Tom Mayo

Tom Mayo


Research Interests

I am interested in how DNA methylation gives rise to transcriptional regulation. To address this question, I will be developing machine learning methods to analyse higher order features of next generation sequencing data.

As the project develops, I aim to develop rigorous analytical tools to integrate data pertaining to other epigenetic mechanisms. These will be used to explore how distinct epigenetic mechanisms interact.

In particular, I will explore the interactions between methylation and MeCP2 binding. MeCP2 is an important protein for healthy neuronal function. Mutations in this gene are responsible for Rett Syndrome, which is debilitating in females and fatal in males

Publications:
2014
  Identifying Spatially Correlated Changes in methylation Profiles Using Kernel Methods
Mayo, T, Schweikert, G & Sanguinetti, G 2014, 'Identifying Spatially Correlated Changes in methylation Profiles Using Kernel Methods' Workshop on Statistical Systems Biology, Warwick, United Kingdom, 9/12/14 - 11/12/14, .
DNA methylation is an intensely studied epigenetic mark associated with many fundamental biological processes of direct clinical relevance. Bisulfite treatment of DNA followed by next generation sequencing provides quantitative methylation data at base pair resolution. However, statistical modelling of such data is challenging. Current approaches do not consider higher order features of the data, so that spatially correlated changes are ignored. A recent paper has shown that the shape of the methylation profile change is predictive of gene expression. Furthermore, parametric tests require high coverage and replication and are prone to overconfidence under high coverage conditions.
We introduce a non-parametric test, M3D, based on the maximum mean discrepancy to address such issues. M3D uses kernel methods to capture spatially correlated changes in methylation profiles and displays improved power over existing methods in challenging conditions. The method is freely available via Bioconductor as package M3D.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Mayo, Tom, Schweikert, Gabrielle & Sanguinetti, Guido.
Publication Date: 2014
Publication Information
Category: Poster
Original Language: English
  Identifying Spatially Correlated Changes in Methylation Profiles Using Kernel Methods
Mayo, T, Sanguinetti, G & Schweikert, G 2014, 'Identifying Spatially Correlated Changes in Methylation Profiles Using Kernel Methods' Eighth International Workshop on Machine Learning in Systems Biology, Strasbourg, Austria, 6/09/14 - 7/09/14, .
DNA methylation is an intensely studied epigenetic mark associated with many fundamental biological processes of direct clinical relevance. Bisulfite treatment of DNA followed by next generation sequencing provides quantitative methylation data at base pair resolution. However, statistical modelling of such data is challenging. Current approaches do not consider higher order features of the data, so that spatially correlated changes are ignored. A recent paper has shown that the shape of the methylation profile change is predictive of gene expression. Furthermore, parametric tests require high coverage and replication and are prone to overconfidence under high coverage conditions.
We introduce a non-parametric test, M3D, based on the maximum mean discrepancy to address such issues. M3D uses kernel methods to capture spatially correlated changes in methylation profiles and displays improved power over existing methods in challenging conditions. The method is freely available via Bioconductor as package M3D.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Mayo, Tom, Sanguinetti, Guido & Schweikert, Gabrielle.
Publication Date: 2014
Publication Information
Category: Poster
Original Language: English

Projects:
Analysis and Modelling of Epigenetic Data: MeCP2 and Methylation in Rett Syndrome (PhD)