Publications

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Publications

  1. Cottret, L.,  Frainay, C.,  Chazalviel, M., Cabanettes F., Gloaguen, Y.,  Camenen, E.,  Merlet, B. et.al. MetExplore: collaborative edition and exploration of metabolic networks. Nucleic Acids Research 2018; DOI: 10.1093/nar/gky301
  2. 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.
  3. Rueedi, R., Mallol, R., Raffler, J., Lamparter, D., Friedrich, N., Vollenweider, P., Waeber, G., Kastenmüller, G,, Kutalik, Z. and Bergmann, SMetabomatching: 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
  4. Chazalviel, M., Frainay, C., Poupin, N., Vinson, F., Merlet, B., Gloaguen, Y., Cottret, L. and Jourdan,F. MetExploreViz: web component for interactive metabolic network visualizationBioinformatics. 2017; DOI:10.1093/bioinformatics/btx588.
  5. 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
  6. 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
  7. 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
  8. 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 solutionsMetabolomics. 2017;13(7):79. DOI:10.1007/s11306-017-1213-z
  9. Cacciatore, S., Tenori, L., Luchinat, C., Bennett, P.R and MacIntyre, D.A. KODAMA: an R package for knowledge discovery and data miningBioinformatics. 2017 15;33(4):621-623. DOI:10.1093/bioinformatics/btw705
  10. 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
  11. Haug, K, Salek, R.M. and Steinbeck, C. Global open data management in metabolomicsCurr Opin Chem Biol.2017. DOI: 10.1016/j.cbpa.2016.12.024
  12. 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
  13. 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
  14. 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 EpidemiologyJ Proteome Res. 2016 Sep 15. DOI: 10.1021/acs.jproteome.6b00125
  15. Saccenti, E., Menichetti, G., Ghini, V., Remondini, D., Tenori, L., and Luchinat, C. (2016) EntropyBased Network Representation of the Individual Metabolic PhenotypeJ Proteome Res. Sep 2; 15(9): 3298-307. DOI:10.1021/acs.jproteome.6b00454
  16. 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
  17. 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 MetabolicNetworksFront Mol Biosci., Feb 16; 3:2. DOI:  10.3389/fmolb.2016.00002
  18. 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

  1. 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).
  2. Philippe Rocca-Sera. PhenoMeNal: Virtual e-Infrastructure supporting Clinical Research and Metabolism Studies. OeRC News. (November 2016)
  3. SNIC Science Cloud – Virtual Research Environments for Clinical Metabolomics (April 2016)
  4. Namrata Kale, Christoph Steinbeck and PhenoMeNal Consortium. PhenoMeNal—An e-infrastructure for analysis of metabolic phenotype data. Metabolomics Spotlight, MetaboNews (January, 2016).
  5. Toxalim – The European project PhenoMeNal (Horizon 2020) launched on September 1st, 2015
  6. Life Science Sweden (Swedish Newspaper) – Tar fram infrastruktur för metabolomik (September 2015)
  7. SciLifeLab – PhenoMeNal: a gateway to personalised medicine (September 2015)
  8. Uppsala Universitet – PhenoMeNal: The gateway to individually adapted medication (September 2015)
  9. DTL – PhenoMeNal project to build an e-infrastructure for clinical metabolomics data (September 2015)
  10. EMBL-EBI Press release – Phenomenal: a gateway to personalised medicine (September 2015)
  11. Namrata Kale and Christoph Steinbeck. PhenoMeNal: towards an e-Infrastructure for pheno- and genotyping data. EGI (2015).