Technical Staff

Mrs Anne M Owen (part-time)

Position in departmentComputer Support Officer
Telephone01206 872704
Office hoursMonday 1.45 to 5pm Thursday 9am to 1pm

As well as being our Computer Support Officer, Anne is doing a part-time PhD within the Department as of October 2009. 


Memon, F. N., Owen, A. M., Sanchez-Graillet, O., Upton, G. J. G. and Harrison, A. P. (2010) Identifying the impact of G-Quadruplexes on Affymetrix 3' Arrays using Cloud Computing. Journal of Integrative Bioinformatics, 7(2):111, 2010. Online Journal: id=111.


Hugh P Shanahan, Anne M Owen, and Andrew P Harrison. “Bioinformatics on the cloud computing platform Azure.” In: PloS One 9.7 (Jan. 2014). Ed. by Shyamal D. Peddada, e102642. ISSN: 1932-6203. DOI: 10.1371/journal.pone.0102642. URL:

Study areas

Analysis of transcriptomics data using cloud computing methods

SupervisorDr Harrison
Thesis titleWide scale analysis of transcriptomics data using cloud computing methods

This study explores different ways of handling and analyzing big data in the field of bioinformatics. The focus has been on improving the analysis of public domain data for GeneChips which are a widely used technology for measuring gene expression. Methods to determine the bias in gene expression due to G-stacks associated with runs of guanine in probes have been explored via the use of a grid and various types of cloud computing.
An attempt has been made to find the best way of storing and analyzing big data used in bioinformatics. A grid and various types of cloud computing have been employed. The experience gained in using a grid and different clouds has been reported.  In the case of Windows Azure, a public cloud has been employed in a new way to demonstrate the use of the R statistical language for research in bioinformatics.

This work has studied the G-stack bias in a broad range of GeneChip data from public repositories. A wide scale survey has been carried out to determine the extent of the G-stack bias in four different chips across three different species. The study commenced with the human GeneChip HG U133A. A second human GeneChip HG U133 Plus2 was then examined, followed by a plant chip, Arabidopsis thaliana, and then a bacterium chip, Pseudomonas aeruginosa. Comparisons have also been made between the use of widely recognised algorithms RMA and PLIER for the normalization stage of extracting gene expression from GeneChip data.

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