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However, this flexibility comes with the end-user price of requiring more knowledge of the inputs and outputs of various modules.ĭeclan N.C. This loose coupling provides flexibility in designing analytic pipelines and the capacity to add new routines or processing steps as they are identified by the community. This means GenePattern can use data available through caBIG's federation and the GenePattern can be set-up as an analytic service.ĭeveloped by bioinformaticists at the Broad Institute, GenePattern loosely couples it analytic pipeline. GenePattern uses caBIG's caGrid services. It also supports analysis of high-density SNP data for the determination of copy number alterations (amplifications and deletions), and loss of heterozygosity detection.įor bioinformaticians, GenePattern provides APIs that permit the use of analysis modules developed in Java, MATLAB, and R (see preceding sections). GenePattern supports class prediction, class discovery, pathway analysis, and marker selection gene expression analyses. GenePattern's pipelines can be exported and imported, allowing them to be shared or used to document published research. Once created, the pipeline can reproduce the steps used in an analysis or be reapplied to novel data sets. More specifically, its workflow captures the computational analysis methods and their parameters.
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An interesting capability of GenePattern is its ability to capture, automate, and share the steps used to perform the analysis. These pipelines are constructed through a common application interface that allows users to gain access to a repository of analytic tools for various data types. More specifically, the output of one step can be used as the input of another. Users create analysis pipelines that chain together various analytic steps required to perform a desired analysis. GenePattern ( is an analysis workflow tool that supports multidisciplinary genomic research. Buetow, in The Molecular Basis of Cancer (Third Edition), 2008 GenePattern