Short bio

Since September 2018, I am an Associate Professor of Bioinformatics at the UCLouvain, in Belgium, and director of the Computational Biology and Bioinformatics (CBIO) group. I am located in the de Duve Institute, on the medical campus in Brussels, where I run a research group and teach at the faculty of pharmacy and biomedical sciences (FASB).

I am an avid open research advocate and make every possible effort to make my research reproducible and openly available. I am a Software Sustainability Institute fellow, a Data and Software Carpentry instructor, and an affiliated member of the Bioconductor project (see below).

I am also involved in the Bullied Into Bad Science campaign, an initiative by and for early career researchers who aim for a fairer, more open and ethical research and publication environment. In 2017-2018, I was also part of the eLife Early-career advisory group and, since 2017, am a #ASAPbio ambassador.

Bioconductor (Huber et al., 2015) is a project that has had a great impact on many aspects of research and development in bioinformatics, computational statistics, bio-medical sciences and many other fields. It also had a big impact on my vision of rigorous research software development as the foundation of reliable, open and reproducible research. Since my very first software contribution (the yaqcaffy package, accepted in Bioconductor 2.2, since then deprecated), I have become involved in multiple capacity in the project. I have contributed many packages (mostly focused on mass spectrometry and proteomics), I am a member of the technical advisory board, deputy-chair of the European Bioconductor Society, I co-lead the Teaching and education committee, I’m a member of the Code of conduct committee since it was set up, I contribute to various working groups, and am an occasional package reviewer. In am one of the winners of the 2021 Bioconductor Community Awards, one of my proudest professional achievements. In addition to delivering great science and software, Bioconductor is a welcoming and supportive community.

Before my current position at the UCLouvain, I was a Senior Research Associate in the Department of Biochemistry at the University of Cambridge. It’s at the Cambridge Centre for Proteomics and the Computational Proteomics Unit (my former research group in Cambridge) that I started working on various aspects of quantitative and spatial proteomics, developing new methods and implementing computational tools with a strong emphasis on rigorous and reproducible data analysis. During my time in Cambridge, I have also a been a visiting scientist in the PRIDE team at the European Bioinformatics Institute and in the Department of Applied Mathematics and Theoretical Physics (DAMTP).

And even before that, during my MSc and PhD work, I studied micro-evolutionary genetic patterns of the Broom leaf beetle Gonioctena variabilis in Southern Europe (Gatto el al., 2008), the application of short interspersed mobile elements (SINEs) to study the evolution of cetaceans, and the applicability of the General Time Reversible nucleotide substitution model in the light of differential lineage sorting (Gatto el al., 2006). I also spend 3 years in industry working on genomic and transcriptomics data, in particular the microarray quality control (Shi et al., 2010).


As pointed out by D. Donoho, An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures. This directly applies to high throughput biology data analysis and I strongly believe that being able to reproduce the complete set of results, replicate an analysis with new data and track the evolution of the work that lead to the scientific novelty are essential aspects of the process of doing research. Hence, I regard the development of scientific software as well as agile and robust analysis methodologies that facilitate reproducible research, an important aspect of my scientific activity.

Clarity and traceability of the data and the analysis methodology enable us to better understand what we do, how and why we do it and consequently exploit the data and comprehend the biology. While not sufficient, these are nevertheless necessary requirements for effective data-driven science.

The collaborative and interdisciplinarity nature of much of the research in biology calls for a open approaches, influenced by the open source and free/libre software movements, from communication between stake holders, open research and development to open dissemination of all research outputs.

Mass spectrometry and proteomics

My work on the design and implementation of reproducible mass spectrometry-based proteomics data analysis pipeline has materialised in the development of the MSnbase (Gatto et al. 2012 and 2021) package to manipulate, process and analyse quantitative proteomics data. The MSnbase infrastructure also supports the work on the statistical learning applied to spatial proteomics (see below). After a decade of MSnbase development, Johannes Rainer, Sebastien Gibb and I have started the R for Mass Spectrometry initiative. Base on our decade-long experiences, we have set ourselves the aim of designing and implementing efficient, thoroughly documented, tested and flexible R software for the analysis and interpretation of high throughput mass spectrometry assays, including proteomics and metabolomics experiments.

Single-cell proteomics

In a Nature Methods Technology Feature in August 2019, Vivien Marx dreamt of single-cell proteomics. In 2021, thanks to some pioneers’ groundbreaking efforts, we can confidently claim that single-cell proteomics is here. The CBIO lab has contributed the scp package (Vanderaa and Gatto, 2021 and pre-print, 2023a and pre-print, 2023b and pre-print) to these efforts. scp relies of R for Mass Spectrometry’s QFeatures package and dedicated functions to enable extensive single-cell proteomics data processing, analysis and interpretation. In 2023, we also contributed to initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments (Gatto et al. 2023 and pre-print). Our latest work proposes the scplainer method, based linear models to understand mass spectrometry-based single-cell proteomics data, that tackles many of the issues described above.

Spatial proteomics

In biology, localisation is function: knowledge of the localisation of proteins is of paramount importance to assess and study their function, and spatial proteomics is the systematic study of the sub-cellular localisation of proteins and changes thereof (Gatto et al. 2010 and 2014). Since 2010, my team has developed novel software and machine learning approaches enabling more reliable and systematic inference of protein localisations using quantitative proteomics. This work has materialised in the pRoloc package (Gatto et al. 2014) that implements various established classification algorithms, effective visualisation techniques (Gatto el al. 2015) as well as novelty detection (Breckels et al. 2013, Crook et al. 2020) and transfer learning (Breckels et al. 2016), harvesting GO annotations of microscopy-based methods to improve the spatial resolution of experimental spatial proteomics data. More recently, we proposed a Bayesian framework to quantify uncertainty and probabilisatically address multi-localsiation of proteins (Crook et al. 2018) and study comparative spatial proteomics to infer protein re-localisation (Crook et al. 2022).


My current course at the UCLouvain are

Over the years I have been involved in many teaching activities, ranging from beginners and advanced R courses, genome biology, proteomics bioinformatics, integrative omics, scientific computing as part of the MPhil in Computational Biology in Cambridge, as well as several Software and Data Carpentry bootcamps. All my teaching material is available in the TeachingMaterial repository.

Please do get in touch if you are interested in running workshops.


See also my Google scholar profile and publications in PubMed.

Research articles

Lynch MP, Wang Y, Gatto L, Culhane AC. demuxSNP: supervised demultiplexing scRNAseq using cell hashing and SNPs. bioRxiv 2024.04.22.590526; doi:10.1101/2024.04.22.590526.

Pirenne S, Manzano-Núñez F, Loriot A, Cordi S, Desmet L, Aydin S, Hubert C, Toffoli S, Limaye N, Sempoux C, Komuta M, Gatto L, Lemaigre FP. Spatial transcriptomics profiling of gallbladder adenocarcinoma: a detailed two-case study of progression from precursor lesions to cancer. bioRxiv 2024.02.27.582232; doi:10.1101/2024.02.27.582232.

Ward B et al. Deep plasma proteomics with data-independent acquisition: A fastlane towards biomarkers identification. bioRxiv 2024.02.23.581160; doi:10.1101/2024.02.23.581160

Hauchamps P, Bayat B, Delandre S, Hamrouni M, Toussaint M, Temmerman S, Lin D, Gatto L CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data BMC Bioinformatics 25, 80 (2024). doi:10.1186/s12859-024-05691-z (pre-print: 10.1101/2023.10.10.561699).

Lizcano-Perret B, Vertommen Didier, Herinckx G, Calabrese V, Gatto L, Roux P and Michiels T Identification of RSK substrates using an analog-sensitive kinase approach (2024) J. Biol. Chem. doi:10.1016/j.jbc.2024.105739.

Vanderaa C and Gatto L scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data (2023) bioRxiv 2023.12.14.571792; doi:10.1101/2023.12.14.571792.

Harvengt AA, Polle OG, Martin M, van Maanen A, Gatto L, et al. Post-Hypoglycemic hyperglycemia are highly relevant markers for stratification of glycemic variability and partial remission status of pediatric patients with new-onset type 1 diabetes (2023) PLOS ONE 18(11): e0294982 doi:10.1371/journal.pone.0294982.

Grégoire S, Vanderaa C, Pyr dit Ruys S, Mazzucchelli G, Kune C, Vertommen D and Gatto L Standardised workflow for mass spectrometry-based single-cell proteomics data processing and analysis using the scp package arXiv arXiv:2310.13598; doi:10.48550/2310.13598.

Cloos AS, Pollet H, Stommen A, Maja M, Lingurski M, Brichard B, Lambert C, Henriet P, Pierreux C, Pyr Dit Ruys S, Van Der Smissen P, Vikkula M, Gatto L, Martin M, Brouillard P, Vertommen D, Tyteca D. Splenectomy improves erythrocyte functionality in spherocytosis based on septin abundance, but not maturation defects. Blood Adv. 2023 Sep 12;7(17):4705-4720. doi:10.1182/bloodadvances.2022009114.

Kelly Eckenrode, Dario Righelli, Marcel Ramos, Ricard Argelaguet, Christophe Vanderaa, Ludwig Geistlinger, Aedin Culhane, Laurent Gatto, Vincent J. Carey, Martin Morgan, Davide Risso, Levi D Waldron Curated single cell multimodal landmark datasets for R/Bioconductor PLOS Computational Biology 19(8): e1011324. doi:10.1371/journal.pcbi.1011324 (preprint bioRxiv:10.1101/2021.10.27.466079).

Gannoun L, De Schrevel C, Belle M, Dauguet N, Achouri Y, Loriot A, Vanderaa C, Cordi S, Dili A, Heremans Y, Rooman I, Leclercq IA, Jacquemin P, Gatto L, Lemaigre FP. Axon guidance genes control hepatic artery development Development. 2023 Aug 15;150(16):dev201642. doi:10.1242/dev.201642. Epub 2023 Aug 17.

Vanderaa C and Gatto L. Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics J. Proteome Res. (2023) doi:10.1021/acs.jproteome.3c00227 (preprint arXiv:2304.06654v2).

Halbout M, Bury M, Nanet A, Gerin I, Graff J, Killian T, Gatto L, Vertommen D, Bommer G. SUZ domain-containing proteins have multiple effects on nonsense-mediated decay target transcripts (2023) Journal of Biological Chemistry, July 2023; doi:10.1016/j.jbc.2023.105095.

Degraeve AL, Haufroid V, Loriot A, et al. Gut microbiome modulates tacrolimus pharmacokinetics through the transcriptional regulation of ABCB1. Microbiome 11, 138 (2023) doi:10.1186/s40168-023-01578-y.

Jannone G, Bonaccorsi Riani E, de Magnée C, Tambucci R, Evraerts J, Ravau J, Baldin P, et al. (2023). Senescence and Senotherapies in Biliary Atresia and Biliary Cirrhosis Aging 15 (11): 4576–99 10.18632/aging.204700.

Zhu J, Naulaerts S, Boudhan L, Martin M, Gatto L, and Van den Eynde BJ. Tumour Immune Rejection Triggered by Activation of alpha2-Adrenergic Receptors (2023) Nature, June, 1-9; doi:10.1038/s41586-023-06110-8.

Colson A, Depoix CL, Lambert I, Leducq C, Bedin M, De Beukelaer M, Gatto L, Loriot A, Peers de Nieuwburgh M, Bouhna K, Baldin P, Hubinont C, Sonveaux P, Debiève F Specific HIF-2α (Hypoxia-Inducible Factor-2) Inhibitor PT2385 Mitigates Placental Dysfunction In Vitro and in a Rat Model of Preeclampsia (RUPP) (2023) Hypertension, 80:1011–1023 doi:10.1161/HYPERTENSIONAHA.122.20739.

Gatto L, Aebersold R, Cox J et al., Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments (2023) Nat Methods 20, 375–386 doi:10.1038/s41592-023-01785-3 (preprint: arXiv:2207.10815).

Vanderaa C and Gatto L The current state of single-cell proteomics data analysis (2023), Current Protocols 3 (1):e658, 10.1002/cpz1.658 (preprint: arXiv:2210.01020).

Crook OM, Davies CTR, Breckels LM, Christopher JA, Gatto L, Kirk PDW, Lilley KS Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE, Nature Communications 13, 5948 (2022) doi:10.1038/s41467-022-33570-9 (Pre-print: biorXiv).

Lizcano-Perret B., Lardinois C., Wavreil F., Hauchamps P., Herinckx G., Sorgeloos F., Vertommen D., Gatto L., Michiels T. Cardiovirus leader proteins retarget RSK kinases toward alternative substrates to perturb nucleocytoplasmic traffic. (2022) PLoS Pathogens 18(2):e1011042. doi:10.1371/journal.ppat.1011042.

Pollé O.G, Delfosse A., Martin M., Louis J., Gies I., den Brinker M., Seret N., Lebrethon M., Mouraux T., Gatto L., Lysy P.A. on behalf of the DIATAG Working Group Glycemic Variability Patterns Strongly Correlate with Partial Remission Status in Children with Newly Diagnosed Type 1 Diabetes. Diabetes Care 45(10):2360–2368 (2022), doi:10.2337/dc21-2543.

Moulis M., Runser S.V.M., Glorieux L. et al. Identification and implication of tissue-enriched ligands in epithelial–endothelial crosstalk during pancreas development. Sci Rep 12, 12498 (2022), doi:10.1038/s41598-022-16072-y.

Ward B, Yombi JC, Balligand J-L, Cani PD, Collet J-F, et al. HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach. Viruses. 2022; 14(7):1373, doi:10.3390/v14071373.

Dewulf JP, Martin M, Marie S, Oguz F, Belkhir L, De Greef J, et al. Urine metabolomics links dysregulation of the tryptophan-kynurenine pathway to inflammation and severity of COVID-19. Sci Rep. 12: 1–8 (2022), doi:10.1038/s41598-022-14292-w.

De Berdt P, Vanvarenberg K, Ucakar B, Bouzin C, Paquot A, Gratpain V, Loriot A, Payen V, Bearzatto B, Muccioli GG, Gatto L, Diogenes A, des Rieux A The human dental apical papilla promotes spinal cord repair through a paracrine mechanism Cell. Mol. Life Sci. 79, 252 (2022), doi:10.1007/s00018-022-04210-8.

Dontaine J, Bouali A, Daussin F et al. The intra-mitochondrial O-GlcNAcylation system rapidly modulates OXPHOS function and ROS release in the heart. Commun Biol 5, 349 (2022), doi:10.1038/s42003-022-03282-3.

Delcorte O, Spourquet C, Lemoine P, Degosserie J, Van Der Smissen P, Dauguet N, Loriot A, Knauf JA, Gatto L, Marbaix E, Fagin JA, Pierreux CE. BRAFV600E Induction in Thyrocytes Triggers Important Changes in the miRNAs Content and the Populations of Extracellular Vesicles Released in Thyroid Tumor Microenvironment. Biomedicines. 2022; 10(4):755, doi:10.3390/biomedicines10040755.

Rainer J, Vicini A, Salzer L, Stanstrup J, Badia JM, Neumann S, Stravs MA, Verri Hernandes V, Gatto L, Gibb S, Witting M. A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R. Metabolites. 2022; 12(2):173, doi:10.3390/metabo12020173.

Aayush Grover and Laurent Gatto ProtFinder: finding subcellular locations of proteins using protein interaction networks bioRxiv 2022.01.11.475836, doi:10.1101/2022.01.11.475836 (software repo).

Dechamps M, De Poortere, Martin M, Gatto L, et al. Inflammation-Induced Coagulopathy Substantially Differs Between COVID-19 and Septic Shock: A Prospective Observational Study Frontiers in Medicine (2022), doi:10.3389/fmed.2021.780750.

Christophe Vanderaa, Laurent Gatto Replication of single-cell proteomics data reveals important computational challenges Expert Review of Proteomics (2021), doi:10.1080/14789450.2021.1988571 (pre-print: 10.1101/2021.04.12.439408v2).

Mulvey CM, Breckels LM, Crook O, Sanders D, Ribeiro A, Geladaki A, Christoforou A, Britovsek NK, Hurrell T, Deery MJ, Gatto L, Smith A, and Lilley KS. Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line Nat Commun 12, 5773 (2021), doi:10.1038/s41467-021-26000-9.

Killian T and Gatto L. Exploiting the DepMap cancer dependency data using the depmap R package [version 1; peer review: 1 approved with reservations]. F1000Research 2021, 10:416, doi:10.12688/f1000research.52811.1.

Jérôme Ambroise, Laurent Gatto, Julie Hurel, Bertrand Bearzatto, Jean-Luc Gala On the many advantages of using the VariantExperiment class to store, exchange and analyze SARS-CoV-2 genomic data and associated metadata bioRxiv 2021.04.05.438328, doi:

Crook OM, Geladaki A, Nightingale DJH, Vennard O, Lilley KS, Gatto L, Kirk PDW A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection PLOS Computational Biology 2020 16(11):e1008288, doi:10.1371/journal.pcbi.1008288 (preprint).

Pollet H et al., Aberrant Membrane Composition and Biophysical Properties Impair Erythrocyte Morphology and Functionality in Elliptocytosis, Biomolecules 2020, 10(8), 1120, doi:10.3390/biom10081120.

Gatto L, Gibb S, Rainer J MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data J. Proteome Res. 2021, 20, 1, 1063–1069, doi:10.1021/acs.jproteome.0c00313 (preprint).

Silverman EK et al., Molecular networks in Network Medicine: Development and applications, WIREs Systems Biology and Medecine, 2020, doi:10.1002/wsbm.1489.

Crook PM, Gatto L, Kirk PDW Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics Statistical Applications in Genetics and Molecular Biology, Volume 18, Issue 6, 1544-6115, doi:10.1515/sagmb-2018-0065 2019 (pre-print: arXiv:1810.05450).

Baers LL, Breckels LM, Mills LA, Gatto L, Deery M, Stevens TJ, Howe CH, Lilley KS, Lea-Smith DJ Proteome mapping of a cyanobacterium reveals distinct compartment organisation and cell-dispersed metabolism, Plant Physiology 2019, doi:10.1104/pp.19.00897.

Petyuk VA, Gatto L and Payne SH Reproducibility and Transparency by Design, Molecular & Cellular Proteomics July 4, 2019, mcp.IP119.001567, doi:10.1074/mcp.IP119.001567.

Crook OM, Breckels LM, Lilley KS, Kirk PDW and Gatto L (2019) A Bioconductor workflow for the Bayesian analysis of spatial proteomics [version 1; peer review: 1 approved, 2 approved with reservations] F1000Research 2019, 8:446, doi:10.12688/f1000research.18636.1.

Crook OM, Lilley KS, Gatto L, Kirk PDW (2019). Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics. arXiv [stat.AP].

Johannes Rainer, Laurent Gatto, Christian X. Weichenberger; ensembldb: an R package to create and use Ensembl-based annotation resources, Bioinformatics, btz031, doi:10.1093/bioinformatics/btz031.

Laurent Gatto, Lisa M Breckels, Kathryn S Lilley Assessing sub-cellular resolution in spatial proteomics experiments Current Opinion in Chemical Biology, 48 123-149 (2019), doi:10.1016/j.cbpa.2018.11.015.

Crook OM, Mulvey CM, Kirk PDW, Lilley KS, Gatto L (2018) A Bayesian mixture modelling approach for spatial proteomics. PLoS Comput Biol 14(11): e1006516.

Eglen, S., Mounce, R., Gatto, L., Currie, A., & Nobis, Y. Recent developments in scholarly publishing: a view from the life sciences. Emerging Topics in Life Sciences, Dec 21, 2018, 2 (6) 775-778, doi:10.1042/ETLS20180172 (pre-print doi:10.31219/

Aikaterini Geladaki, Nina Kocevar Britovsek, Lisa M. Breckels, Tom S. Smith, Claire M. Mulvey, Oliver M. Crook, Laurent Gatto, Kathryn S. Lilley Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics Nature Communications volume 10, Article number:331 (2019) (pre-print 10.1101/378364).

Segeritz CP, Rashid ST, Cardoso de Brito M, Paola MS, Ordonez A, Morell CM, Kaserman JE, Madrigal P, Hannan N, Gatto L, Tan L, Wilson AA, Lilley K, Marciniak SJ, Gooptu B, Lomas DA, Vallier L. hiPSC hepatocyte model demonstrates the role of unfolded protein response and inflammatory networks in α(1)-antitrypsin deficiency. J Hepatol. 2018 Jun 4. pii: S0168-8278(18)32113-5. doi:10.1016/j.jhep.2018.05.028.

Nett I, Mulas C, Gatto L, Lilley KS, Smith A. Negative feedback via RSK modulates Erk‐dependent progression from naïve pluripotency EMBO reports (2018) e45642, doi:10.15252/embr.201745642.

Thul PJ, et al. A subcellular map of the human proteome. Science. 2017 May 11. pii: eaal3321, doi:10.1126/science.aal3321. PubMed PMID:28495876.

Mulvey CM, Breckels LM, Geladaki A, Kocevar Britovsek N, Nightingale DJH, Christoforou A , Elzek M, Deery MJ, Gatto L, Lilley KS. Using HyperLOPIT to perform high-resolution mapping of the spatial proteome. Nature Protocols, 12, 1110–1135 (2017) doi:10.1038/nprot.2017.026 (See the F1000Research workflow for details on the computational side of the protocol.)

Leprevost FD, et al. BioContainers: An open-source and community-driven framework for software standardization. Bioinformatics. 2017 Mar 30, doi:10.1093/bioinformatics/btx192. [Epub ahead of print] PubMed PMID:28379341.

Breckels LM, Mulvey CM, Lilley KS and Gatto L. A Bioconductor workflow for processing and analysing spatial proteomics data 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 May 1;30(9):1322-4. doi:10.1093/bioinformatics/btu013 (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).

MAQC Consortium The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models Nature Biotechnology 28, 827–838 2010 (PubMed, pdf).

Gatto L., Mardulyn P. and Pasteels J.M. Morphological and mitochondrial DNA analysis suggest the presence of a hybrid zone between two species of the leaf beetle Gonioctena variabilis species complex in southern Spain, Biological Journal of the Linnean Society, 2008, 94(1), 105-114 (abstract, pdf).

Danis B., George T.C., Goriely S., Dutta B., Renneson J., Gatto L., Fitzgerald-Bocarsly P., Marchant A., Goldman M., Willems F. and De Wit D. Interferon regulatory factor 7-mediated responses are defective in cord blood plasmacytoid dendritic cells. Eur J Immunol. 2008 Feb;38(2):507-17. (PubMed, pdf).

Gatto L., Catanzaro D. and Milinkovitch M.C. Assessing the Applicability of the GTR Nucleotide Substitution Model Through Simulations Evolutionary Bioinformatics 2006:2 (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.

Technical Notes

Gatto L. Data Management Plan for a Biotechnology and Biological Sciences Research Council (BBSRC) Tools and ResourcesDevelopment Fund (TRDF) Grant, Research Ideas and Outcomes (2017), doi:10.3897/rio.3.e11624.

Gatto, L. and Schretter, C. Designing Primer Pairs and Oligos with OligoFaktorySE. North America, 15, oct. 2009 (pdf,software).

Schretter, C. and Gatto, L. A Tiny Queuing System for Blast Servers December, 2005 (short and slighly longer versions).

Web/press coverage

News in Proteomics Research blog posts about R for proteomics, R and Bioconductor, spatial proteomics and handling of missing values.

Genome web Statistical Design Remains Sticking Point for Proteomics Experiments Jul 27, 2017.

Publishers put the squeeze on ResearchGate, 11 Oct 2017.

Nature News & Comment Correspondance Preprints help journalism, not hinder it, , 29 Aug 2018.


I have developed and have contributed to many open source R/Bioconductor packages, in particular proteomics software and data packages, all of which are available on my own and my group’s GitHub pages. See Gatto and Christoforou, 2014, Gatto et al., 2014 and the RforProteomics vignettes for an overview of the R/Bioconductor infrastructure for mass spectrometry and proteomics.


This is an incomplete list of talks I have given in the past.

Principled and Reproducible Mass Spectrometry-based Single Cell Proteomics Data Analysis, USHUPO, 08 March 2020.

Standardised and reproducible analysis of mass spectrometry-based single-cell proteomics data, Single Cell Proteomics meeting, 18 August 2020.

MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data, ISCB ISMB 2020 virtual conference, 15 July 2020.

MSnbase design principles and code walk, Online May Institute for Computation and statistics for mass spectrometry and proteomics, 7 May 2020.

Probabilistic mapping of the sub-cellular proteome, VIB Applied Bioinformatics in Life Sciences (3rd edition), 13-14 February 2020, Leuven Belgium.

Invited talk at the Belgian Biophysical Society Symposium Protein folding and stability, 30 August 2019, Liège.

Invited talk at the The Alan Turing Institute Workshop on Statistical Data Science for Proteomics and Metabolomics, 13th June 2019.

Invited talk at the International Society for Clinical Biostatistics BePA mini-symposium at the KULeuven, 18 July 2019.

Becoming a better scientist with open and reproducible research, Are you ready for publishing reproducible research? meeting, 16 May 2019, TU Delft, Netherlands.

Open source and open development proteomics software at the EuBIC 2018 developer’s meeting, 9 - 12 January 2018, Ghent, Belgium.

A longer talk about the Bullied Into Bad Science campaign at the OpenConCam 2017 conference on the 16 November 2017. The content and slides are available here.

Short I won’t be #BulliedIntoBadScience! as part of the panel discussion Next-generation initiatives advancing open, at OpenCon 11 - 13 November 2017, Berlin.

Mapping the sub-cellular proteome, 8 November 2017, Leibniz Institut for Aging, Jena, Germany.

Open Science in Practice, 25 September 2017, Lausanne, Switzerland. An early career researcher’s view on modern and open scholarship.

Proteomics Method Forum, Oxford, UK, 22-23 June 2017. The Bioconductor project - analysis and comprehension of high-throughput proteomics data.

Research Data Management Forum, London, UK, 9th June 2017. An early career researcher’s view on modern and open scholarship … and careers.

Office of Scholarly Communication Training - How to Get the Most Out of Modern Peer Review, Cambridge, UK, 30 Mar 2017. The role of peer-reviewers in promoting open science.

European Bioconductor Developer Meeting, Zurich, Switzerland, 6 - 7 December 2016. MSnbase2 - disk access is the limit.

Cambridge Computational Biology Institute, UK, 16 November 2016. Mapping the sub-cellular proteome: Computational analyses of high-throughput mass spectrometry-based spatial proteomics data.

Dialogue on methods for ecology, Cambridge, UK, 15 November 2016, Learning from heterogeneous data in spatial proteomics.

Quantitative Proteomics and Data Analysis, Chester, UK, 4 - 5 April 2016. Inspection, visualisation and analysis of quantitative proteomics data (slides, vignette).

Introduction to Integrative Omics: proteomics, European Bioinformatics Institute, Hinxton, UK, 8 March 2016.

Get in touch

On the fediverse at, by email at name.surname<at>, via a Github issue on a relevant repo or on the Bioconductor community slack.