Developed at the Broad Institute of MIT and Harvard

Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, and Mesirov JP. GenePattern 2.0. Nature Genetics 38 no. 5

(2006): pp500-501

GenePattern is supported by funding from the NIH

Today…

• Introduction to GenePattern– Why

– What

– How

• Demonstration

• Summary

Challenges

• Modern research methods follow a more integrative approach

• Tools are not available to biomedical researchers

• Tools are difficult to use

• Results difficult to interpret correctly

Purpose

• Create tools that are easily accessible to biomedical researchers

• Allows for a combination of multiple data sources and methods

• Allows for “reproducible research”

GenePattern

1. Offers a repository of analytic and visualization tools: Modules

2. Easy creation of complex methods from these tools: Pipelines

3. The rapid development and dissemination of new methods: Programming Environment

1. Modules

• Point and click

• ~ 60 analysis modules (handout)

• Documentation

• Designed for Affymetrix data

• 14 different file extensions

2. Pipelines

• Golub et al illustrates need

• Records the methods, parameters and data to ensure reproducibility

• Allows methods to be “chained”

• Published or create new

• Easily shared

• Assigns version numbers

3. Programming environment

• Libraries allow transparent access to GenePattern modules from R, Matlab and Java

• Language independent mechanism to add new tools to the module repository

• Tools can be your own or public (e.g. from Bioconductor)

Functional Architecture

Taken from Reich et al Nature Genetics 2006

Components

1. The GenePattern server

2. The Java Client

3. The Web Client

Software Architecture

Reich et al Nature Genetics 2006

GenePattern• Current version

– Release: 2.0.1, Release date 3/2/2006

• OS compatibility:

– Windows: XP, 2000, 2003

– Mac: OS X 1.3.9 or later

– Unix: Linux, Solaris, Tru64

• Hardware requirements:

– 256MB RAM

– 500MB disk space

Demonstration

http://www.broad.mit.edu/cancer/software/genepattern/

Gene Expression Analysis

• Four broad categories1. Differential analysis/Marker selection

2. Prediction

3. Class discovery

4. Pathway analysis

• Data Formats

• Annotations

Proteomics

• SELDI, MALDI and LC-MS in mzXML format

• Quality assessment

• Peak detection

• Spectra comparison

• Proteomic analysis pipeline

• Data conversion

SNP analysis

• In alpha testing

• Uses high-density SNP microarray data

• Copy number alterations

• Loss of heterozygosity (LOH) detection

Data preprocessing and conversion

• Importing, exporting and file conversion

• Normalization, filtering and imputing

• ID conversion and annotation

• Row and column extraction, transpose, reorder and split data

Comparison of Selected Microarray Analysis Software Platforms

Reich et al Nature Genetics 2006

Summary

• Has a few minor problems

• Is it something MIBLab can use?– Who is user?

– What is it missing? Should be easily added

SourcesGould J, Getz G, Monti S, Reich M, Mesirov JP. Comparative Gene

Marker Selection suite. Bioinformatics. 2006 May 18;

Liefeld T, Reich M, Gould J, Zhang P, Tamayo P, Mesirov JP. GeneCruiser: a web service for the annotation of microarray data. Bioinformatics. 2005 Sep 15;21(18):3681-2.

Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nature Genetics 2006 May;38(5):500-1.