MAExplorer – microarray exploratory data analysis
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Microarray databases
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To develop a tool for then easy and user-friendly management of peptide microarray experiments and for the use of the results of these experiments for the study the immune response against HIV virus infection in clinical samples.Applying bioinformatics and statistics for the analysis of data coming from microarray experiments as well as implementing a MIAME (Minimum Information About a Microarray Experiment) compliant database for managing and annotating experiments, results and samples.We present a new tool for managing not only nucleic acid microarray experiments but also protein microarray experiments. From the analysis of experimental data, we can detect different profiles in the reactivity of the sera with different genotypes.We have developed a new tool for managing microarray data including clinical annotations for the samples as well as the capability of annotating other microarray formats different to those based on nucleic acids. The use of peptide microarrays and bioinformatics analysis opens a new scope for the characterization of the immune response, and analyzing and identifying the humoral response of viruses with different genotypes.
Microarray databases
Gene chip analysis
Protein microarray
Antibody microarray
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'This review introduces the application of statistical theory in the design of cDNA microarray experiments, microarray data analysis and some problems encountered in other phases of cDNA microarray hybridization experiments. The paper also summarizes the merits of each method.
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Statistical Analysis
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DNA microarray analysis is the main core in genome mapping. Each microarray image contains millions of information about genes. Microarray analysis is considered one of the most recent and important technologies in exploring the genome. One of the key steps in microarray analysis is to extract gene information from the gene spots, these information represent gene expression levels in the microarray. This paper proposes a new methodology to improve microarray spot analysis based on spot extracted segments. It concentrates on each spot segment area independently rather than analyzing all the spots area together of the microarray image. This paper provides a formal model to enhance the intensity values obtained from gene expression levels of the microarray at any intensity expressed level. It also this paper presents the adaptive threshold techniques to be used for microarray segmentation. The experimental results show that the mean of the gene expression intensity value was 87.77. Key words: Microarray images, microarray analysis, image segmentation, gridding, microarray addressing, spot localization, spot extraction.
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Microarray databases
Gene chip analysis
Profiling (computer programming)
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This study aimed to use gene chips to investigate differential gene expression profiles in the occurrence and development of acute myocardial infarction (AMI). The study included 12 AMI patients and 12 healthy individuals. Total mRNA of peripheral bloodwas extracted and reversed-transcribed to cDNA for microarray analysis. After establishing two pools with three subjects each (3 AMI patients and 3 healthy individuals), the remaining samples were used for RT-qPCR to confirm the microarray data. From the microarray results, seven genes were randomly selected for RT-qPCR. RT-qPCR results were analyzed by the 2-ΔΔCt method. Microarray analysis showed that 228 genes were up- regulated and 271 were down-regulated (p ≤ 0.05, |logFC| > 1). Gene ontology showed that these genes belong to 128 cellular components, 521 biological processes, and 151 molecular functions. KEGG pathway analysis showed that these genes are involved in 107 gene pathways. RT-qPCR results for the seven genes showed expression levels consistent with those obtained by microarray. Thus, microarray data could be used to select the pathogenic genes for AMI. Investigating the abnormal expression of these differentially expressed genes might suggest efficient strategies for the prevention, diagnosis, and treatment of AMI.
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A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system. This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression (fold change) but they are weakly expressed in experimental studies (based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies; such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016 (up- and downregulated) genes were determined to be related to Schwann cells. However, no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes. Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
Schwann cell
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cDNA microarray technology is a powerful and efficient tool for large-scale screening differentially expressed genes simultaneously.By cDNA microarray,a total of 2 210 genes were found to be significantly up-or down-regulated in nasopharyngeal carcinoma(NPC) tissues,compared with normal nasopharynx(NP) tissues.Using the BioCarta database,a pathway-based network was constructed to visualize the relations between the genes obtained from the microarray data.With statistical analysis,a batch of genes were found(especially RAN,CEL,RELA genes) to have a significant influence on this pathway-based network.To verify the pathway network analysis results,RT-PCR was performed to detect the expression levels of RAN,CEL,RELA genes and it was found that RAN and CEL genes were significantly up-regulated in more than 80% of NPC samples.When the pathway-based network analysis results were further compared to the results obtained from ArrayXPath,a software for mapping and visualizing microarray data with integrated biological pathway resources available online,up to 40%(32/80) of genes were found to be identical,indicating the pathway-based network analysis was effective and feasible.Therefore,pathway based screening strategy should be a new way to analyze the high-throughput microarray data.
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Widespread use of microarrays has generated large amounts of data, the interrogation of the public microarray repositories, identifying similarities between microarray experiments is now one of the major challenges. Approaches using defined group of genes, such as pathways and cellular networks (pathway analysis), have been proposed to improve the interpretation of microarray experiments. We propose a novel method to compare microarray experiments at the pathway level, this method consists of two steps: first, generate pathway signatures, a set of descriptors recapitulating the biologically meaningful pathways related to some clinical/biological variable of interest, second, use these signatures to interrogate microarray databases. We demonstrate that our approach provides more reliable results than with gene-based approaches. While gene-based approaches tend to suffer from bias generated by the analytical procedures employed, our pathway based method successfully groups together similar samples, independently of the experimental design. The results presented are potentially of great interest to improve the ability to query and compare experiments in public repositories of microarray data. As a matter of fact, this method can be used to retrieve data from public microarray databases and perform comparisons at the pathway level.
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Gene chip analysis
Biological pathway
Pathway Analysis
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Microarray technology is being used widely in various biomedical research areas; the corresponding microarray data analysis is an essential step toward the best utilizing of array technologies. Here we review two components of the microarray data analysis: a low level of microarray data analysis that emphasizes the designing, the quality control, and the preprocessing of microarray experiments, then a high level of microarray data analysis that focuses on the domain-specific microarray applications such as tumor classification, biomarker prediction, analyzing array CGH experiments, and reverse engineering of gene expression networks. Additionally, we will review the recent development of building a predictive model in genome expression and regulation studies. This review may help biologists grasp a basic knowledge of microarray bioinformatics as well as its potential impact on the future evolvement of biomedical research fields.
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RiceDB，在各种各样的生物上下文注解米饭 microarray 的一个基于万维网的综合数据库被开发。它由八个模块组成。Affymetrix 探查的进程设置关于米饭印射到不同数据库的 RiceMap 模块档案，和到 microarray 代表的基因的目的由经由标识符或每个数据库的就职数字检索注解信息设定；RiceGO 模块显示在一个 microarray 集合和基因本体论之间的协会(去) 范畴；RiceKO 模块被用来基于 KEGG 生物化学的小径注解一个 microarray 集合；RiceDO 模块显示与一个 microarray 集合联系的领域的信息；RiceUP 模块被用来为一个 microarray 集合代表的所有基因获得倡导者序列；调整了基因的 RiceMR 模块表潜力 microRNA 由一个 microarray 集合代表了；RiceCD 和 RiceGF 被用来注解一个 microarray 集合在染色体分发和米饭 paralogous 家庭分发的上下文代表的基因。自动注解的结果与用手的注解主要一致。microarray 数据的生物解释被 RiceDB 的帮助加快。
Microarray databases
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Antibody microarray
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