Rocca-Serra, P., Salek, R.M., Arita, M., Correa, E., Dayalan, S., Gonzalez-Beltran A., Ebbels T. et al. (2016) Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics,12(1):14. DOI: 10.1007/s11306-015-0879-3
Merlet, B., Paulhe N., Vinson, F., Frainay, C., Chazalvie,l M., Poupin, N., Gloaguen, Y. et al. (2016) A ComputationalSolution to AutomaticallyMapMetaboliteLibraries in the Context of GenomeScaleMetabolicNetworks. Front Mol Biosci., Feb 16; 3:2. DOI: 10.3389/fmolb.2016.00002
Blaise, B.J., Correia, G., Tin, A., Young, J.H., Vergnaud, A.C., Lewis, M., Pearce, J.T. et al. (2016) Power Analysis and Sample Size Determination in Metabolic Phenotyping.Anal Chem. May 17; 88(10): 5179-88. DOI:10.1021/acs.analchem.6b00188
Saccenti, E., Menichetti, G., Ghini, V., Remondini, D., Tenori, L., and Luchinat, C. (2016) Entropy–BasedNetworkRepresentation of the IndividualMetabolicPhenotype. J Proteome Res. Sep 2; 15(9): 3298-307. DOI:10.1021/acs.jproteome.6b00454
Karaman, I., Ferreira, D.L., Boulange, C.L., Kaluarachchi, M.R., Herrington, D., Dona, A.C, Castagné, R. et al. (2016) A Workflow For IntegratedProcessing of Multi-CohortUntargeted1H NMRMetabolomicsData In LargeScaleMetabolicEpidemiology. J Proteome Res. 2016 Sep 15. DOI: 10.1021/acs.jproteome.6b00125
Selivanov, V.A., Benito, A., Miranda, A., Aguilar, E., Polat I.H., Centelles, J.J., Jayaraman, A., Lee, P.W., Marin, S. and Cascante, M. MIDcor, an R-program for decipheringmassinterferences in massspectra of metabolitesenriched in stableisotopes. BMC Bioinformatics. 2017. DOI: 10.1186/s12859-017-1513-3
The Workflow4Metabolomics computational infrastructure: meeting the workflow challengeMetaboMeeting, December 2015, Cambridge.
MetExplore and Genome-Scale metabolic networks, MetaboMeeting, December 2015, Cambridge, UK
PhenoMeNal H2020 project: A gateway to personalised medicine, Metabolic phenotyping in clinical practice, May 2016, Barcelona. The workshop was hosted by the PhenoMeNal consortium to define the state-of-the-art in Metabolic Phenotyping in the clinic. The event was attended by a mixed audience from medical/clinical, industry or commercial, government, working or collaborating using metabolomics data and tools and technologies related to clinic. Click here to view
PhenoMeNal: A comprehensive and standardised e-infrastructure for analysing medical phenotype data, PhenoMeNal Industry Workshop, June 2016, Dublin. The workshop was hosted during the Metabolomics Conference in Dublin with an aim to create a lasting interaction with the industy, in order to raise awareness of the PhenoMeNal initiative and to ensure optimal interoperability of PhenoMeNal infrastructure and instrument vendor’s data formats and tools. Click here to view
Deciphering cancer metabolism using NMR,8th GERMN / 5th Iberian NMR Meeting, June 2016, Valencia, Spain.
PhenoMeNal: Large Scale Computing for Medical Metabolomics, Industry Programme Quarterly Meeting, September 2016, EMBL-EBI, UK.The presentation was part of the quarterly meeting for EMBL-EBI Industry Programme. Click here to view
PhenoMeNal: Large Scale Computing for Medical Metabolomics, Genome Campus Software Craftsmanship Community, 2016, EMBL-EBI, UK. The presentation describes the work done in the context of containerising metabolomics software tools and running them on top of a container orchestrator environment (Google Kubernetes), a cluster for containers that can be deployed on scalable infrastructure. Click here to view.
PhenoMeNal – Large scale computing for medical metabolomics, RCUK Cloud Working Group Workshop, November 2016. The workshop was intended to bring together researchers and technical specialists to share expertise in the application of cloud computing technology for the research community. Click here to view.