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Escherichia coli : From cDNA Microarray Raw Data to Pathways and Published Abstracts

Created: 2008-05-08 15:25:29      Last updated: 2008-05-12 09:01:37

This workflow takes in a CDNA raw file and a normalisation method then returns a series of images/graphs which represent the same output obtained using the R and bioconductor. Also retruned by this workflow are a list of the top differentialy expressed genes (size dependant on the number specified as input - geneNumber), which are then used to find the candidate pathways which may be influencing the observed changes in the microarray data. By identifying the candidate pathways, more detailed insights into the gene expression data can be obtained. These pathways are subsequently used to obtain a corpus of published abstracts (from the PubMed database) relating to each biological pathway identified. These pathways are subsequently used to obtain a corpus of published abstracts (from the PubMed database) relating to each biological pathway identified. Also it generates a pie chart which, indicates the number of genes in a dataset that are regulated by a known transcriptional regulator, or by combination of regulators, and can suggest previously unknown regulatory interactions. The information for each regulon comes from files that are created manually from the EcoCyc database.

NOTE - You will also need to install R and Rserv on your machine and install the libaries required by the R script into you R library directory

The example inputs for this workflow are as follows:

geneNumber = the number of differentialy expressed gene to be returned above a given p-value, e.g. 20 path = the direct path to the raw data file location, e.g. C:/Microarray_Data/FILES/ - note the forward slashes p-value = the p-value cut-off value for the array data, e.g. 0.05 foldChange = the fold change value for the microarray data, e.g. 1 (means greater than 1 or less than -1) regulonDir = the direct path to the regulon file.

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