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Gene Expression and Microarrays

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These are a collection of Gene Expression and Microarrays links.

[info] Gene Chips (DNA Microarrays)
[info] DNA Microarrays
[info] Large-Scale Gene Expression and Microarray Links and Resources
[info] MicroArray related activities at the EBI
[info] Listing of DNA microarray links
[info] Vivian Cheung's group
[info] Microarrays.Org
[info] GRID IT: Resources for Microarray Technology
[info] DNA Microarrays
[info] Microarray & Data Analysis


[info] Gene Chip Microarray Protocol Websites
[info] Molecular Oncology and Development

Gene Expression Database Tools

[info] NCGR Gene Expression Databases, Analytical Tools
[info] Software for MicroArray Gene Expression Analysis
[info] Microarray Software
[info] microarrays.org Software
[info] GenEx
[info] Microarray group at the University of Manchester

Other Data mining Tools

[info] Gene Network Inference from Large-Scale Gene Expression Data
[info] Knowledge-based Analysis of Microarray Gene Expression Data Using Support Vector Machines
[info] Data Mining: Making Sense of Gene Expression Data

List of Public Gene Expression Databases

[info] Worm Chip Directory
[info] Stanford Microarray Database (SMD)
[info] ExpressDB
[info] EPODB
[info] The Gene Expression Database (GXD)
[info] Gene Expression Omnibus (GEO)
[info] The microarray project (uAP)
[info] ArrayDB
[info] uArray Database (mAdb)
[info] ChipDB: A Genome Expression Monitoring Database System
[info] The ArrayExpress Database
[info] RNA Abundance Database (RAD)
[info] GeneX: a Collaborative Internet Database and Toolset for Gene Expression Data
[info] Microarray analysis tool(MAT) software

Commercial Database Tools

[info] GeneChipLIMS
[info] LifeArray Software
[info] Spotfire Array Explorer
[info] GenomXtools
[info] Reslover
[info] Genetic Analysis Technology Consortium (GATC)

Data formats

[info] Gene Expression Markup Language (GEML)

Detailed information on the above options

Gene Chips (DNA Microarrays)
Here are the basics on DNA microarray technology and a list of academic and industrial links related to this exciting new technology.

DNA Microarrays
Here are some interesting web sites and papers which will introduce you to one of the more exciting technological beakthroughs in genomics -- DNA microarrays. This site is far from a comprehensive listing, but it may be a good place to start.

Large-Scale Gene Expression and Microarray Links and Resources
This site is a collection of web resources and pointers to information on large-scale gene expression studies, and especially microarray technologies. The links included are biased towards the development and applications of informatics and bioinformatics for these technologies.

MicroArray related activities at the EBI
These pages are a jumping point to activities at the European Bioinformatics Institute that focus on microarrays, the gene expression data that results from them, the issues revolving around the immense influx of this data, and of course the analysis that one would like to perform on it.

Listing of DNA microarray links

Vivian Cheung's group
The main focus of our lab is the development of Direct Identical-by-Descent (IBD) Mapping. Direct IBD Mapping is a DNA microarray-based mapping technique that allows isolation and mapping of DNA fragments shared IBD between individuals. It unites two methods, genomic mismatch scanning (GMS) and DNA microarray technology. GMS allows physical isolation of IBD DNA fragments between two individuals. If two individuals have inherited the disease gene(s) from a common ancestor then the gene(s) should reside within the IBD regions shared between them. Once isolated the genomic location of the DNA fragments can be mapped by hybridization onto a DNA microarray that contains mapped clones arranged in physical map order. By comparing the IBD maps of sets of affected individuals, we can narrow the candidate gene region(s).

Welcome to microarrays.org, a new public source for microarraying information, tools, and protocols.

GRID IT: Resources for Microarray Technology

DNA Microarrays

Microarray & Data Analysis
This is a collection of papers with emphasis on analysis of microarray (a.k.a. DNA chip) data. General reviews on microarry are occasionally included. Analysis of gene network is also occasionally included. However, papers on microarray technology itself are not usually in. Papers emphasizing results of such analysis are also not in.

Gene Chip Microarray Protocol Websites
Lists of protocols.

Molecular Oncology and Development
Lists of protocols.

NCGR Gene Expression Databases, Analytical Tools
In the table are some of the tools, databases and other resources that we have reviewed over the last few months.

Software for MicroArray Gene Expression Analysis
I have tested several PC based software packages for the analysis of microarray data. All packages were demonstration versions with limited file load/save/print capability and/or a time-limited license, except ScanAlyze, Cluster and TreeView from Stanford University, which are freeware. In some cases the lack of saving or printing out the results made side by side comparison impracticable. Nevertheless the demos allow an accurate assessment of the capabilities of each package.

Microarray Software

microarrays.org Software

GenEx TM is a database that allows scientists working with any organism to internally distribute and visualize gene expression data from micro-arrays, Affymetrix chips, and related technologies. GenEx TM allows researchers to publish both text and image files on the web in a common format that is viewable by other scientists via any web browser. Publishing is as easy as a right click from within GeneSpring TM. GeneSpring TM users can also download any experiments published on GenEx TM to their local desktop in order to leverage the knowledge from these experiments in combination with there own. After an experiment has been published, anyone with the correct URL and a web browser can view the published experiments.

Microarray group at the University of Manchester
Software support for microarray expression analysis.

Gene Network Inference from Large-Scale Gene Expression Data
With the advent of the"Age of Genomics" an entirely new class of data is emerging. Can we really expect to construct a detailed biochemical model of, say, an entire yeast cell with some 6000 genes (only about 1000 of which were defined before sequencing started, and about 50% of which are clearly related to other known genes), by analyzing each gene and determining all the binding and reaction constants one by one? Likewise, from the perspective of drug target identification for human disease, we cannot realistically hope to characterize all the relevant molecular interactions one-by-one as a requirement for building a predictive disease model.

There is a need for methods that can handle this data in a global fashion, and that can analyze such large systems at some intermediate level, without going all the way down to the exact biochemical reactions. At the very least, such an analysis could help guide the traditional pharmacological and biochemical approaches towards those genes most worthy of attention among the thousands of newly discovered genes. Ideally, a sufficiently predictive and explanatory model at an intermediate level could obviate the need for an exact understanding of the system at the biochemical level.

Knowledge-based Analysis of Microarray Gene Expression Data Using Support Vector Machines
We introduce a new method of functionally classifying genes using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines. SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods such as hierarchical clustering methods and self organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Data Mining: Making Sense of Gene Expression Data

Worm Chip Directory
The full genome chips have been printed and are now being used for experiments. These microarrays contain a spot for each gene, plus some control spots.

Stanford Microarray Database (SMD)
SMD stores raw and normalized data from microarray experiments, as well as their corresponding image files. In addition, SMD provides interfaces for data retrieval, analysis and visualization.

ExpressDB is a relational database containing yeast RNA expression data. As of July, 1999 it contains 17.5 million pieces of information loaded from 11 published and in-house expression studies. A manuscript describing the database and the process of managing and analyzing expression data has been submitted for publication.

EpoDB (Erythropoiesis database) is a database of genes that relate to vertebrate red blood cells. It includes DNA sequence, structural features, protein information, gene expression information and transcription factor binding sites.

The Gene Expression Database (GXD)
GXD integrates the many types of expression data and provides links to other relevant resources to place the data into the larger biological and analytical context. The time and space of gene expression is described by a controlled Dictionary of Anatomical Terms that is part of the Anatomy Database. For in situ expression assays, the textual annotations in GXD are complemented by 2 images of original expression data that are indexed via the terms from the dictionary.

Gene Expression Omnibus (GEO)
In order to support the public use and dissemination of gene expression data, NCBI has launched the Gene Expression Omnibus. GEO is our effort to build a gene expression data repository and online resource for the retrieval of gene expression data from any organism or artificial source. Gene expression data from multiple platforms, including spotted microarray (microarray), high-density oligonucleotide array (HDA), hybridization filter (filter) and serial analysis of gene expression (SAGE) data, will be accepted, accessioned, and archived as a public data set. A series of precomputed definitions and descriptions of the data, as well as online tools for the interactive retrieval and analysis of this expression data will follow shortly thereafter. It is anticipated that this repository and resource will become operational and ready for general submissions in Spring 2000.

The microarray project (uAP)
The Microarray Project is a collaborative research effort between numerous intramural scientists in multiple Institutes and Divisions of the National Institutes of Health (NIH), including the National Human Genome Research Institute (NHGRI), National Center for Biotechnology Information (NCBI), National Cancer Institute (NCI), National Institute of Neurological Disorders and Stroke (NINDS), Biomedical Engineering and Instrumentation Program (BEIP), Division of Computer Research and Technology (DCRT) and many others.

ArrayDB 2.1.03 is available in a BETA VERSION.

uArray Database (mAdb)
NCI/DCS uArray Center mAdb Gateway.

ChipDB: A Genome Expression Monitoring Database System
chipDB is a genome expression monitoring database system designed to allow members of the Young Lab and the yeast research community to analyze data produced by high-throughput expression monitoring technologies such as Affymetrix gene chips.

The ArrayExpress Database
The EBI has discussed the possibility of establishing a public repository for DNA microarray based gene expression data with many of the major laboratories developing and using these technologies in Europe and the USA.

Following these discussions, the European Bioinformatics Institute is committed to establishing a public repository for microarray based gene expression data, named ArrayExpress. Currently the EBI is establishing a pilot database containing microarray gene expression data that are available publicly.

RNA Abundance Database (RAD)
Slides for a talk explaining RAD.

GeneX: a Collaborative Internet Database and Toolset for Gene Expression Data
The National Center for Genome Resources and the Computational Genomics Group at the University of California, Irvine are participating in the GeneX project to provide an Internet-available repository of gene expression data with an integrated toolset that will enable researchers to analyze their data and compare them with other such data. The corpus of such data will allow more confidence to be placed on the conclusions reached in this analysis, as well as sharing the considerable cost of generating these datasets.

Microarray analysis tool(MAT) software
A Microarray data management and analysis software has developed in AECOM. The following is the data analysis flow chart.

GeneChip LIMS is a data handling, management, storage, and analysis package for users who are generating large quantities of GeneChip probe array data and require a data management solution. This client-server solution can be integrated into an existing network to provide multiple end users access to the system. Data is stored in a GATC Since we keep standard database that can be accessed using the GeneChip Data Mining Tool or third party vendor mining tools that are GATC-compliant.

LifeArray Software
LifeArray software is an enterprise-wide system that uses a relational database to store expression data in a central repository. Powerful query and viewer tools enable individual researchers to perform a variety of analyses to more quickly identify relevant gene expression patterns from millions of data points.

Spotfire Array Explorer
Spotfire Array Explorer enables Spotfire Pro users to access expression data from GATC-compliant databases and to explore and analyze this complex data in an interactive graphical manner to identify trends, relationships, anomalies and outliers. Spotfire Pro is a desktop application that uses the web and a highly interactive graphical environment to accelerate the discovery process by allowing researchers to access, analyze and explore complex multi-dimensional data from disparate sources.

GenomXtools Basic Sequence Analysis is a set of Visual Genomics applications for mining bioinformatic data and generating BSML output. Query Manager analyzes complex databases and creates BSML content for the visualization of query results. GenomXtools are currently the only applications available for creating BSML documents as data analysis products. These tools complement our BSML document authoring and editing applications.

The Resolver system is a turnkey solution for storing,retrieving, and analyzing large quantities of geneexpression data generated using cDNA microarrays,oligonucleotide arrays, and other technologies.

Genetic Analysis Technology Consortium (GATC)
Molecular Dynamics and Affymetrix have formed the Genetic Analysis Technology Consortium (GATC) in an attempt to standardize the rapidly growing field of array-based genetic analysis, paving the way for the more affordable and productive development of therapeutic, diagnostic and disease management products. The consortium was created to provide a unified technology platform to design, process, read and analyze DNA-arrays. As a result of the GATC, researchers will benefit as probe arrays, readers, reagents, and software and database architectures that are GATC-compliant will be compatible, eliminating the need for redundant equipment and software.

Gene Expression Markup Language (GEML)
An open-standard XML format for DNA microarray and gene expression data.

The Gene Expression Markup Language (GEML) is a file format for storing DNA microarray and gene expression data. GEML is an open-standard XML format which enables exchange of data between gene expression databases and analysis systems. GEML stores which data collection methodology was used, without making assumptions about the meaning of a measurement. This enables possible normalization, integration, and comparison of data across methodologies. GEML handles expression profile data and allows scan images and chip layouts (or"patterns") to be easily referenced and tracked. GEML is independent of any particular database schema.

Any Comments, Questions? Support@hgmp.mrc.ac.uk