Bioinformatics for gene expression analysis

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Bioinformatics for gene expression analysis

Heather Vincent
New developments in the high-throughput methods for gene expression
analysis bring new challenges in data analysis and management.  Error
models for microarray data have been well studied, but more work is
still needed on appropriate methods for short-read sequence data.

Moving beyond the pre-processing steps, biological understanding
requires the integration of expression data with other types of data,
such as GO categories and KEGG pathways.

The University of Manchester's distance course in microarray data
which runs again in March, provides practical experience in both the
pre-processing and in data integration stages in the analysis.
Participants will study microarray data in depth, and will also be
introduced to the most recent methods for transcriptome analysis.

For those interested in learning about data integration in more depth,
the microarray course is designed to link to our sister course in
network analysis, Bioinformatics for Systems Biology
Bioinformatics for Systems Biology will run again in October 2011.

There are links to further information on all of our courses here :  If you
have any questions, or would like to book a place on a course, please
contact our office for Advanced Professional Education ([hidden email]).

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