检索结果-内蒙古大学图书馆

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

引用

BMC Research Notes 2012年第1期5卷 1-6页

作者： Dai, Yilin Guo, Ling Li, Meng Chen, Yi-Bu Department of Mathematical Sciences Michigan Technological University Houghton MI 49934 1400 Townsend Drive United States Bioinformatics Service Program Norris Medical Library University of Southern California Los Angeles CA 91007 2003 Zonal Ave United States

Background: 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. Findings: We present microarray ? 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. Conclusion: Coupled with a well-designed user interface, microarray ? 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. © 2012 Dai et al.;licensee BioMed Central Ltd.

关键词： Gene expression microarray data analysis Probe reannotation

来源：评论

学校读者我要写书评

暂无评论

PBC: A Software Framework Facilitating Pattern-Based Clustering for microarray data analysis

PBC: A Software Framework Facilitating Pattern-Based Cluster...

引用

International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing

作者： Shin, Dong-Guk Hong, Seung-Hyun Joshi, Pujan Nori, Ravi Pei, Baikang Wang, Hsin-Wei Harrington, Patrick Kuo, Lynn Kalajzic, Ivo Rowe, David Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Univ Connecticut Dept Stat Storrs CT 06269 USA Univ Connecticut Ctr Hlth Dept Genet & Dev Biol Farmington CT 06030 USA

ISBN: (纸本)9780769537399

microarray data produces expression pattern of thousands of genes at once. Grouping these gene expression patterns to have each group convey some biologically meaningful sight entails use of a clustering method. Two problems exist when attempting to use conventional clustering methods for the microarray data analysis. Presence of outliers skews the mean value computation which, in turn influences placement of inconsistent gene expression patterns into one group. The clustering algorithms themselves generally cannot determine the right size of the clusters. We present a new method which approaches to the clustering problem from a different angle. That is, the clustering of gene expression patterns is better dealt with within a software framework that is conducive to helping biologists derive the right size of clusters utilizing their understanding of the experimental context once the baseline clusters are computed using the fold changes of gene expression levels. We discuss our experiences of using the framework in analyzing numerous microarray data experiments.

关键词： clustering microarray data analysis data mining bioinformatic gene expression pattern

来源：评论

学校读者我要写书评

暂无评论

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

引用

BMC BIOINFORMATICS 2008年第1期9卷 1-11页

作者： Barton, G. Abbott, J. Chiba, N. Huang, D. W. Huang, Y. Krznaric, M. Mack-Smith, J. Saleem, A. Sherman, B. T. Tiwari, B. Tomlinson, C. Aitman, T. Darlington, J. Game, L. Sternberg, M. J. E. Butcher, S. A. Univ London Imperial Coll Sci Technol & Med Fac Nat Sci Ctr Bioinformat Div Mol Biosci London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med London E Sci Ctr Dept Comp Fac Engn London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med MRC Ctr Clin Sci London W12 0NN England NCI Lab Immunopathogenesis & Bioinformat Clin Serv Program SAIC Frederick Inc Ft Detrick MD 21702 USA

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

关键词： Server Side microarray data analysis Exon Array Single User Interface Grid Cluster

来源：评论

学校读者我要写书评

暂无评论

WebArray: an online platform for microarray data analysis

引用

BMC BIOINFORMATICS 2005年第1期6卷 306-306页

作者： Xia, XQ McClelland, M Wang, YP Sidney Kimmel Canc Ctr Dept Canc Genet San Diego CA 92121 USA Sidney Kimmel Canc Ctr Genom Core Facil San Diego CA 92121 USA

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 http://***/webarray/. It runs on a Linux server with Apache and MySQL.

关键词： False Discovery Rate microarray data analysis Affy Package Loess Curve Free Open Source Software

来源：评论

学校读者我要写书评

暂无评论

SED, a normalization free method for DNA microarray data analysis

引用

BMC BIOINFORMATICS 2004年第1期5卷 121-121页

作者： Wang, HJ Huang, H Oscient Pharmaceut Corp Waltham MA 02453 USA

Background: analysis of DNA microarray data usually begins with a normalization step where intensities of different arrays are adjusted to the same scale so that the intensity levels from different arrays can be compared with one other. Both simple total array intensity-based as well as more complex "local intensity level" dependent normalization methods have been developed, some of which are widely used. Much less developed methods for microarray data analysis include those that bypass the normalization step and therefore yield results that are not confounded by potential normalization errors. Results: Instead of focusing on the raw intensity levels, we developed a new method for microarray data analysis that maps each gene's expression intensity level to a high dimensional space of SEDs ( Signs of Expression Difference), the signs of the expression intensity difference between a given gene and every other gene on the array. Since SED are unchanged under any monotonic transformation of intensity levels, the SED based method is normalization free. When tested on a multi-class tumor classification problem, simple Naive Bayes and Nearest Neighbor methods using the SED approach gave results comparable with normalized intensity-based algorithms. Furthermore, a high percentage of classifiers based on a single gene's SED gave good classification results, suggesting that SED does capture essential information from the intensity levels. Conclusion: The results of testing this new method on multi-class tumor classification problems suggests that the SED-based, normalization-free method of microarray data analysis is feasible and promising.

关键词： Feature Selection Intensity Level Chip Format Near Neighbor microarray data analysis

来源：评论

学校读者我要写书评

暂无评论

Assessing stability of gene selection in microarray data analysis

引用

BMC BIOINFORMATICS 2006年第1期7卷 50-50页

作者： Qiu, X Xiao, YH Gordon, A Yakovlev, A Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA

Background: The number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data. Results: Using resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjusted p-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations. Conclusion: Resampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.

关键词： False Discovery Rate Gene Selection Bonferroni Adjustment microarray data analysis Multiple Testing Procedure

来源：评论

学校读者我要写书评

暂无评论

The miracle of microarray data analysis

引用

Genome biology 2001年第9期2卷 REPORTS4021页

作者： Y F Leung D S Lam C P Pang Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong 147K Argyle Street Kowloon Hong Kong. cppang@cuhk.edu.hk

A report on the tenth Annual Bioinformatics and Genome Research meeting of the Cambridge Healthtech Institute's Beyond Genome 2001 series, San Francisco, USA, 17-19 June 2001.

关键词： microarray data analysis Tiling Array Exon Array Unsupervised Learning Method Automate data analysis

来源：评论

学校读者我要写书评

暂无评论

Development of a novel microarray data analysis tool without normalization for genotyping degraded forensic DNA

引用

FORENSIC SCIENCE INTERNATIONAL-GENETICS 2023年第1期65卷 102885-102885页

作者： Yagasaki, Kayoko Nishida, Nao Mabuchi, Akihiko Tokunaga, Katsushi Fujimoto, Akihiro Univ Tokyo Grad Sch Med Dept Human Genet 73-1 Hongo Tokyo 1130033 Japan Natl Ctr Global Hlth & Med Genome Med Sci Project 1-21-1 Toyama Tokyo 1628655 Japan Natl Ctr Global Hlth & Med Genome Med Sci Project 1-7-1 Kohnodai Ichikawa Chiba 2728516 Japan Tokyo Metropolitan Police Dept Forens Sci Lab 3-35-21 Shakujiidai Tokyo 1770045 Japan

Since the arrest of the Golden State Killer in the US in April 2018, forensic geneticists have been increasingly interested in the investigative genetic genealogy (IGG) method. While this method has already been in practical use as a powerful tool for criminal investigation, we have yet to know well the limitations and potential risks. In this current study, we performed an evaluation study focusing on degraded DNA using the Affymetrix GenomeWide Human SNP Array 6.0 platform (Thermo Fisher Scientific). We revealed one of the potential problems that occur during SNP genotype determination using a microarray-based platform. Our analysis results indicated that the SNP profiles derived from degraded DNA contained many false heterozygous SNPs. In addition, it was confirmed that the total amount of probe signal intensity on microarray chips derived from degraded DNA decreased significantly. Because the conventional analysis algorithm performs normalization during genotype determination, we concluded that noise signals could be genotype-called. To address this issue, we proposed a novel microarray data analysis method without normalization (nMAP). Although the nMAP algorithm resulted in a low call rate, it substantially improved genotyping accuracy. Finally, we confirmed the usefulness of the nMAP algorithm for kinship inferences. These findings and the nMAP algorithm will make a contribution to the advance of the IGG method.

关键词： Investigative genetic genealogy Kinship analysis microarray data analysis Degraded DNA KING software Normalization

来源：评论

学校读者我要写书评

暂无评论

Identification of Adriamycin Resistance Genes in Breast Cancer based on microarray data analysis

Identification of Adriamycin Resistance Genes in Breast Canc...

引用

2022CCTB中国肿瘤标志物学术大会暨中国整合肿瘤学大会暨第十六届肿瘤标志物青年科学家论坛暨中国肿瘤标志物产业创新大会

作者： Zhaolei Cui Yan Chen Fujian Cancer Hospital

Objective:Breast cancer is a common malignant tumor with increasing incidence *** study aimed to investigate the molecular mechanisms of the adriamycin(ADR)resistance in breast ***:The GSE76540 dataset downloaded from the National Center for Biotechnology Information(NCBI) Gene Expression Omnibus(GEO) database was adopted for *** expressed genes(DEGs) in chemo-sensitive cases and chemo-resistant cases were identified using the GEO2R online tool *** analysis and KEGG enrichment analysis of DEGs were carried out by using the DAVID online *** protein-protein interaction(PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes(STRING) and visualized with Cytoscape *** impact of key tumor genes on the survival and prognosis were ***:A total of 1481 DEGs were excavated,including 549 up-regulated genes and 932down-regulated *** to the GO analysis,the DEGs were significantly enriched in:extracellular matrix organization,positive regulation of transcription from RNA polymerase II promoter,lung development,positive regulation of gene expression,axon quidance and so *** results of KEGG pathway enrichment analysis showed that the most enriched DEGs can be detected in:pathways in cancer,PI3K AKT signaling pathway,focal adhesion,ras signaling pathway and so *** the PPI network analysis,hub genes of CDH1,ESR1,SOX2,AR,GATA3,FOXA1,KRT19,CLDN7,AGR2,ESRP1,RAB25,CLDN4,IGF1R,CLDN3 and IRS1 were ***,there is a correlation filter out these hub genes in resistance of ***:Hub genes associated with ADR resistance were identified using bioinformatic *** results of this study may contribute to the development of targeted therapy for breast cancer.

关键词： breast cancer microarray data analysis adriamycin resistance

来源：评论

学校读者我要写书评

暂无评论

A trend pattern assessment approach to microarray gene expression profiling data analysis

引用

PATTERN RECOGNITION LETTERS 2007年第12期28卷 1472-1482页

作者： Cao, Kajia Zhu, Qiuming Iqbal, Javeed Chan, John W. C. Univ Nebraska Dept Comp Sci Omaha NE 68182 USA Univ Nebraska Med Ctr Dept Pathol & Microbiol Omaha NE 68105 USA

We study the problem of how to assess the reliability of a statistical measurement on data set containing unknown quantity of noises, inconsistencies, and outliers. A practical approach that analyzes the dynamical patterns (trends) of the statistical measurements through a sequential extreme-boundary-points (EBP) weed-out process is explored. We categorize the weed-out trend patterns (WOTP) and examine their relation to the reliability of the measurement. The approach is applied to the processes of extracting genes that are predictive to BCL2 translocations and to clinical survival outcomes of diffuse large B-cell lymphoma (DLBCL) from DNA microarray gene expression profiling data sets. Fisher's Discriminate Criterion (FDC) is used as a statistical measurement in the processes. It is found that the weed-out trend analysis (WOTA) approach is effective for qualitatively assessing the statistics-based measurements in the experimentations conducted. (c) 2007 Elsevier B.V. All rights reserved.

关键词： gene expression profiling microarray data analysis boundary points dynamical patterns trend evaluations fisher's discriminate criterion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：