Skip to main content

Topic: Post-doc position at CEA (France) in LC-MS signal processing (Read 4861 times) previous topic - next topic

Post-doc position at CEA (France) in LC-MS signal processing
   Health and information technologies are of major focus at CEA (French Alternative Energies and Atomic Energy Commission), which is a prominent player in European technological research ( Mathematics, life sciences and computing are part of the interdisciplinary scientific expertise at the Saclay research center (5,000 researchers and collaborators). Applied research in the Laboratory of Data Analysis Tools aims at developing innovative mathematical approaches to deal with large volume and high complexity of "omic" data, in particular those originating from mass spectrometry (e.g. in proteomics and metabolomics). In particular, our lab is a member of the national MetaboHUB infrastructure in metabolomics.
   Detection of chemical contaminants (pesticides, drug residuals, adulterations) is of major importance in food industry. Contaminations of raw materials (e.g. milk) propagate exponentially to all end-products, with devastating health and economic consequences. Whereas current screening relies on a few MS/MS methods that are specific to a single molecule, new "non-targeted" approaches are required which would use the full mass spectrum of the sample to simultaneously identify all molecules of interest by comparison with a reference database of spectra, and even detect the presence of unknown, potentially hazardous molecules (Garcia-Reyes et al., 2007).
   Metabolomics by liquid chromatography coupled to mass spectrometry has emerged in the last few years as a promising technology for non-targeted detection of small molecules (Werner et al., 2008). Because of the size of the data generated by high-resolution modern instruments (~100 Mo/sample) and their complexity (both time and mass dimensions), signal processing algorithms are critical for comprehensive and robust extraction of molecular information (Katajama and Oresic, 2007).

   Garcia-Reyes et al. (2007). Comprehensive screening of target, non-target and unknown pesticides in food by LC-TOF-MS. TrAC Trends in Analytical Chemistry, 26:828-841.
   Katajamaa M. and Oresic M. (2007). Data processing for mass spectrometry-based metabolomics. Journal of Chromatography A, 1158:318-328.
   Werner et al. (2008). Mass spectrometry for the identification of the discriminating signals from metabolomics: Current status and future trends. Journal of Chromatography B, 871:143-163.

   Within a large industrial and academic collaborative study aimed at untargeted screening for food products, our contribution focuses on LC-MS signal processing and data analysis. First, signal processing must be optimized for detection of trace contaminants. The XCMS package of the R software is widely used in metabolomics for peak detection and retention time alignment (Smith et al., 2006). Despite the strengths of the algorithm, peak detection is not always optimal when using classical parameters, leading to many false positive which, in turn, mask putative contaminant signals in subsequent statistical analysis. In addition, automatic assessment of experiment quality, efficient normalization (to avoid batch effects or analytical drift) and retention time correction (for inter-sample or inter-experiment comparison) are critical for reliable detection of trace signal.
   Second, statistical methods will be developed to design the database of reference spectra and to identify outlier samples (Charlton et al., 2008). In the case of unknown contaminants, structural information provided by the high resolution (e.g. tabulated mass differences between peaks signing known chemical functions) and public chemical database query will help to characterize potentially hazardous molecules.
   To be successful, the developed methods will have to be implemented as software tools which are fast, readily handled and interpreted by the end-user (e.g. biologist). Beyond the detection of food contaminants, the methods developed in the project will be of high interest within the metabolomic community.

   Charlton et al. (2008). Non-targeted detection of chemical contamination in carbonated soft drinks using NMR spectroscopy, variable selection and chemometrics. Analytica Chimica Acta, 618:196-203.
   Smith et al. (2006). XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Analytical Chemistry, 78:779-787.

   Job description:
   In our laboratory, the post holder will develop new approaches for fast signal processing (baseline removal, smoothing, 2D peak picking, time alignment) and data analysis (classification, biostatistics, integration of heterogeneous data) in the context of innovative LC-MS approaches. The corresponding software tools will have to handle data from various technological platforms, and will be integrated within a common framework. Both R and C/C++ languages will be used.

   Interested applicants should hold a Ph.D. in applied mathematics, and be highly motivated by multidisciplinary team work aiming at industrial and clinical applications. The candidate should have presented his Ph.D. thesis for less than two years before the starting date and should have a good record of scientific publications.

   Our offer:
1-year contract (renewable once).
Net salary: about 2200 €/month, depending on experience.

Please send your CV and your letter of motivation to:
Etienne Thévenot
Laboratory of Data Analysis Tools
Mail: etienne(dot)thevenot(at)cea(dot)fr
  • CEA