Bioinformatician
Lisa Breckels is a Bioinformatician and Research Associate based at the Cambridge Centre for Proteomics (CCP) at the University of Cambridge, UK.
Biography
Since November 2010, Lisa has worked in the Cambridge Centre for Proteomics in computational spatial proteomics as a Research Associate. Her main area of research focuses on computational analysis of high-throughput quantitative spatial proteomics data and development of new machine learning tools and R packages, contributing to the Bioconductor project. She is particularly interested in visualisation and developing robust and reproducible pipelines.
Prior to working in Cambridge she received a BSc (Hons) in Mathematics from the University of Essex and went on to complete a PhD in Computational Biology. Her PhD was a funded BBSRC CASE (Collaborative Awards in Science and Engineering) Award project under the supervision of Prof. Chris Reynolds (Dept. of Biological Sciences, University of Essex) and Dr. Ian Wall (GSK, Harlow). Working in structure-based drug-design she used molecular modelling and molecular dynamics simulation techniques to gain an insight into the structural and functional properties of GPCRs in their multiple states.
Publications
OM Crook, CTR Davies, LM Breckels, JA Christopher, L Gatto, PDW Kirk, KS Lilley. Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE, Nature Communications volume 135948 (2022) doi:10.1038/s41467-022-33570-9 (Pre-print: biorXiv)
E Villanueva, T Smith, M Pizzinga, M Elzek, RML Queiroz, RF Harvey, LM Breckels, OM Crook, M Monti, V Dezi, AE Willis, KS Lilley. A system-wide quantitative map of RNA and protein subcellular localisation dynamics. bioRxiv (2022) 01.24.477541;
CM Mulvey*, LM Breckels*, OM Crook, DJ Sanders, ALR Ribeiro, A Geladaki, Andy Christoforou, NK Britovšek, T Hurrell, MJ Deery, L Gatto, AM Smith, KS Lilley, Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line, Nature communications 12 (1), 1-19 (2021) doi: https://doi.org/10.1038/s41467-021-26000-9
MAW Elzek*, JA Christopher*, LM Breckels*, KS Lilley, Localization of Organelle Proteins by Isotope Tagging: Current status and potential applications in drug discovery research, Drug Discovery Today: Technologies, Volume 39, (2021) https://doi.org/10.1016/j.ddtec.2021.06.003
A Geladaki, NK Britovšek, LM Breckels, TS Smith, OL Vennard, CM Mulvey, OM Crook, LGatto, KS Lilley, Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics, Nature Communications volume 10, Article number: 331 (2019), https://doi.org/10.1038/s41467-018-08191-w
OM Crook, LM Breckels, KS Lilley, PDW Kirk, L Gatto, A Bioconductor workflow for the Bayesian analysis of spatial proteomics, F1000Research (2019) doi: 10.12688/f1000research.18636.1
CM Mulvey*, LM Breckels*, A Geladaki, NK Britovšek, DJH Nightingale, A Christoforou, MAW Elzek, MJ Deery, L Gatto, KS Lilley, Using hyperLOPIT to perform high-resolution mapping of the spatial proteome. Nat Protoc 12, 1110–1135 (2017) https://doi.org/10.1038/nprot.2017.026
Breckels LM, Holden SB, Wojnar D, Mulvey CM, Christoforou A, Groen A, et al. (2016) Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput Biol 12(5): e1004920. (2016) https://doi.org/10.1371/journal.pcbi.1004920
Breckels LM, Mulvey CM, Lilley KS, Gatto L. A Bioconductor workflow for processing and analysing spatial proteomics data. F1000Res. Dec 28;5:2926. (2016) doi: http://doi.org/10.12688/f1000research.10411.2
Gatto, L., Breckels, L. M., Burger, T., Nightingale, D. J., Groen, A. J., Campbell, C., Nikolovski, N., et al.. A foundation for reliable spatial proteomics data analysis.. Mol Cell Proteomics, 13 (8), 1937-1952. (2014) https://doi.org/10.1074/mcp.M113.036350
L.M. Breckels, L. Gatto, A. Christoforou, A.J. Groen, K.S. Lilley, M.W.B. Trotter. The effect of organelle discovery upon sub-cellular protein localisation, Journal of Proteomics, Volume 88, Pages 129-140, ISSN 1874-3919 (2013) https://doi.org/10.1016/j.jprot.2013.02.019
Please see Google Scholar
Teaching and Supervisions
2024/2025:
- Expression proteomics analysis in R - Training Lead, Trainer
2023/2024:
- Analysis of expression Proteomics data in R - Training Lead, Trainer
2022/2023:
- Introduction to Unix and bash - Trainer
- Mass Spectrometry and Proteomics Data Analysis With R/Bioconductor - Trainer
2018/2019:
- Exploring, visualising and analysing proteomics data in R - Trainer
- NST Part II BBS Bioinformatics, Computational Proteomics - Trainer
2016/2017:
- Data Carpentry - Trainer
