Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data Mining
With the advances in analytical techniques and data mining, the chances to elucidate a plant metabolome and understand the metabolite variation in response to external stimuli such as stress are gradually becoming feasible in a global manner. This approach represents a very important resea...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
Swiss Chemical Society
2005-06-01
|
| Series: | CHIMIA |
| Subjects: | |
| Online Access: | https://chimia.ch/chimia/article/view/4005 |
| _version_ | 1811320014184120320 |
|---|---|
| author | Aly Thiocone Elia Grata Julien Boccard Pierre-Alain Carrupt Serge Rudaz Jean-Luc Wolfender |
| author_facet | Aly Thiocone Elia Grata Julien Boccard Pierre-Alain Carrupt Serge Rudaz Jean-Luc Wolfender |
| author_sort | Aly Thiocone |
| collection | DOAJ |
| description |
With the advances in analytical techniques and data mining, the chances to elucidate a plant metabolome and understand the metabolite variation in response to external stimuli such as stress are gradually becoming feasible in a global manner. This approach represents a very important
research challenge because of the structural diversity of the metabolites and the convolute nature of biological matrices. Based on a new collaborative framework emerging from the recent creation of the EPGL, a project aimed at the development of methodology for the treatment of complex metabolomic
datasets with data mining has been initiated with the expertise of different EPGL laboratories. In this paper the strategies used for the study of the metabolic variations in a biological model (the effect of mechanical wounding on Arabidopsis thaliana) are described. Metabolite profiling
based on micro-extraction and LC/MS analysis with various detection methods has been used. Data were parsed in the form of ion maps and treatment of this complex set of MS information was performed with dedicated bioinformatic tools. This approach should enable the evaluation of metabolome
variations in a comprehensive manner for a better understanding of complex biological mechanisms and for the detection of novel bioactive molecules.
|
| first_indexed | 2024-04-13T12:53:02Z |
| format | Article |
| id | doaj.art-6455055bedad4a489b891c220cb39421 |
| institution | Directory Open Access Journal |
| issn | 0009-4293 2673-2424 |
| language | deu |
| last_indexed | 2024-04-13T12:53:02Z |
| publishDate | 2005-06-01 |
| publisher | Swiss Chemical Society |
| record_format | Article |
| series | CHIMIA |
| spelling | doaj.art-6455055bedad4a489b891c220cb394212022-12-22T02:46:09ZdeuSwiss Chemical SocietyCHIMIA0009-42932673-24242005-06-0159610.2533/000942905777676425Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data MiningAly ThioconeElia GrataJulien BoccardPierre-Alain CarruptSerge RudazJean-Luc Wolfender With the advances in analytical techniques and data mining, the chances to elucidate a plant metabolome and understand the metabolite variation in response to external stimuli such as stress are gradually becoming feasible in a global manner. This approach represents a very important research challenge because of the structural diversity of the metabolites and the convolute nature of biological matrices. Based on a new collaborative framework emerging from the recent creation of the EPGL, a project aimed at the development of methodology for the treatment of complex metabolomic datasets with data mining has been initiated with the expertise of different EPGL laboratories. In this paper the strategies used for the study of the metabolic variations in a biological model (the effect of mechanical wounding on Arabidopsis thaliana) are described. Metabolite profiling based on micro-extraction and LC/MS analysis with various detection methods has been used. Data were parsed in the form of ion maps and treatment of this complex set of MS information was performed with dedicated bioinformatic tools. This approach should enable the evaluation of metabolome variations in a comprehensive manner for a better understanding of complex biological mechanisms and for the detection of novel bioactive molecules. https://chimia.ch/chimia/article/view/4005Arabidopsis thalianaData miningLc/msMetabolomic |
| spellingShingle | Aly Thiocone Elia Grata Julien Boccard Pierre-Alain Carrupt Serge Rudaz Jean-Luc Wolfender Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data Mining CHIMIA Arabidopsis thaliana Data mining Lc/ms Metabolomic |
| title | Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data Mining |
| title_full | Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data Mining |
| title_fullStr | Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data Mining |
| title_full_unstemmed | Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data Mining |
| title_short | Synergy at the 'Ecole de Pharmacie Genève-Lausanne': Methodology Developments for the Treatment of Complex Metabolomic Datasets with Data Mining |
| title_sort | synergy at the ecole de pharmacie geneve lausanne methodology developments for the treatment of complex metabolomic datasets with data mining |
| topic | Arabidopsis thaliana Data mining Lc/ms Metabolomic |
| url | https://chimia.ch/chimia/article/view/4005 |
| work_keys_str_mv | AT alythiocone synergyattheecoledepharmaciegenevelausannemethodologydevelopmentsforthetreatmentofcomplexmetabolomicdatasetswithdatamining AT eliagrata synergyattheecoledepharmaciegenevelausannemethodologydevelopmentsforthetreatmentofcomplexmetabolomicdatasetswithdatamining AT julienboccard synergyattheecoledepharmaciegenevelausannemethodologydevelopmentsforthetreatmentofcomplexmetabolomicdatasetswithdatamining AT pierrealaincarrupt synergyattheecoledepharmaciegenevelausannemethodologydevelopmentsforthetreatmentofcomplexmetabolomicdatasetswithdatamining AT sergerudaz synergyattheecoledepharmaciegenevelausannemethodologydevelopmentsforthetreatmentofcomplexmetabolomicdatasetswithdatamining AT jeanlucwolfender synergyattheecoledepharmaciegenevelausannemethodologydevelopmentsforthetreatmentofcomplexmetabolomicdatasetswithdatamining |