Basic Principles and Applications of Microarrays in Medicine

2011 ◽  
pp. 367-374 ◽  
Author(s):  
Andriani Daskalaki ◽  
Athina A. Lazakidou
Keyword(s):  
Microarray Data ◽  
Growth And Development ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Disease Classification ◽  
Microarray Data Analysis ◽  
Marker Genes ◽  
Systematic Analysis ◽  
Diagnosis And Prognosis ◽  
Genome Wide Data

The simultaneous expression of a large number of genes is a critical component of normal growth and development, and the maintenance of health. Microarray technology is used to understand fundamental aspects of growth and development, as well as to explore the underlying genetic causes of many human diseases. Systematic analysis of microarray data will yield insight into molecular biological processes and the functions of thousands of gene products in parallel. This approach allows for better understanding in cellular signaling, disease classification, diagnosis, and prognosis. Microarrays allow scientists to analyze the expression of many genes in a single experiment quickly and efficiently. One important goal of computational analysis of microarrays is to extract clues from microarray data and translate the information into biological understanding diseases in medicine and dentistry. There are different platforms or types of DNA microarrays that are commercially available: Glass DNA microarrays and high-density oligonucleotide microarrays. DNA microarray experiments generate large quantities of genome-wide data. To extract useful information from expression profiles, computational tools that compute, statistically validate and display data can be used. An important step in the computation of microarray data is normalization. The purpose of the normalization prozess is to identify and remove the effects of systematic variation in the measured fluorescence intensities other than differential expressions. There are different methods for the normalization of data: total intensity normalization, regression normalization, normalization using ratio statistics, and variance stabilization (VSN). A major goal of microarray data analysis is to identify differentially expressed genes. Selecting marker genes is an important issue for disease classification based on gene expression data.

scholarly journals CIDA: An integrated software for the design, characterisation and global comparison of microarrays

2007 ◽  
Vol 4 (3) ◽  
pp. 224-242
Author(s):  
Sabah Khalid ◽  
Mohsin Khan ◽  
Alistair Symonds ◽  
Karl Fraser ◽  
Ping Wang ◽  
...  
Keyword(s):  
Human Genome ◽  
Microarray Data ◽  
Expression Profiles ◽  
Human Life ◽  
Microarray Data Analysis ◽  
Microarray Technology ◽  
Gene Chips ◽  
Global Comparison ◽  
Effective Manner ◽  
Microarray Gene

Abstract Microarray technology has had a significant impact in the field of systems biology involving the investigation into the biological systems that regulate human life. Identifying genes of significant interest within any given disease on an individual basis is no doubt time consuming and inefficient when considering the complexity of the human genome. Thus, the genetic profiling of the entire human genome in a single experiment has resulted in microarray technology becoming a widely used experimental tool. However, without the use of tools for several aspects of microarray data analysis the technology is limited. To date, no such tool has been developed that allows the integration of numerous microarray results from different research laboratories as well as the design of customised gene chips in a cost-effective manner. In light of this, we have designed the first integrated and automated software called Chip Integration, Design and Annotation (CIDA) for the cross comparison, design and functional annotation of microarray gene chips. The software provides molecular biologists with the control to cross compare the biological signatures generated from multiple microarray studies, design custom microarray gene chips based on their research requirements and lastly characterise microarray data in the context of immunogenomics. Through the relative comparison of related microarray experiments we have identified 258 genes with common gene expression profiles that are not only upregulated in anergic T cells, but also in cells over-expressing the transcription factor Egr2, that has been identified to play a role in T cell anergy. Using the gene chip design aspect of CIDA we have designed and subsequently fabricate immuno-tolerance gene chips consisting of 1758 genes for further research.The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk/CIDA/. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchgroups/mi/publications/supplementary/cida

scholarly journals Use of High-Throughput DNA Microarrays to Identify Biomarkers for Bladder Cancer

2003 ◽  
Vol 49 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Marta Sánchez-Carbayo
Keyword(s):  
Bladder Cancer ◽  
Clinical Outcome ◽  
High Throughput ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tumor Stage ◽  
Ease Of Use ◽  
Tumor Subtypes ◽  
Diagnosis And Prognosis

Abstract Background: Numerous markers have been described to correlate to some extent with tumor stage and prognosis of patients with bladder cancer. The power of many of these biomarkers in detecting superficial disease or predicting the clinical outcome of individual tumors is limited, and alternative markers are still in demand. High-throughput microarrays represent novel means for cancer research and tumor marker discovery. Approach: The aim of this report was to discuss the application of DNA technologies to provide novel biomarkers for bladder cancer. Content: Specific bladder tumor subtypes have distinct gene expression profiles. The use of high-throughput DNA microarrays allows identification of the most prevalent and relevant alterations within bladder tumors. Clusters of differentially expressed genes will become biomarkers to discriminate subgroups of patients with different histopathology or clinical outcome. Additionally, the identified individual molecular targets might be further validated and developed into novel serum or urinary biomarkers for the diagnosis and/or as prognostic factors to be applied in clinical practice. The diagnosis and prognosis of bladder cancer would be enhanced by the use of such markers, and the marker itself may constitute a therapeutic target when studied in appropriate patients and control groups. Summary: Expression profiling with high-throughput DNA microarrays has the potential of providing critical clues for the management of bladder cancer patients. As the quality, standardization, and ease of use of the technology increase and the costs decrease, DNA microarrays will move from being a technology restricted to research to clinical laboratories in the near future.

scholarly journals Algoritmos de identificación de biclústeres con evolución coherente en microarreglos de ADN [Algorithms for discover coherent evolution biclusters on DNA microarrays]

2016 ◽  
Author(s):  
Lyda Peña Paz
Keyword(s):  
Mathematical Model ◽  
Data Analysis ◽  
Microarray Data ◽  
Dna Microarrays ◽  
Expression Level ◽  
Microarray Data Analysis ◽  
New Developments ◽  
Expression Levels ◽  
Different Types

El Biclustering es una técnica empleada para el análisis de microarreglos de ADN, con el objetivo de identificar subgrupos de genes y de condiciones que muestren patrones similares de comportamiento. Existen diferentes tipos de biclústeres, siendo los de evolución coherente uno de los más difíciles de identificar debido a que no se consideran los valores exactos de los niveles de expresión sino el sentido en que se mueven. En este trabajo se presenta inicialmente un resumen de los algoritmos utilizados en la identificación de biclústeres con evolución coherente, seguido del análisis de estos y las propuestas para el desarrollo de nuevos algoritmos.Palabras Claves: Bioinformática, Microarreglos ADN, Biclustering.Biclustering is an important technique to microarray data analysis. Biclustering aims to identify a subset of genes that are co-regulated under a subset of conditions. It is possible to identify different types of biclusters in microarrays specifically, constant values, coherent values and coherent evolution. Coherent evolution bicluster is a particular type of bicluster that considers the variability of the expression level but not the exact value, so that the process for finding this kind of bicluster is more difficult because there is not a mathematical model that explains the values of expression levels included in the bicluster. This paper is a review of the different algorithm proposed to do this task and some ideas for new developments are proposed.Keywords: Bioinformatics, DNA microarrays, Biclustering

scholarly journals Transcriptomic response of Anopheles gambiae sensu stricto mosquito larvae to Curry tree (Murraya koenigii) phytochemicals

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Clarence M. Mang’era ◽  
Fathiya M. Khamis ◽  
Erick O. Awuoche ◽  
Ahmed Hassanali ◽  
Fidelis Levi Odhiambo Ombura ◽  
...  
Keyword(s):  
Immune Response ◽  
Anopheles Gambiae ◽  
Growth And Development ◽  
Leaf Extract ◽  
Expression Profiles ◽  
Sensu Stricto ◽  
Mosquito Larvae ◽  
Insect Vector ◽  
Murraya Koenigii ◽  
Transcriptomic Response

Abstract Background Insect growth regulators (IGRs) can control insect vector populations by disrupting growth and development in juvenile stages of the vectors. We previously identified and described the curry tree (Murraya koenigii (L.) Spreng) phytochemical leaf extract composition (neplanocin A, 3-(1-naphthyl)-l-alanine, lumiflavine, terezine C, agelaspongin and murrayazolinol), which disrupted growth and development in Anopheles gambiae sensu stricto mosquito larvae by inducing morphogenetic abnormalities, reducing locomotion and delaying pupation in the mosquito. Here, we attempted to establish the transcriptional process in the larvae that underpins these phenotypes in the mosquito. Methods We first exposed third-fourth instar larvae of the mosquito to the leaf extract and consequently the inherent phytochemicals (and corresponding non-exposed controls) in two independent biological replicates. We collected the larvae for our experiments sampled 24 h before peak pupation, which was 7 and 18 days post-exposure for controls and exposed larvae, respectively. The differences in duration to peak pupation were due to extract-induced growth delay in the larvae. The two study groups (exposed vs control) were consequently not age-matched. We then sequentially (i) isolated RNA (whole larvae) from each replicate treatment, (ii) sequenced the RNA on Illumina HiSeq platform, (iii) performed differential bioinformatics analyses between libraries (exposed vs control) and (iv) independently validated the transcriptome expression profiles through RT-qPCR. Results Our analyses revealed significant induction of transcripts predominantly associated with hard cuticular proteins, juvenile hormone esterases, immunity and detoxification in the larvae samples exposed to the extract relative to the non-exposed control samples. Our analysis also revealed alteration of pathways functionally associated with putrescine metabolism and structural constituents of the cuticle in the extract-exposed larvae relative to the non-exposed control, putatively linked to the exoskeleton and immune response in the larvae. The extract-exposed larvae also appeared to have suppressed pathways functionally associated with molting, cell division and growth in the larvae. However, given the age mismatch between the extract-exposed and non-exposed larvae, we can attribute the modulation of innate immune, detoxification, cuticular and associated transcripts and pathways we observed to effects of age differences among the larvae samples (exposed vs control) and to exposures of the larvae to the extract. Conclusions The exposure treatment appears to disrupt cuticular development, immune response and oxidative stress pathways in Anopheles gambiae s.s larvae. These pathways can potentially be targeted in development of more efficacious curry tree phytochemical-based IGRs against An. gambiae s.s mosquito larvae.

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

2003 ◽  
Vol 01 (03) ◽  
pp. 541-586 ◽  
Author(s):  
Tero Aittokallio ◽  
Markus Kurki ◽  
Olli Nevalainen ◽  
Tuomas Nikula ◽  
Anne West ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Biological Research ◽  
Microarray Experiments ◽  
Dna Microarray Data ◽  
Open Questions ◽  
Analysis Technique ◽  
Wide Range

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

scholarly journals AP2/ERF and R2R3-MYB family transcription factors: potential associations between temperature stress and lipid metabolism in Auxenochlorella protothecoides

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guanlan Xing ◽  
Jinyu Li ◽  
Wenli Li ◽  
Sin Man Lam ◽  
Hongli Yuan ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Lipid Metabolism ◽  
Temperature Stress ◽  
Expression Profiles ◽  
Critical Factor ◽  
Biofuel Production ◽  
Systematic Analysis ◽  
Triacylglycerol Accumulation ◽  
Response To Temperature ◽  
R2r3 Myb

Abstract Background Both APETALA2/Ethylene Responsive Factor (AP2/ERF) superfamily and R2R3-MYB family were from one of the largest diverse families of transcription factors (TFs) in plants, and played important roles in plant development and responses to various stresses. However, no systematic analysis of these TFs had been conducted in the green algae A. protothecoides heretofore. Temperature was a critical factor affecting growth and lipid metabolism of A. protothecoides. It also remained largely unknown whether these TFs would respond to temperature stress and be involved in controlling lipid metabolism process. Results Hereby, a total of six AP2 TFs, six ERF TFs and six R2R3-MYB TFs were identified and their expression profiles were also analyzed under low-temperature (LT) and high-temperature (HT) stresses. Meanwhile, differential adjustments of lipid pathways were triggered, with enhanced triacylglycerol accumulation. A co-expression network was built between these 18 TFs and 32 lipid-metabolism-related genes, suggesting intrinsic associations between TFs and the regulatory mechanism of lipid metabolism. Conclusions This study represented an important first step towards identifying functions and roles of AP2 superfamily and R2R3-MYB family in lipid adjustments and response to temperature stress. These findings would facilitate the biotechnological development in microalgae-based biofuel production and the better understanding of photosynthetic organisms’ adaptive mechanism to temperature stress.

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