Journal articles

Breckels LM, Mulvey CM, Lilley KS and Gatto L. A Bioconductor workflow for processing and analysing spatial proteomics data [version 1; referees: awaiting peer review]. F1000Research 2016, 5:2926 (doi:10.12688/f1000research.10411.1). Software: MSnbase, pRoloc, pRolocGUI

Wieczorek S, Combes F, Lazar C, Giai Gianetto Q, Gatto L, Dorffer A, Hesse A, Coute Y, Ferro M, Bruley C, and Burger T. DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics Bioinformatics 2016, doi:10.1093/bioinformatics/btw580.

Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Leprevost Fda V, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ, Konovalov A, Flight RM, Blin K, Vizcaino JA. Ten Simple Rules for Taking Advantage of Git and GitHub. PLoS Comput Biol. 2016 Jul 14;12(7):e1004947. doi:10.1371/journal.pcbi.1004947 PMID:27415786.

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, Software)

Fabre B, Korona D, Groen A, Vowinckel J, Gatto L, Deery MJ, Ralser M, Russell S, Lilley KS. Analysis of the Drosophila melanogaster proteome dynamics during the embryo early development by a combination of label-free proteomics approaches, Proteomics, 2016 (PMID:27029218, Publisher)

Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. J Proteome Res. 2016 Apr 1;15(4):1116-25. (Publisher, PMID:26906401, Software: CRAN and Bioconductor)

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 (PMID:26754106, data, PRIDE, resource)

Gatto L, Hansen KD, Hoopmann MR, Hermjakob H, Kohlbacher O and Beyer, A Testing and validation of computational methods for mass spectrometry. J Proteome Res. 2015. doi: 10.1002/stem.2067 (PubMed).

Mulvey CM, Schröter C, Gatto L, Dikicioglu D, Baris Fidaner I, Christoforou A, Deery MJ, Cho LT, Niakan KK, Martinez-Arias A, Lilley KS. Dynamic proteomic profiling of extra-embryonic endoderm differentiation in mouse embryonic stem cells. Stem Cells. 2015 Jun 8. doi: 10.1002/stem.2067 (PubMed).

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 (PubMed, Publisher and software: Bioconductor, github).

Huber W et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015 Jan 29;12(2):115-21 (PubMed, Publisher).

Hiemstra TF et al. Human urinary exosomes as innate immune effectors, J Am Soc Nephrol. 2014 Sep;25(9):2017-27. (PubMed,Publisher).

Nikolovski N, Shliaha PV, Gatto L, Dupree P and Lilley KS Label free protein quantification for plant Golgi protein localisation and abundance, Plant Physiol. pp.114.245589; First Published on August 13, 2014; doi:10.1104/pp.114.245589 (Publisher, PubMed)

Griss J, et al. The mzTab Data Exchange Format: communicating MS-based proteomics and metabolomics experimental results to a wider audience, Mol Cell Proteomics. 2014 June 30. (Publisher)

Tomizioli M, et al. Deciphering thylakoid sub-compartments using a mass spectrometry-based approach, Mol Cell Proteomics. 2014 May 28. (Publisher, PubMed)

Gatto L, et al. A foundation for reliable spatial proteomics data analysis, Mol Cell Proteomics. 2014 Aug;13(8):1937-52. (Publisher, PubMed, software, press coverage)

Walzer M, et al. qcML: an exchange format for quality control metrics from mass spectrometry experiments, Mol Cell Proteomics. 2014 Apr 23. (PubMed).

Vizcaíno J.A. et al. ProteomeXchange: globally co-ordinated proteomics data submission and dissemination, Nature Biotechnology 2014, 32, 223–226. (PubMed)

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 (software, PubMed, publisher, software and data).

Groen A., Sancho-Andrés G., Breckels LM., Gatto L., Aniento F. and Lilley K.S. Identification of Trans Golgi Network proteins in Arabidopsis thaliana root tissue Journal of Proteome Research, 2013 (PubMed, publisher).

Wilf N.M. et al. RNA-seq reveals the RNA binding proteins, Hfq and RsmA, play various roles in virulence, antibiotic production and genomic flux in Serratia sp. ATCC 39006 BMC Genomics 2013, 14:822.

Gatto L. and Christoforou A. Using R and Bioconductor for proteomics data analysis, Biochim Biophys Acta - Proteins and Proteomics, 2013. (PubMed, pre-print and software: Bioconductor, github).

Bond N.J., Shliaha P.V, Lilley K.S., and Gatto L. Improving qualitative and quantitative performance for MSE-based label free proteomics, J. Proteome Res., 2013 (PubMed, publisher, software).

Shliaha P.V, Bond N.J., Gatto L. and Lilley K.S. The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies, J. Proteome Res., 2013 (PubMed, publisher, software).

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 (PubMed, software).

Chambers M. et al. A Cross-platform Toolkit for Mass Spectrometry and Proteomics, Nature Biotechnology 30, 918–920, 2012 (PubMed, pdf, software [1|2]).

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 (PubMed, pdf, software).

Capuano F., Bond N.J., Gatto L., Beaudoin F., Napier J., Benvenuto E., Lilley K.S, and Baschieri S. LC-MS/MS methods for absolute quantification and identification of proteins associated to chimeric plant oil bodies, Analytical Chemistry, Dec 15;83(24):9267-72, 2011 (PubMed - data).

Foster J.M., Degroeve S., Gatto L., Visser, M., Wang, R., Griss J., Apweiler R. and Martens L. A posteriori quality control for the curation and reuse of public proteomics data, Proteomics 11(11):2182-94, 2011 (PubMed, pdf).

Lilley K.S., Deery M.J. and Gatto L. Challenges for Proteomics Core Facilities, Proteomics 11: 1017–1025, 2011 (PubMed, pdf).

Gatto L., Vizcaíno J.A., Hermjakob H., Huber W. and Lilley K.S. Organelle proteomics experimental designs and analysis Proteomics, 10:22, 3957-3969, 2010 (PubMed, pdf).

Book chapters

Christoforou A., Mulvey C., Breckels LM., Gatto L. and Lilley KS. Spatial Proteomics: Practical Considerations for Data Acquisition and Analysis in Protein Subcellular Localisation Studies in Quantitative Proteomics, 185-210, The Royal Society of Chemistry, 2014.

Breckels LM, Gibb S, Petyuk V and Gatto L R for Proteomics in Proteome Informatics, The Royal Society of Chemistry, November 2016.

See also Laurent’s Google scholar profile

Selected posters

Laurent Gatto, Lisa M. Breckels, Thomas Naake, Samuel Wieczorek, Thomas Burger and Kathryn S. Lilley A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data 5 - 8 October 2014, Madrid, HUPO meeting.

Sebastian Gibb, Lisa M Breckels, Thomas Lin Pedersen, Vladislav A Petyuk, Kathryn S Lilley and Laurent Gatto A current perspective on using R and Bioconductor for proteomics data analysis 5 - 8 October 2014, Madrid, HUPO meeting.

Lisa Breckels, Sean Holden, Kathryn Lilley, Laurent Gatto A transfer learning framework for organelle proteomics data European Conference on Computational Biology 2014, 7 - 10 Sep 2014.