In This Issue
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Announcements
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New Modules
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Training and Events
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Talk to Us
Preview release of GenePattern 2.0 Available
The GenePattern team has made available a beta release of GenePattern 2.0, and we would like your feedback on its new features. These include:
- Integrated Pipeline and Graphical Environment: You can now create, view and edit pipelines all directly in the easy-to-use Java Client. Click on the Pipeline->New menu to get started building a pipeline in the Graphical Client.
- Automatic Pipeline Creation: Right-click on a result file and select "Create Pipeline" to create a pipeline containing the sequence of analyses that resulted in the creation of that file.
- Web Client Redesign: The layout of the Web Client has been improved to make more frequent operations easier and to give easier access to previous analyses and result files.
- Module Suites: There is now another way that modules can be grouped together in addition to pipelines. Module suites are packages of modules that have similar functionality, such as clustering or proteomics - so you can now, for example, install all modules in the "clustering" suite at the same time, or view only those modules in the "proteomics" suite if you want to limit the list of modules only to that type. This functionality will be available shortly when new suites are posted to the GenePattern module repository.
The GenePattern 2.0 installer is available at http://www.genepattern.org/download/beta/. We welcome any feedback you may have as we prepare to release the final GenePattern 2.0. You can send your questions or comments to gp-help@broad.mit.edu.
GenePattern Analyzes Proteomics
We are proud to announce the release of a suite of modules that make GenePattern a platform for the analysis of mass spectrometry (MS) based proteomic data. These modules support the analysis of SELDI (surface enhanced laser desorption/ionization), MALDI (matrix assisted laser desorption/ionization), and LC/MS* (liquid chromatography-mass spectrometry) MS data. The analysis suite includes modules that process and visualize individual spectra, perform spectral quality assessment, normalization, peak detection using digital filters, and peak matching to identify the same peaks across different spectra. There is also a module that combines all of these functions into a single seamless analysis. Data can be in csv or in standard mzXML format.
These proteomic functions can be easily integrated into GenePattern's other analytical modules. By representing peaks as features, these modules create a file that is similar to an expression dataset and can be used in a variety of pattern recognition analyses including clustering, classification, and differential marker selection using various other GenePattern modules.
* The GenePattern team thanks the Fred Hutchinson Cancer Center for their contribution of the LC/MS analysis module.
Several new GenePattern modules are available on the GenePattern module repository.
You can view comprehensive documentation on our
module page
and can
install these modules
by clicking on Install/Update in the Admin frame of your local GenePattern server.
Proteomics modules
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AreaChange
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Calculates fraction of area under the spectrum that is attributable to signal (area after noise removal / original area).
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CompareSpectra
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Compares two spectra to determine similarity.
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Peaks
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Determine peaks in the spectrum using a series of digital filters.
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LocatePeaks
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Locates detected peaks in a spectrum.
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PlotPeaks
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Plots the original spectrum and filtered (peak detected) spectrum.
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ProteoArray
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LC-MS proteomic data processing module.
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ProteomicsAnalysis
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Runs the proteomics analysis on the set of input spectra.
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MAGEMLImportViewer
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Imports data in MAGE-ML format into GenePattern. MAGE-ML is a standard for the representation of microarray expression data. Files containing MAGE-ML data can be downloaded from ArrayExpress (http://www.ebi.ac.uk/arrayexpress/).
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ExpressionFileCreator
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Creates an expression dataset from a set of individual Affymetrix CEL files. The conversion is done using either the standard Affymetrix probe modeling algorithm MAS5 or the RMA algorithm.
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NMFConsensus
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NMF (Non-negative Matrix Factorization) is an unsupervised learning algorithm which has been shown to identify molecular patterns when applied to gene expression data. Rather than separating gene clusters based on distance computation, NMF detects context-dependent patterns of gene expression in complex biological systems. The matrices can be analyzed visually and used to compute statistics to help select a number of factors that provide stable clustering solutions and correspond to the discovered subclasses.
This module supports the methodology described in J-P. Brunet, P. Tamayo, T. Golub, J. Mesirov Metagenes and Molecular Pattern Discovery using Matrix Factorization, PNAS 101, 4164-4169 (2004). GenePattern implements an R-language version of NMF, while the original MATLAB version can be found at http://www.broad.mit.edu/cancer/pub/nmf/.
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GenePattern Training Workshops
The GenePattern workshops have been extremely popular. The next round of workshops are scheduled for the 9th of Novemeber at the TIGR 8th Annual Conference on Computational Genomics.
These workshops teach participants the features of GenePattern, including:
- an intuitive graphical user interface for users at all levels of computational sophistication
- comprehensive repository of clustering, prediction, preprocessing, and visualization modules for analysis of microarray data
- a pipeline environment that allows users to chain tasks together to create and share methodologies
- a task integration environment that allows rapid, code-free integration of new tools
- a programming environment that allows users to access GenePattern modules from the Java, MATLAB, and R programming languages
Watch our web site for updates on future workshops, or
sign up
to be notified immediately.
Please let us know how you're using GenePattern!
User Survey
If you use GenePattern, we would like to know how your experience has been. Our
user survey
is a brief online form that lets you give us feedback about the software and other aspects of using GenePattern. Your responses are greatly appreciated - they will help us to understand how GenePattern is being used and how to make it as valuable a tool as possible.
Publications
If you've published a paper that makes use of GenePattern, we'd love to hear about it. Even if you are just using GenePattern in a novel way, let us know!
Email the GenePattern team
Early Adopters
If you'd like early access to new GenePattern releases to help us test new GenePattern features, please let us know.
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