Simon Tomlinson (PhD)

Senior Lecturer in Bioinformatics, Group Leader

Postgraduate teaching

My current teaching responsibilites centre around the School of Biological Sciences Bioinformatics MSc.

I was Programme Director for the successful Bioinformatics MSc for over ten years ending 2019 and now I am working on a new MSc programme.

This academic year 2019-2020 I am running and teaching three full MSc Bioinformatics courses.

Bioinformatics Algorithms (10 credit level 11 course)

Functional Genomic Technologies (10 credit level 11 course)

Research Dissertation (60 credit level 11 course)

I am a Personal Tutor, a member of the Exam Board for Bioinformatics and SBS representative on the Informatics Board of Studies.

My group has supervised over 20 successful PhD students and aproximately 30 successful MSc students.  

 

Open to PhD supervision enquiries?

Yes

Areas of interest for supervision

If you are interested in completing a PhD in the group or for the group to host a project please feel free to get in contact to discuss the possibilities.  Please note that the lab itself works purely in silico- we collaborate with other groups for any  experimental components on our projects.   Projects in the group are increasingly moving in a data science direction centering on the use of large scale analysis of functional genomic data to explore gene regulation.

 

Current PhD students supervised

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 

Research summary

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.

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.

Affiliated research centres

Prof Ian Chambers

Prof Keisuke Kaji

Prof Clare Blackburn

Prof Kamil Kranc

Prof  Katrin Ottersbach

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