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GenePattern 2.0

Nature Genetics volume 38, pages 500–501 (2006)Cite this article

To the Editor:

Whole-genome expression profiling has created a revolution in the way we study disease and basic biology. Since 1997, the number of published results based on an analysis of gene expression microarray data has grown from 30 to over 5,000 publications per year. Sophisticated mathematical methods have been developed for use in patient diagnosis and prognosis, identification of new drug targets and understanding biological mechanisms. However, these tools are often out of the direct reach of the biomedical researchers who can so critically benefit from them because they can be difficult to understand and use correctly. This challenge is even more relevant in the context of 'integrative' approaches, where a multitude of data sources and methods are combined in the analysis of a single problem.

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Figure 1: GenePattern pipelines.

References

  1. Golub, T.R. et al. Science 286, 531–537 (1999).

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  2. Gentleman, R. Stat. Appl. Genet. Mol. Biol. [online] 4, 2 (2005) <http://www.bepress.com/sagmb/vol4/iss1/art2>.

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Author information

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Michael Reich, Ted Liefeld, Joshua Gould, Jim Lerner, Pablo Tamayo & Jill P Mesirov

Authors
  1. Michael Reich
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  2. Ted Liefeld
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  3. Joshua Gould
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  4. Jim Lerner
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  5. Pablo Tamayo
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  6. Jill P Mesirov
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Supplementary information

Supplementary Figure 1

GenePattern functional architecture. (PDF 553 kb)

Supplementary Figure 2

GenePattern software architecture. (PDF 633 kb)

Supplementary Table 1

Microarray analysis software comparison. (PDF 24 kb)

Supplementary Table 2

Contents of the GenePattern module repository. (PDF 12 kb)

Supplementary Note (PDF 73 kb)

Supplementary Tutorial (PDF 2854 kb)

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Reich, M., Liefeld, T., Gould, J. et al. GenePattern 2.0. Nat Genet 38, 500–501 (2006). https://doi.org/10.1038/ng0506-500

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  • DOI: https://doi.org/10.1038/ng0506-500

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