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27. 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)

26. 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).

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

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

23. 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)

22. 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)

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

20. 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)

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

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

17. 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 under OA license, software and data).

16. 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).

15. 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.

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

13. 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).

12. 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).

11. 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).

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

9. 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).

8. 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).

7. 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).

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

5. 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).

4. 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).

3. 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).

2. 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).

1. 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.

Technical Notes

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

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

Oral communications

Gatto L. Spatial proteomics: Combining experimental and annotation data to predict protein sub-cellular localisation., 13 Jan 2015, European Bioconductor Developer meeting, EMBL, Heidelberg.

Gatto L. An overview of the (growing) R/Bioc ecosystem for mass spectrometry communications and proteomics MRC Clinical Sciences Centre, Imperial College London, 5 December 2014, London.

Gatto L. Computational Challenges in Mass Spectrometry-Based Spatial Proteomics, 8 October 2014, Madrid, HUPO meeting.

Petyuk V., Gatto L., Lin Pedersen T., Payne S. and Smith R. MSnID: A Convenience Tool for Handling MS/MS Identifications in R/Bioconductor, 6 - 9 April, Seattle, US HUPO 2014.

Gatto L. Tracking proteins inside cells, 30 Jan 2014, Trinity College Science Society, Cambridge, UK (invited talk).

Breckels LM., Lilley KS., Gatto L. Bioconductor tools for high-throughput spatial proteomics European Bioconductor Developers' Meeting 9-10 Dec 2013, Cambridge, UK.

Gatto L., Breckels LM. and Lilley KS. Computational anaylses of high-throughput spatial proteomics data Bioconductor conference, 18 - 19 July 2013, Fred Hutchinson Cancer Research Center - Seattle, WA (invited talk).

Gatto L. and Lilley K.S. Unified omics data containers for robust, transparent communications and flexible computational biology PROSPECTOM Workshop 29 – 30 November 2012, Grenoble, France.

Gatto L. Proteomics with R Medizinisches Proteom-Center (MPC), Ruhr-Universitat Bochum, 16th July 2012 (invited talk).

Lilley K.S., Christoforou A., Gatto L., Groen A., Mulvey C., Simpson L. and Trotter M.W.B. Organelle Proteomics: tools to establish a global picture of protein subcellular localisation Keystone Symposium on Proteomics, Interactomes, 7 - 12 May 2012, Stockholm, Sweden.

Gatto L. and Christoforou A. Quantitative labelled MS2 proteomics: from data structure to QC European Bioconductor Developer Meeting, 8 - 9 December 2011, Manchester, UK (invited talk).

Gatto L. MS-based proteomics using Bioconductor Computational Statistics for Genome Biology, 1st July 2011, Brixen, Italy.

Gatto L. and Lilley K.S. Statistics and experimental design Proteomics Methods Forum, 16th June 2011, Newcastle, UK.

Gatto L. Mass Spectrometry and Proteomics/Metabolomics using R and Bioconductor Bioconductor Developer Meeting Europe, EMBL Heidelberg, 17 - 18 November 2010. (invited talk, This is a short introduction talk on mass spectrometry that aimed at setting stage the talks of the proteomics session into context.)

Gatto L. Labelled quantitative proteomics with MSnbase Bioconductor Developer Meeting Europe, EMBL Heidelberg, 17-18 November 2010. (invited talk)

Gatto L. Sub-cellular localisation of proteins with pRoloc Bioconductor Developer Meeting Europe, EMBL Heidelberg, 17-18 November 2010. (invited talk)

Gatto L. and Laes J-F. How to objectively and efficiently assess the inter-lab quality of microarray data MGED10/AMATA7 meeting, Brisbane, Australia, 3-5 Septembre 2007.

Gatto L., Catanzaro D. and Milinkovitch M. C. Validity of the GTR nucleotide substitution model in simulated and real data 9th Evolutionary Biology Meeting, Marseille, France, 21-23 September 2005.


Gibb, S., Breckels LM, Lin Pedersen T., Petyuk VA., Lilley KS. and Gatto L. A current perspective on using R and Bioconductor for proteomics data analysis 5 - 8 October 2014, Madrid, HUPO meeting.

Gatto L., Breckels LM., Naake T., Wieczorek S., Burger T. and Lilley KS. A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data 5 - 8 October 2014, Madrid, HUPO meeting.

Lin Pedersen T., Gatto L., Petyuk V., Kim S. MSGFplus and MSGFgui: R Packages for Interpretation of MS/MS Data, 6 - 9 April, Seattle, US HUPO 2014.

Gatto L., Gibb S., Breckels LM., Christoforou A. and Lilley KS. Using R and Bioconductor for proteomics data analysis [pdf and github] Bioconductor conference, 18 - 19 July 2013, Fred Hutchinson Cancer Research Center - Seattle, WA.

Shliaha P.V, Gatto L., Bond N.J., Mueller M., Capuano F., Raiser M. and Lilley K.S. A novel approach to improve quantitation accuracy and proteome coverage upon application of TWIMS (Travelling wave ion mobility mass spectrometry). 61st ASMS Conference, 9 - 13 June 2013, Minneapolis, MN.

Rees J.S., Gatto L., Howard J.A., Lilley K.S. and Jackson A.P. SILAC-iPAC: a quantitative method for exploring genuine components of protein complexes and interactomes by parallel affinity capture. Weak Protein-Ligand Interactions: New Horizons in Biophysics and Cell Biology 14 - 18 October 2012, Beijing, China

Gardiner M.D., Gatto L. and Nagase N. Difference gel electrophoresis analysis of cartilage degradation in a mouse model of OA identifies changes in key molecular processes World Congress on Osteoarthritis, 15 - 18 September 2011, San Diego, CA, USA.

Gatto L., Simpson L.M, Trotter M.W.B. and Lilley K.S. pRoloc - A unifying bioinformatics framework for organelle proteomics HUPO 10th Annual World Congress, 4 - 7 September 2011, Geneva, Switzerland.

Groen A.J., Sancho-Andres G., Gatto L., Charles P.D., Aniento F. and Lilley K.S Identification of new marker proteins of the Trans Golgi Network in Arabidopsis thaliana HUPO 10th Annual World Congress, 4 - 7 September 2011, Geneva, Switzerland.

Simpson L.M, Groen A.J., Gatto L., Lilley K.S and Trotter M.W.B. A Pattern Identification Framework for Gradient-based Organelle Proteomics Data HUPO 10th Annual World Congress, 4 - 7 September 2011, Geneva, Switzerland.

Gatto L. and Lilley K.S. Towards reproducible MSMS data preprocessing, quality control and quantification BSPR/EBI Proteomics Meeting, Hinxton, United Kingdom, 13-15 July 2010, poster available from Nature Precedings.   

Flamant L., Laes J-F., Gatto L. , Raes M. and Michiels C. Markers of chemotherapy resistance of breast cancer cells under hypoxic conditions Oncogenes and Human Cancer: The Next 25 Years, Madrid, Spain, 3-6 October 2007.

Schretter C., Gatto L. and Milinkovitch M. C. OligoFaktory : A Web Portal for Interactive Oligonucleotide Design Benelux Bioinformatics Conference, Ghent, Belgium, 14-15 April 2005.