechanisms? Across four experiments, 3-year-old children (N = 163) were tested in visual search tasks in which targets were cued with only a visual preview versus a visual preview and a spoken name. The experiments were designed to determine whether labels facilitated…

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

Inferring Molecular Processes Heterogeneity from Transcriptional Data.

PubMed

Gogolewski, Krzysztof; Wronowska, Weronika; Lech, Agnieszka; Lesyng, Bogdan; Gambin, Anna

2017-01-01

RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs.

Inferring Molecular Processes Heterogeneity from Transcriptional Data

PubMed Central

Wronowska, Weronika; Lesyng, Bogdan; Gambin, Anna

2017-01-01

RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs. PMID:29362714

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

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

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.

Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

PubMed

Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

2011-02-01

We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

6th Yahya Cohen Lecture: visual experience during cataract surgery.

PubMed

Au Eong, K G

2002-09-01

The visual sensations many patients experience during cataract surgery under local anaesthesia have received little attention until recently. This paper reviews the recent studies on this phenomenon, discusses its clinical significance and suggests novel approaches to reduce its negative impact on the surgery. Literature review. Many patients who have cataract surgery under retrobulbar, peribulbar or topical anaesthesia experience a variety of visual sensations in their operated eye during surgery. These visual sensations include perception of light, movements, flashes, one or more colours, surgical instruments, the surgeon's hand/fingers, the surgeon and changes in light brightness. Some patients experience transient no light perception, even if the operation is performed under topical anaesthesia. The clinical significance of this phenomenon lies in the fact that approximately 7.1% to 15.4% of patients find their visual experience frightening. This fear and anxiety may cause some patients to become uncooperative during surgery and trigger a sympathetic surge, causing such undesirable effects as hypertension, tachycardia, ischaemic strain on the heart, hyperventilation and acute panic attack. Several approaches to reduce the negative impact of patients' visual experience are suggested, including appropriate preoperative counselling and reducing the ability of patients to see during surgery. The findings that some patients find their intraoperative visual experience distressing have a major impact on the way ophthalmologists manage their cataract patients. To reduce its negative impact, surgeons should consider incorporating appropriate preoperative counselling on potential intraoperative visual experience when obtaining informed consent for surgery.

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.

Evolution of the MIDTAL microarray: the adaption and testing of oligonucleotide 18S and 28S rDNA probes and evaluation of subsequent microarray generations with Prymnesium spp. cultures and field samples.

PubMed

McCoy, Gary R; Touzet, Nicolas; Fleming, Gerard T A; Raine, Robin

2015-07-01

The toxic microalgal species Prymnesium parvum and Prymnesium polylepis are responsible for numerous fish kills causing economic stress on the aquaculture industry and, through the consumption of contaminated shellfish, can potentially impact on human health. Monitoring of toxic phytoplankton is traditionally carried out by light microscopy. However, molecular methods of identification and quantification are becoming more common place. This study documents the optimisation of the novel Microarrays for the Detection of Toxic Algae (MIDTAL) microarray from its initial stages to the final commercial version now available from Microbia Environnement (France). Existing oligonucleotide probes used in whole-cell fluorescent in situ hybridisation (FISH) for Prymnesium species from higher group probes to species-level probes were adapted and tested on the first-generation microarray. The combination and interaction of numerous other probes specific for a whole range of phytoplankton taxa also spotted on the chip surface caused high cross reactivity, resulting in false-positive results on the microarray. The probe sequences were extended for the subsequent second-generation microarray, and further adaptations of the hybridisation protocol and incubation temperatures significantly reduced false-positive readings from the first to the second-generation chip, thereby increasing the specificity of the MIDTAL microarray. Additional refinement of the subsequent third-generation microarray protocols with the addition of a poly-T amino linker to the 5' end of each probe further enhanced the microarray performance but also highlighted the importance of optimising RNA labelling efficiency when testing with natural seawater samples from Killary Harbour, Ireland.

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.

Verbal overshadowing of visual memories: some things are better left unsaid.

PubMed

Schooler, J W; Engstler-Schooler, T Y

1990-01-01

It is widely believed that verbal processing generally improves memory performance. However, in a series of six experiments, verbalizing the appearance of previously seen visual stimuli impaired subsequent recognition performance. In Experiment 1, subjects viewed a videotape including a salient individual. Later, some subjects described the individual's face. Subjects who verbalized the face performed less well on a subsequent recognition test than control subjects who did not engage in memory verbalization. The results of Experiment 2 replicated those of Experiment 1 and further clarified the effect of memory verbalization by demonstrating that visualization does not impair face recognition. In Experiments 3 and 4 we explored the hypothesis that memory verbalization impairs memory for stimuli that are difficult to put into words. In Experiment 3 memory impairment followed the verbalization of a different visual stimulus: color. In Experiment 4 marginal memory improvement followed the verbalization of a verbal stimulus: a brief spoken statement. In Experiments 5 and 6 the source of verbally induced memory impairment was explored. The results of Experiment 5 suggested that the impairment does not reflect a temporary verbal set, but rather indicates relatively long-lasting memory interference. Finally, Experiment 6 demonstrated that limiting subjects' time to make recognition decisions alleviates the impairment, suggesting that memory verbalization overshadows but does not eradicate the original visual memory. This collection of results is consistent with a recording interference hypothesis: verbalizing a visual memory may produce a verbally biased memory representation that can interfere with the application of the original visual memory.

An Interview with Matthew P. Greving, PhD. Interview by Vicki Glaser.

PubMed

Greving, Matthew P

2011-10-01

Matthew P. Greving is Chief Scientific Officer at Nextval Inc., a company founded in early 2010 that has developed a discovery platform called MassInsight™.. He received his PhD in Biochemistry from Arizona State University, and prior to that he spent nearly 7 years working as a software engineer. This experience in solving complex computational problems fueled his interest in developing technologies and algorithms related to acquisition and analysis of high-dimensional biochemical data. To address the existing problems associated with label-based microarray readouts, he beganwork on a technique for label-free mass spectrometry (MS) microarray readout compatible with both matrix-assisted laser/desorption ionization (MALDI) and matrix-free nanostructure initiator mass spectrometry (NIMS). This is the core of Nextval’s MassInsight technology, which utilizes picoliter noncontact deposition of high-density arrays on mass-readout substrates along with computational algorithms for high-dimensional data processingand reduction.

SigReannot-mart: a query environment for expression microarray probe re-annotations.

PubMed

Moreews, François; Rauffet, Gaelle; Dehais, Patrice; Klopp, Christophe

2011-01-01

Expression microarrays are commonly used to study transcriptomes. Most of the arrays are now based on oligo-nucleotide probes. Probe design being a tedious task, it often takes place once at the beginning of the project. The oligo set is then used for several years. During this time period, the knowledge gathered by the community on the genome and the transcriptome increases and gets more precise. Therefore re-annotating the set is essential to supply the biologists with up-to-date annotations. SigReannot-mart is a query environment populated with regularly updated annotations for different oligo sets. It stores the results of the SigReannot pipeline that has mainly been used on farm and aquaculture species. It permits easy extraction in different formats using filters. It is used to compare probe sets on different criteria, to choose the set for a given experiment to mix probe sets in order to create a new one.

Gaussian mixture clustering and imputation of microarray data.

PubMed

Ouyang, Ming; Welsh, William J; Georgopoulos, Panos

2004-04-12

In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.

Direct multiplexed measurement of gene expression with color-coded probe pairs.

PubMed

Geiss, Gary K; Bumgarner, Roger E; Birditt, Brian; Dahl, Timothy; Dowidar, Naeem; Dunaway, Dwayne L; Fell, H Perry; Ferree, Sean; George, Renee D; Grogan, Tammy; James, Jeffrey J; Maysuria, Malini; Mitton, Jeffrey D; Oliveri, Paola; Osborn, Jennifer L; Peng, Tao; Ratcliffe, Amber L; Webster, Philippa J; Davidson, Eric H; Hood, Leroy; Dimitrov, Krassen

2008-03-01

We describe a technology, the NanoString nCounter gene expression system, which captures and counts individual mRNA transcripts. Advantages over existing platforms include direct measurement of mRNA expression levels without enzymatic reactions or bias, sensitivity coupled with high multiplex capability, and digital readout. Experiments performed on 509 human genes yielded a replicate correlation coefficient of 0.999, a detection limit between 0.1 fM and 0.5 fM, and a linear dynamic range of over 500-fold. Comparison of the NanoString nCounter gene expression system with microarrays and TaqMan PCR demonstrated that the nCounter system is more sensitive than microarrays and similar in sensitivity to real-time PCR. Finally, a comparison of transcript levels for 21 genes across seven samples measured by the nCounter system and SYBR Green real-time PCR demonstrated similar patterns of gene expression at all transcript levels.

Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data.

PubMed

Tsou, Ann-Ping; Sun, Yi-Ming; Liu, Chia-Lin; Huang, Hsien-Da; Horng, Jorng-Tzong; Tsai, Meng-Feng; Liu, Baw-Juine

2006-07-01

Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.

Two-Dimensional VO2 Mesoporous Microarrays for High-Performance Supercapacitor

NASA Astrophysics Data System (ADS)

Fan, Yuqi; Ouyang, Delong; Li, Bao-Wen; Dang, Feng; Ren, Zongming

2018-05-01

Two-dimensional (2D) mesoporous VO2 microarrays have been prepared using an organic-inorganic liquid interface. The units of microarrays consist of needle-like VO2 particles with a mesoporous structure, in which crack-like pores with a pore size of about 2 nm and depth of 20-100 nm are distributed on the particle surface. The liquid interface acts as a template for the formation of the 2D microarrays, as identified from the kinetic observation. Due to the mesoporous structure of the units and high conductivity of the microarray, such 2D VO2 microarrays exhibit a high specific capacitance of 265 F/g at 1 A/g and excellent rate capability (182 F/g at 10 A/g) and cycling stability, suggesting the effect of unique microstructure for improving the electrochemical performance.

Development of cortical orientation selectivity in the absence of visual experience with contour

PubMed Central

Hussain, Shaista; Weliky, Michael

2011-01-01

Visual cortical neurons are selective for the orientation of lines, and the full development of this selectivity requires natural visual experience after eye opening. Here we examined whether this selectivity develops without seeing lines and contours. Juvenile ferrets were reared in a dark room and visually trained by being shown a movie of flickering, sparse spots. We found that despite the lack of contour visual experience, the cortical neurons of these ferrets developed strong orientation selectivity and exhibited simple-cell receptive fields. This finding suggests that overt contour visual experience is unnecessary for the maturation of orientation selectivity and is inconsistent with the computational models that crucially require the visual inputs of lines and contours for the development of orientation selectivity. We propose that a correlation-based model supplemented with a constraint on synaptic strength dynamics is able to account for our experimental result. PMID:21753023

Resting-State Retinotopic Organization in the Absence of Retinal Input and Visual Experience

PubMed Central

Binda, Paola; Benson, Noah C.; Bridge, Holly; Watkins, Kate E.

2015-01-01

Early visual areas have neuronal receptive fields that form a sampling mosaic of visual space, resulting in a series of retinotopic maps in which the same region of space is represented in multiple visual areas. It is not clear to what extent the development and maintenance of this retinotopic organization in humans depend on retinal waves and/or visual experience. We examined the corticocortical receptive field organization of resting-state BOLD data in normally sighted, early blind, and anophthalmic (in which both eyes fail to develop) individuals and found that resting-state correlations between V1 and V2/V3 were retinotopically organized for all subject groups. These results show that the gross retinotopic pattern of resting-state connectivity across V1-V3 requires neither retinal waves nor visual experience to develop and persist into adulthood. SIGNIFICANCE STATEMENT Evidence from resting-state BOLD data suggests that the connections between early visual areas develop and are maintained even in the absence of retinal waves and visual experience. PMID:26354906

An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer.

PubMed

Lorenzo, Enery; Camacho-Caceres, Katia; Ropelewski, Alexander J; Rosas, Juan; Ortiz-Mojer, Michael; Perez-Marty, Lynn; Irizarry, Juan; Gonzalez, Valerie; Rodríguez, Jesús A; Cabrera-Rios, Mauricio; Isaza, Clara

2015-06-01

Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path.

Experience With Rapid Microarray-Based Diagnostic Technology and Antimicrobial Stewardship for Patients With Gram-Positive Bacteremia.

PubMed

Neuner, Elizabeth A; Pallotta, Andrea M; Lam, Simon W; Stowe, David; Gordon, Steven M; Procop, Gary W; Richter, Sandra S

2016-11-01

OBJECTIVE To describe the impact of rapid diagnostic microarray technology and antimicrobial stewardship for patients with Gram-positive blood cultures. DESIGN Retrospective pre-intervention/post-intervention study. SETTING A 1,200-bed academic medical center. PATIENTS Inpatients with blood cultures positive for Staphylococcus aureus, Enterococcus faecalis, E. faecium, Streptococcus pneumoniae, S. pyogenes, S. agalactiae, S. anginosus, Streptococcus spp., and Listeria monocytogenes during the 6 months before and after implementation of Verigene Gram-positive blood culture microarray (BC-GP) with an antimicrobial stewardship intervention. METHODS Before the intervention, no rapid diagnostic technology was used or antimicrobial stewardship intervention was undertaken, except for the use of peptide nucleic acid fluorescent in situ hybridization and MRSA agar to identify staphylococcal isolates. After the intervention, all Gram-positive blood cultures underwent BC-GP microarray and the antimicrobial stewardship intervention consisting of real-time notification and pharmacist review. RESULTS In total, 513 patients with bacteremia were included in this study: 280 patients with S. aureus, 150 patients with enterococci, 82 patients with stretococci, and 1 patient with L. monocytogenes. The number of antimicrobial switches was similar in the pre-BC-GP (52%; 155 of 300) and post-BC-GP (50%; 107 of 213) periods. The time to antimicrobial switch was significantly shorter in the post-BC-GP group than in the pre-BC-GP group: 48±41 hours versus 75±46 hours, respectively (P<.001). The most common antimicrobial switch was de-escalation and time to de-escalation, was significantly shorter in the post-BC-GP group than in the pre-BC-GP group: 53±41 hours versus 82±48 hours, respectively (P<.001). There was no difference in mortality or hospital length of stay as a result of the intervention. CONCLUSIONS The combination of a rapid microarray diagnostic test with an antimicrobial stewardship intervention improved time to antimicrobial switch, especially time to de-escalation to optimal therapy, in patients with Gram-positive blood cultures. Infect Control Hosp Epidemiol 2016;1-6.

Experimental analysis of oligonucleotide microarray design criteria to detect deletions by comparative genomic hybridization.

PubMed

Flibotte, Stephane; Moerman, Donald G

2008-10-21

Microarray comparative genomic hybridization (CGH) is currently one of the most powerful techniques to measure DNA copy number in large genomes. In humans, microarray CGH is widely used to assess copy number variants in healthy individuals and copy number aberrations associated with various diseases, syndromes and disease susceptibility. In model organisms such as Caenorhabditis elegans (C. elegans) the technique has been applied to detect mutations, primarily deletions, in strains of interest. Although various constraints on oligonucleotide properties have been suggested to minimize non-specific hybridization and improve the data quality, there have been few experimental validations for CGH experiments. For genomic regions where strict design filters would limit the coverage it would also be useful to quantify the expected loss in data quality associated with relaxed design criteria. We have quantified the effects of filtering various oligonucleotide properties by measuring the resolving power for detecting deletions in the human and C. elegans genomes using NimbleGen microarrays. Approximately twice as many oligonucleotides are typically required to be affected by a deletion in human DNA samples in order to achieve the same statistical confidence as one would observe for a deletion in C. elegans. Surprisingly, the ability to detect deletions strongly depends on the oligonucleotide 15-mer count, which is defined as the sum of the genomic frequency of all the constituent 15-mers within the oligonucleotide. A similarity level above 80% to non-target sequences over the length of the probe produces significant cross-hybridization. We recommend the use of a fairly large melting temperature window of up to 10 degrees C, the elimination of repeat sequences, the elimination of homopolymers longer than 5 nucleotides, and a threshold of -1 kcal/mol on the oligonucleotide self-folding energy. We observed very little difference in data quality when varying the oligonucleotide length between 50 and 70, and even when using an isothermal design strategy. We have determined experimentally the effects of varying several key oligonucleotide microarray design criteria for detection of deletions in C. elegans and humans with NimbleGen's CGH technology. Our oligonucleotide design recommendations should be applicable for CGH analysis in most species.

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

Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach.

PubMed

Motakis, E S; Nason, G P; Fryzlewicz, P; Rutter, G A

2006-10-15

Many standard statistical techniques are effective on data that are normally distributed with constant variance. Microarray data typically violate these assumptions since they come from non-Gaussian distributions with a non-trivial mean-variance relationship. Several methods have been proposed that transform microarray data to stabilize variance and draw its distribution towards the Gaussian. Some methods, such as log or generalized log, rely on an underlying model for the data. Others, such as the spread-versus-level plot, do not. We propose an alternative data-driven multiscale approach, called the Data-Driven Haar-Fisz for microarrays (DDHFm) with replicates. DDHFm has the advantage of being 'distribution-free' in the sense that no parametric model for the underlying microarray data is required to be specified or estimated; hence, DDHFm can be applied very generally, not just to microarray data. DDHFm achieves very good variance stabilization of microarray data with replicates and produces transformed intensities that are approximately normally distributed. Simulation studies show that it performs better than other existing methods. Application of DDHFm to real one-color cDNA data validates these results. The R package of the Data-Driven Haar-Fisz transform (DDHFm) for microarrays is available in Bioconductor and CRAN.

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

Microintaglio Printing for Soft Lithography-Based in Situ Microarrays

PubMed Central

Biyani, Manish; Ichiki, Takanori

2015-01-01

Advances in lithographic approaches to fabricating bio-microarrays have been extensively explored over the last two decades. However, the need for pattern flexibility, a high density, a high resolution, affordability and on-demand fabrication is promoting the development of unconventional routes for microarray fabrication. This review highlights the development and uses of a new molecular lithography approach, called “microintaglio printing technology”, for large-scale bio-microarray fabrication using a microreactor array (µRA)-based chip consisting of uniformly-arranged, femtoliter-size µRA molds. In this method, a single-molecule-amplified DNA microarray pattern is self-assembled onto a µRA mold and subsequently converted into a messenger RNA or protein microarray pattern by simultaneously producing and transferring (immobilizing) a messenger RNA or a protein from a µRA mold to a glass surface. Microintaglio printing allows the self-assembly and patterning of in situ-synthesized biomolecules into high-density (kilo-giga-density), ordered arrays on a chip surface with µm-order precision. This holistic aim, which is difficult to achieve using conventional printing and microarray approaches, is expected to revolutionize and reshape proteomics. This review is not written comprehensively, but rather substantively, highlighting the versatility of microintaglio printing for developing a prerequisite platform for microarray technology for the postgenomic era. PMID:27600226

Mathematics make microbes beautiful, beneficial, and bountiful.

PubMed

Jungck, John R

2012-01-01

Microbiology is a rich area for visualizing the importance of mathematics in terms of designing experiments, data mining, testing hypotheses, and visualizing relationships. Historically, Nobel Prizes have acknowledged the close interplay between mathematics and microbiology in such examples as the fluctuation test and mutation rates using Poisson statistics by Luria and Delbrück and the use of graph theory of polyhedra by Caspar and Klug. More and more contemporary microbiology journals feature mathematical models, computational algorithms and heuristics, and multidimensional visualizations. While revolutions in research have driven these initiatives, a commensurate effort needs to be made to incorporate much more mathematics into the professional preparation of microbiologists. In order not to be daunting to many educators, a Bloom-like "Taxonomy of Quantitative Reasoning" is shared with explicit examples of microbiological activities for engaging students in (a) counting, measuring, calculating using image analysis of bacterial colonies and viral infections on variegated leaves, measurement of fractal dimensions of beautiful colony morphologies, and counting vertices, edges, and faces on viral capsids and using graph theory to understand self assembly; (b) graphing, mapping, ordering by applying linear, exponential, and logistic growth models of public health and sanitation problems, revisiting Snow's epidemiological map of cholera with computational geometry, and using interval graphs to do complementation mapping, deletion mapping, food webs, and microarray heatmaps; (c) problem solving by doing gene mapping and experimental design, and applying Boolean algebra to gene regulation of operons; (d) analysis of the "Bacterial Bonanza" of microbial sequence and genomic data using bioinformatics and phylogenetics; (e) hypothesis testing-again with phylogenetic trees and use of Poisson statistics and the Luria-Delbrück fluctuation test; and (f) modeling of biodiversity by using game theory, of epidemics with algebraic models, bacterial motion by using motion picture analysis and fluid mechanics of motility in multiple dimensions through the physics of "Life at Low Reynolds Numbers," and pattern formation of quorum sensing bacterial populations. Through a developmental model for preprofessional education that emphasizes the beauty, utility, and diversity of microbiological systems, we hope to foster creativity as well as mathematically rigorous reasoning. Copyright © 2012 Elsevier Inc. All rights reserved.

Independent sources of anisotropy in visual orientation representation: a visual and a cognitive oblique effect.

PubMed

Balikou, Panagiota; Gourtzelidis, Pavlos; Mantas, Asimakis; Moutoussis, Konstantinos; Evdokimidis, Ioannis; Smyrnis, Nikolaos

2015-11-01

The representation of visual orientation is more accurate for cardinal orientations compared to oblique, and this anisotropy has been hypothesized to reflect a low-level visual process (visual, "class 1" oblique effect). The reproduction of directional and orientation information also leads to a mean error away from cardinal orientations or directions. This anisotropy has been hypothesized to reflect a high-level cognitive process of space categorization (cognitive, "class 2," oblique effect). This space categorization process would be more prominent when the visual representation of orientation degrades such as in the case of working memory with increasing cognitive load, leading to increasing magnitude of the "class 2" oblique effect, while the "class 1" oblique effect would remain unchanged. Two experiments were performed in which an array of orientation stimuli (1-4 items) was presented and then subjects had to realign a probe stimulus within the previously presented array. In the first experiment, the delay between stimulus presentation and probe varied, while in the second experiment, the stimulus presentation time varied. The variable error was larger for oblique compared to cardinal orientations in both experiments reproducing the visual "class 1" oblique effect. The mean error also reproduced the tendency away from cardinal and toward the oblique orientations in both experiments (cognitive "class 2" oblique effect). The accuracy or the reproduced orientation degraded (increasing variable error) and the cognitive "class 2" oblique effect increased with increasing memory load (number of items) in both experiments and presentation time in the second experiment. In contrast, the visual "class 1" oblique effect was not significantly modulated by any one of these experimental factors. These results confirmed the theoretical predictions for the two anisotropies in visual orientation reproduction and provided support for models proposing the categorization of orientation in visual working memory.

Experiences of Students with Visual Impairments in Canadian Higher Education

ERIC Educational Resources Information Center

Reed, Maureen; Curtis, Kathryn

2012-01-01

Introduction: This article presents a study of the higher education experiences of students with visual impairments in Canada. Methods: Students with visual impairments and the staff members of disability programs were surveyed and interviewed regarding the students' experiences in entering higher education and completing their higher education…

Experience, Context, and the Visual Perception of Human Movement

ERIC Educational Resources Information Center

Jacobs, Alissa; Pinto, Jeannine; Shiffrar, Maggie

2004-01-01

Why are human observers particularly sensitive to human movement? Seven experiments examined the roles of visual experience and motor processes in human movement perception by comparing visual sensitivities to point-light displays of familiar, unusual, and impossible gaits across gait-speed and identity discrimination tasks. In both tasks, visual…

Fluorescence-based bioassays for the detection and evaluation of food materials.

PubMed

Nishi, Kentaro; Isobe, Shin-Ichiro; Zhu, Yun; Kiyama, Ryoiti

2015-10-13

We summarize here the recent progress in fluorescence-based bioassays for the detection and evaluation of food materials by focusing on fluorescent dyes used in bioassays and applications of these assays for food safety, quality and efficacy. Fluorescent dyes have been used in various bioassays, such as biosensing, cell assay, energy transfer-based assay, probing, protein/immunological assay and microarray/biochip assay. Among the arrays used in microarray/biochip assay, fluorescence-based microarrays/biochips, such as antibody/protein microarrays, bead/suspension arrays, capillary/sensor arrays, DNA microarrays/polymerase chain reaction (PCR)-based arrays, glycan/lectin arrays, immunoassay/enzyme-linked immunosorbent assay (ELISA)-based arrays, microfluidic chips and tissue arrays, have been developed and used for the assessment of allergy/poisoning/toxicity, contamination and efficacy/mechanism, and quality control/safety. DNA microarray assays have been used widely for food safety and quality as well as searches for active components. DNA microarray-based gene expression profiling may be useful for such purposes due to its advantages in the evaluation of pathway-based intracellular signaling in response to food materials.