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Tomlinson: Stem Cell Bioinformatics

We are interested in using bioinformatics techniques to gain understanding of key molecular features of stem cell biology.

Modern high-throughput techniques such as microarray expression profiling generate huge amounts of data. In order to interpret this data it is essential to apply appropriate data storage, analysis and visualization techniques. Software resources must also be available that allow the framing of biological questions in the context of the wealth of available data.

Working within the EU project Eurostemcell we developed a database designed to store and share the data outputs from this large consortium. This database, StemDB now forms a key component of the data management strategy for the EU FP7 project Eurosystem.

We are interested in the application of integrative analysis approaches in order to gain understanding of complex data sets such as genome scale expression data from stem cell populations. Non-trivial understanding of profiling data from these cell populations is especially chalenging given the heterogeniety of these cells, the capacity for spontaneous commitment and differentiation and the ability of the cells to adapt to their culture environment.

We have refined data analysis approaches that support the identification of complex patterns across the huge body of available data. We use these integrative approaches to dissect stem cell transcriptional profiles in order to identify key regulatory components within a large body of data. We are applying these methods to dissect the key molecular genes involved in self-renewal and differentiation of mouse and human embryonic stem cells.