Centre for Regenerative Medicine

Simon Tomlinson Research Group

Stem cell bioinformatics

The stem cell bioinformatics group uses computational methods to explore the molecular mechanisms underpinning stem cells. To accomplish this we develop and apply advanced analysis techniques that make it possible to dissect complex collections of data from a wide range of technologies and sources.

Aims and areas of interest

The fields of stem cell biology and regenerative medicine research are fundamentally about understanding dynamic cellular processes such as development, reprogramming, repair, differentiation and the loss, acquisition or maintenance of pluripotency. In order to precisely decipher these processes at a molecular level, it is critical to identify and study key regulatory genes and transcriptional circuits.

Modern high-throughput molecular profiling technologies provide a powerful approach to addressing these questions as they allow the profiling of tens of thousands of gene products in a single experiment. A central  focus of our work is to use bioinformatics to interpret the information produced by such technologies. We work extensively with data from public repositories and collaborations over a wide variety of platforms such as microarrays, RNA-seq and ChIP-seq, using the latest methods to integrate studies and explore biological function.

Dr Simon Tomlinson

Group Leader

Contact details

Current research interests

In general we are interested to understand how the functional properties of stem cells are encoded in their genome and expressed through the transcriptome. We take a purely bioinformatics approach to this work, capitalising on the wealth of genomic and functional data now available. We work mainly in the mouse embryonic stem cell system but occasionally in related areas. We take an integrative approach to data analysis, typically working with large data compendia and combining well established methods (analysis of gene expression scRNA-seq, RNA-seq etc) with advanced emerging methods from data science. To aid our work we make extensive use of R, custom databases, large scale storage and cluster computing. We build software systems using Java and even occasionally use Python.

Group Members

Kay Kong -PhD student(Joint  first supervisor with Prof Kamil Kranc)

Ludi Ling -MSc Bioinformatics student

Meishan Liu -MSc Bioinformatics student 

Yuqing Wang- MSc Bioinformatics student

Xinyu Yi- MSc Bioinformatics student

Busra Senin-  Undergraduate summer internship student 

Collaborators

  • Prof Ian Chambers
  • Prof Keisuke Kaji
  • Prof Clare Blackburn
  • Prof Alexander Medvinsky