Senior Research Associate, Department of Biochemistry, University of Cambridge
I use statistics and machine learning to uncover relevant patterns in high throughput biology data and make every effort to make my research outputs (papers, software and data) open to everyone to read and re-use.
In biology, localisation is function: knowledge of the sub-cellular localisation of proteins is of paramount importance to assess and study their function and refine our understanding of cellular processes. Spatial or organelle proteomics is the systematic study of proteins and their sub-cellular localisation. My work is focused on the analysis of multivariate quantitative mass spectrometry-based proteomics data to infer sub-cellular localisation of proteins using contemporary and novel machine learning approaches. This research is implemented in a set of open source R/Bioconductor packages such as MSnbase and pRoloc. The software suite allows researcher to manage data, meta data and sub-cellular marker sets, apply state-of-the-art machine learning techniques to predict protein-organelle associations and incorporate data from other organelle proteomics initiatives and biological repositories. Particular emphasis is placed on reproducibility of the analyses, rigorous data exploration, comprehension of the data and the analysis pipeline, leading to a sound understanding of the data and informed interpretation of the results.
I am an affiliated member of the Bioconductor project and am involved in teaching R and scientific programming at the University of Cambridge and various workshops in the UK and abroad. I am also the main organiser of the Cambridge R user group; we have regular informal meetings and lunch seminars with short presentations and discussions on scientific computing and R programming.
Check out contributions by and mentions of Laurent Gatto on www.software.ac.uk