The Stanford Microarray Database: data access and quality assessment tools

The Stanford Microarray Database (SMD; http://genome-www.stanford.edu/microarray/) serves as a microarray research database for Stanford investigators and their collaborators. In addition, SMD functions as a resource for the entire scientific community, by making freely available all of its source code and providing full public access to data published by SMD users, along with many tools to explore and analyze those data. SMD currently provides public access to data from 3500 microarrays, including data from 85 publications, and this total is increasing rapidly. In this article, we describe some of SMD's newer tools for accessing public data, assessing data quality and for data analysis.     

The MUSC DNA Microarray Database

SUMMARY: The Medical University of South Carolina (MUSC) DNA Microarray Database is a web-accessible archive of DNA microarray data. The database was developed using the DNA microarray project/data management system, micro ArrayDB. Annotations for each DNA microarray project and associated cRNA target information are stored in a MySQL relational database and linked to array hybridization data (raw and normalized). At the discretion of investigators, data are placed into the public domain where they can be interrogated and downloaded through a web browser. In addition to serving as an online resource of gene expression data, the MUSC DNA Microarray Database is a model for other academic DNA microarray data repositories. AVAILABILITY: Browsing and downloading of MUSC DNA Microarray Database information can be done after registration at http://proteogenomics.musc.edu/pss/home.php.     

The Longhorn Array Database (LAD): An Open-Source,MIAME compliant implementation of the Stanford Microarray Database (SMD)

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.     

基因表达谱微阵列网络数据库在肿瘤研究中的应用

基因表达谱微阵列数据库是一类可提供存储、查询、下载分析的在线网络数据库,在肿瘤相关领域的研究中提供了大量的数据来源。由于微阵列分析对于无生物/医学信息学专业背景的研究人员仍然有较多困难,致使该数据库的使用尚未普及。本文从数据查询、下载分析和使用方法等方面对常用基因表达谱微阵列数据库进行概述,并对现阶段基因表达微阵列数据库的应用策略进行总结,旨在帮助该领域研究的初学工作者了解数据库的基本知识并推动其在科研工作中的应用。     

酵母全基因组芯片的应用

酵母作为最常用的模式生物,其全基因组测序最先完成。利用已知的酵母基因组信息,结合基因芯片技术,可进一步系统研究酵母的功能基因组表达。基因芯片技术是上世纪末发展起来的一项集分子生物学、生物信息学和电子学等科目的生物高新技术。酵母全基因组芯片,可以用以从基因表达水平,研究环境、物理、化学因子、毒理和药物作用的机制,在最终阐明酵母基因组功能的同时,为生物学研究提供更优化的模式生物模型。     

The MetaCyc Database

MetaCyc is a metabolic-pathway database that describes 445 pathways and 1115 enzymes occurring in 158 organisms. MetaCyc is a review-level database in that a given entry in MetaCyc often integrates information from multiple literature sources. The pathways in MetaCyc were determined experimentally, and are labeled with the species in which they are known to occur based on literature references examined to date. MetaCyc contains extensive commentary and literature citations. Applications of MetaCyc include pathway analysis of genomes, metabolic engineering and biochemistry education. MetaCyc is queried using the Pathway Tools graphical user interface, which provides a wide variety of query operations and visualization tools. MetaCyc is available via the World Wide Web at http://ecocyc.org/ecocyc/metacyc.html, and is available for local installation as a binary program for the PC and the Sun workstation, and as a set of flatfiles. Contact metacyc-info@ai.sri.com for information on obtaining a local copy of MetaCyc.     

Split-Plot Microarray Experiments

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.     

微阵列生物芯片技术

综述了微阵列生物芯片的制备方法、原理以及应用,并讨论了今后的发展趋势。     

Microarray Data Analysis

The analysis of differential gene expression in microarray experiments requires the development of adequate statistical tools. This article describes a simple statistical method for detecting differential expression between two conditions with a low number of replicates. When comparing two group means using a traditional t-test, gene-specific variance estimates are unstable and can lead to wrong conclusions. We construct a likelihood ratio test while modelling these variances hierarchically across all genes, and express it as a t-test statistic. By borrowing information across genes we can take advantage of their large numbers, and still yield a gene-specific test statistic. We show that this hierarchical t-test is more powerful than its traditional version and generates less false positives in a simulation study, especially with small sample sizes. This approach can be extended to cases where there are more than two groups.     

Microarray data assembler

SUMMARY: Large volumes of microarray data are generated and deposited in public databases. Most of this data is in the form of tab-delimited text files or Excel spreadsheets. Combining data from several of these files to reanalyze these data sets is time consuming. Microarray Data Assembler is specifically designed to simplify this task. The program can list files and data sources, convert selected text files into Excel files and assemble data across multiple Excel worksheets and workbooks. This program thus makes data assembling easy, saves time and helps avoid manual error. AVAILABILITY: The program is freely available for non-profit use, via email request from the author, after signing a Material Transfer Agreement with Johns Hopkins University.     

芯片数据标准化方法比较研究

在基因芯片实验中,基因表达水平之间的相关性在推断基因间相互关系时起到非常重要的作用.未经标准化处理的芯片数据基因之间往往都呈现出很强的相关性,这些高相关性一部分是由基因表达水平变化引起的,而另外一部分是由系统偏差引起的.对芯片数据进行标准化处理的目的之一是消除系统偏差引起的高相关性,同时保留由真正生物学原因引起的基因表达水平高相关性.虽然目前对标准化方法已经有了不少比较研究,但还较少有人研究标准化方法对基因之间相关系数的影响,以及哪种方法最有利于恢复基因之间的相关性结构.通过对基因表达水平数据的模拟,具体比较了几种常用标准化方法的效果,从而给出最有利于恢复基因之间相关性结构的那种标准化方法.     

微阵列技术在植物学上的应用进展

微阵列技术(又称生物芯片技术)是在国际上近几年才发展起来的高新技术。高效、快速、高通量的微阵列技术已获得了飞速的发展,并被广泛地应用于生命科学的研究之中。本文主要讨论了它在植物生物学中的应用。例如植物DNA测序、基因表达分析、检测基因突变和多态性位点等。