Dr Laurent Gatto is a Senior Research Associate in the Department of Biochemistry. He moved to Cambridge in January 2010 to work in the Cambridge Centre for Proteomics on various aspects of quantitative and spatial proteomics, developing methodological advances and implementing computational tools with a strong emphasis on rigorous and reproducible data analysis. He is also a visiting scientist in the PRIDE team at the European Bioinformatics Institute, affiliate teaching staff at the Cambridge Computational Biology Institute, a Software Sustainability Institute fellow and a Software/Data Carpentry instructor. Since August 2013, he heads the Computational Proteomics Unit.
The Computational Proteomics Unit's main activities centre around the sound analysis of proteomics data and integration of different sources of heterogeneous data. We work in close collaboration with biologists to tackle biologically challenging questions using statistics and machine learning to understand the data and uncover biologically relevant patterns. The development and publication of scientific software (1, 2) is an integral part of our work and is reflected by our contributions to the Bioconductor project.
Breckels LM, Holden S, Wonjar D, Mulvey CM, Christoforou A, Groen AJ, Kohlbacher O, Lilley KS, Gatto L. Learning from heterogeneous data sources: an application in spatial proteomics. PLoS Comput Biol. 2016 May 13;12(5):e1004920 doi:10.1371/journal.pcbi.1004920.
Christoforou A, Mulvey CM, Breckels LM, Geladaki A, Hurrell T, Hayward PC, Naake T, Gatto L, Viner R, Arias AM, Lilley KS. A draft map of the mouse pluripotent stem cell spatial proteome. Nat Commun. 2016 Jan 12;7:9992 doi:10.1038/ncomms9992.
Gatto L, Breckels LM, Naake T and Gibb S Visualisation of proteomics data using R and Bioconductor. Proteomics. 2015 Feb 18. doi:10.1002/pmic.201400392.
Huber W et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015 Jan 29;12(2):115-21.
Gatto L, et al. A foundation for reliable spatial proteomics data analysis, Mol Cell Proteomics. 2014 Aug;13(8):1937-52.
Vizcaíno J.A. et al. ProteomeXchange: globally co-ordinated proteomics data submission and dissemination, Nature Biotechnology 2014, 32, 223–226.
Gatto L., Breckels L.M, Burger T, Wieczorek S. and Lilley K.S. Mass-spectrometry based spatial proteomics data analysis using pRoloc and pRolocdata, Bioinformatics, 2014.
Gatto L. and Christoforou A. Using R and Bioconductor for proteomics data analysis, Biochim Biophys Acta - Proteins and Proteomics, 2013.
Breckels L.M., Gatto L., Christoforou A., Groen A.J., Lilley K.S. and Trotter M.W.B. The Effect of Organelle Discovery upon Sub-Cellular Protein Localisation, Journal of Proteomics, 2013
Chambers M. et al. A Cross-platform Toolkit for Mass Spectrometry and Proteomics, Nature Biotechnology 30, 918–920, 2012.
Gatto L. and Lilley K.S. MSnbase - an R/Bioconductor package for isobaric tagged mass spectrometry data visualisation, processing and quantitation, Bioinformatics, 28(2), 288-289, 2012.
MSnbase: Base Functions and Classes for MS-based Proteomics, Bioconductor.
pRoloc: A unifying bioinformatics framework for spatial proteomics, Bioconductor.
pRolocGUI: Interactive visualisation of spatial proteomics data, Bioconductor.
RforProteomics: Companion package to the 'Using R and Bioconductor for proteomics data analysis' and 'Visualisation of proteomics data using R and Bioconductor' publications, Bioconductor.