Microarray data analysis using GeneSifter Lab Edition

GeneSifter integrates powerful data management tools with analysis options that enable scientists to determine both the statistical and biological significance of microarray data, all in a convenient Web-based system. Support for common platforms (Affymetrix, Agilent, CodeLink, Illumina, etc.), as well as custom arrays, makes web-based GeneSifter the most effective and accessible microarray data analysis system available today.

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    Key Features

     
    With the One Click Gene SummaryTM, information from more than a dozen annotation sources can be used to identify targets for further study or to prioritize individual genes based on biological function.
     
    GeneSifter provides powerful statistical and analytical algorithms (RMA, PAM, ANOVA, CLARA, Benjamini and Hochberg, etc.) through an intuitive web interface. GeneSifter users have easy access to comparison statistics for identifying differentially expressed genes and cluster analysis for identifying patterns of gene expression and segregating genes based on these patterns.
     
    Ontology, pathway and z-score reports automatically summarize the biological significance of a gene list. By examining the gene list as a whole, GeneSifter users can identify broad biological themes of importance.
     
    Concurrent usage and collaboration:
    Web-based GeneSifter allows you to view and share your results with a colleague located down the corridor or on another continent.
     
    Comprehensive compatibility with Illumina® and Affymetrix® GeneChip® Arrays:
    Full support for native Affymetrix data files, easy access to RMA and GC-RMA for probe level analysis, GeneChip Array specific analysis features, training and support resources make GeneSifter the fastest route from raw data to biological results for users of Affymetrix® GeneChip® Arrays.
     
     
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    Add-on service:

    Affymetrix and CodeLink users of GeneSifter Lab Edition have an additional service available to them: GeneSifter Jumpstart includes initial data upload, quality assessment, early statistical and biological analysis, and preparation of the data for publication.