Publications
- Peters, K., Worrich, A., Weinhold A. et.al. Current Challenges in Plant Eco-Metabolomics. Int J Mol Sci. 2018 May 6;19(5). pii: E1385.DOI: 10.3390/ijms19051385
- DOI: 10.1093/nar/gky301
- Rosato, A., Tenori, L., Cascante, M., De Atauri Carulla, P.R., Martins Dos Santos, V.A.P., and Saccenti, E. From correlation to causation: analysis of metabolomics data using systems biology approaches. Metabolomics. 2018;14(4):37. doi: 10.1007/s11306-018-1335-y. Epub 2018 Feb 27.
- Rueedi, R., Mallol, R., Raffler, J., Lamparter, D., Friedrich, N., Vollenweider, P., Waeber, G., Kastenmüller, G,, Kutalik, Z. and Bergmann, S. Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput Biol. 2017 Dec 1;13(12):e1005839; DOI:10.1371/journal.pcbi.1005839
- Chazalviel, M., Frainay, C., Poupin, N., Vinson, F., Merlet, B., Gloaguen, Y., Cottret, L. and Jourdan,F. MetExploreViz: web component for interactive metabolic network visualization. Bioinformatics. 2017; DOI:10.1093/bioinformatics/btx588.
- Schober, D., Jacob, D., Wilson, M., Cruz, J.A., Marcu, A., Grant, J.R., Moing, A. et.al. nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data. Anal Chem. 2018 Jan 2;90(1):649-656; DOI:10.1021/acs.analchem.7b0279
- van Rijswijk, M., Beirnaert, C., Caron, C. et al.The future of metabolomics in ELIXIR . F1000Research 2017, 6(ELIXIR):1649. DOI: 10.12688/f1000research.12342.1
- Meier, R., Ruttkies, C., Treutler,H. , and Neumann, S. Bioinformatics can boost metabolomics research. J Biotechnol. 2017 May 26. pii: S0168-1656(17)30253-5. DOI:10.1016/j.jbiotec.2017.05.018
- Herman, S., Emami Khoonsari, P., Aftab, O., Krishnan, S., Strömbom, E., Larsson, R., Hammerling, U., Spjuth, O., Kultima, K., and Gustafsson, M. Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions. Metabolomics. 2017;13(7):79. DOI:10.1007/s11306-017-1213-z
- Cacciatore, S., Tenori, L., Luchinat, C., Bennett, P.R and MacIntyre, D.A. KODAMA: an R package for knowledge discovery and data mining. Bioinformatics. 2017 15;33(4):621-623. DOI:10.1093/bioinformatics/btw705
- Takis, P.G., Tenori, L., Ravera, E. and Luchinat, C. Gelified Biofluids for High-Resolution Magic Angle Spinning 1H NMR Analysis: The Case of Urine. Anal Chem. 2017 89(2):1054-1058.DOI:10.1021/acs.analchem.6b04318
- Haug, K, Salek, R.M. and Steinbeck, C. Global open data management in metabolomics. Curr Opin Chem Biol.2017. DOI: 10.1016/j.cbpa.2016.12.024
- 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 deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes. BMC Bioinformatics. 2017. DOI: 10.1186/s12859-017-1513-3
- Bandrowski, A., Brinkman, R., Brochhausen, M., Brush, M.H, Bug, B., Chibucos, M.C., Clancy, K. et al. The Ontology for Biomedical Investigations. PLoS One. 2016 Apr 29;11(4):e0154556. DOI:10.1371/journal.pone.0154556
- Karaman, I., Ferreira, D.L., Boulange, C.L., Kaluarachchi, M.R., Herrington, D., Dona, A.C, Castagné, R. et al. (2016) A Workflow For Integrated Processing of Multi-Cohort Untargeted 1H NMR Metabolomics Data In Large ScaleMetabolic Epidemiology. J Proteome Res. 2016 Sep 15. DOI: 10.1021/acs.jproteome.6b00125
- Saccenti, E., Menichetti, G., Ghini, V., Remondini, D., Tenori, L., and Luchinat, C. (2016) Entropy–Based Network Representation of the Individual Metabolic Phenotype. J Proteome Res. Sep 2; 15(9): 3298-307. DOI:10.1021/acs.jproteome.6b00454
- 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
- Merlet, B., Paulhe N., Vinson, F., Frainay, C., Chazalvie,l M., Poupin, N., Gloaguen, Y. et al. (2016) A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale MetabolicNetworks. Front Mol Biosci., Feb 16; 3:2. DOI: 10.3389/fmolb.2016.00002
- 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
Newsletter, Articles and News
- Ola Spjuth and Anders Larsson.Deploying PhenoMeNal virtual research environments on the EGI Federated Cloud. EGI (2018)
- Ralf J. M. Weber, Mark R. Viant, James Bradbury, Philippe Rocca-Serra, Pablo Moreno, Namrata Kale, Claire O’Donovan, Christoph Steinbeck, and PhenoMeNal Consortium. PhenoMeNal—An easy to use, scalable and cloud-based e-infrastructure to process and analyse metabolomics data. Metabolomics Spotlight, MetaboNews (October, 2017).
- Philippe Rocca-Sera. PhenoMeNal: Virtual e-Infrastructure supporting Clinical Research and Metabolism Studies. OeRC News. (November 2016)
- SNIC Science Cloud – Virtual Research Environments for Clinical Metabolomics (April 2016)
- Namrata Kale, Christoph Steinbeck and PhenoMeNal Consortium. PhenoMeNal—An e-infrastructure for analysis of metabolic phenotype data. Metabolomics Spotlight, MetaboNews (January, 2016).
- Toxalim – The European project PhenoMeNal (Horizon 2020) launched on September 1st, 2015
- Life Science Sweden (Swedish Newspaper) – Tar fram infrastruktur för metabolomik (September 2015)
- SciLifeLab – PhenoMeNal: a gateway to personalised medicine (September 2015)
- Uppsala Universitet – PhenoMeNal: The gateway to individually adapted medication (September 2015)
- DTL – PhenoMeNal project to build an e-infrastructure for clinical metabolomics data (September 2015)
- EMBL-EBI Press release – Phenomenal: a gateway to personalised medicine (September 2015)
- Namrata Kale and Christoph Steinbeck. PhenoMeNal: towards an e-Infrastructure for pheno- and genotyping data. EGI (2015).