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

Soft Subspace Clustering for Cancer Microarray Data Analysis

2014 ◽  
pp. 131-145
Author(s):  
Natthakan Iam-On ◽  
Tossapon Boongoen
Keyword(s):  
Microarray Data ◽  
Dimensional Space ◽  
Subspace Clustering ◽  
Research Direction ◽  
Microarray Data Analysis ◽  
Consensus Clustering ◽  
Gene Expressions ◽  
Tumor Subtypes ◽  
Microarray Gene ◽  
New Research

A need has long been identified for a more effective methodology to understand, prevent, and cure cancer. Microarray technology provides a basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes, and individualized treatment. Recently, soft subspace clustering was introduced as an accurate alternative to conventional techniques. This practice has proven effective for high dimensional data, especially for microarray gene expressions. In this review, the basis of weighted dimensional space and different approaches to soft subspace clustering are described. Since most of the models are parameterized, the application of consensus clustering has been identified as a new research direction that is capable of turning the difficulty with parameter selection to an advantage of increasing diversity within an ensemble.

scholarly journals Microarray Probe Expression Measures, Data Normalization and Statistical Validation

2003 ◽  
Vol 4 (4) ◽  
pp. 442-446 ◽  
Author(s):  
Silvia Saviozzi ◽  
Raffaele A. Calogero
Keyword(s):  
Microarray Data ◽  
Microarray Data Analysis ◽  
Data Normalization ◽  
Microarray Technology ◽  
Statistical Validation ◽  
Microarray Probe ◽  
Computational Tools ◽  
Intensity Measures ◽  
High Throughput Method ◽  
Gene Expression Intensity

DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.

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 Lantern: an integrative repository of functional annotations for lncRNAs in the human genome

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Swapna Vidhur Daulatabad ◽  
Rajneesh Srivastava ◽  
Sarath Chandra Janga
Keyword(s):  
Human Genome ◽  
Cellular Localization ◽  
Expression Profiles ◽  
Free Text ◽  
Dynamic Visualization ◽  
Specific Expression ◽  
Web Resource ◽  
Functional Dynamics ◽  
Non Coding Rnas ◽  
Retrieval Engine

Abstract Background With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare. Results We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed’s abstract retrieval engine and NCBO’s recommender annotation system. Lantern’s annotations were benchmarked against lncRNAdb’s manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform. Conclusions Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern.

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.

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