@article{SteinfathGaertnerLisecetal.2009,
  author    = {Steinfath, Matthias and G{\"a}rtner, Tanja and Lisec, Jan and Meyer, Rhonda Christiane and Altmann, Thomas and Willmitzer, Lothar and Selbig, Joachim},
  title     = {Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers},
  series = {Theoretical and applied genetics : TAG ; international journal of plant breeding research},
  volume    = {120},
  journal   = {Theoretical and applied genetics : TAG ; international journal of plant breeding research},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {0040-5752},
  doi       = {10.1007/s00122-009-1191-2},
  pages     = {239 -- 247},
  year      = {2009},
  abstract  = {A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80\% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.},
  language  = {en}
}
@article{KempaHummelSchwemmeretal.2009,
  author    = {Kempa, Stefan and Hummel, Jan and Schwemmer, Thorsten and Pietzke, Matthias and Strehmel, Nadine and Wienkoop, Stefanie and Kopka, Joachim and Weckwerth, Wolfram},
  title     = {An automated GCxGC-TOF-MS protocol for batch-wise extraction and alignment of mass isotopomer matrixes from differential C-13-labelling experiments : a case study for photoautotrophic-mixotrophic grown Chlamydomonas reinhardtii cells},
  issn      = {0233-111X},
  doi       = {10.1002/jobm.200800337},
  year      = {2009},
  abstract  = {Two dimensional gas chromatography coupled to time-of-flight mass spectrometry (GCxGC-TOF-MS) is a promising technique to overcome limits of complex metabolome analysis using one dimensional GC-TOF-MS. Especially at the stage of data export and data mining, however, convenient procedures to cope with the complexity of GCxGC-TOF-MS data are still in development. Here, we present a high sample throughput protocol exploiting first and second retention index for spectral library search and subsequent construction of a high dimensional data matrix useful for statistical analysis. The method was applied to the analysis of 13 C-labelling experiments in the unicellular green alga Chlamydomonas reinhardtii. We developed a rapid sampling and extraction procedure for Chlamydomonas reinhardtii laboratory strain (CC503), a cell wall deficient mutant. By testing all published quenching protocols we observed dramatic metabolite leakage rates for certain metabolites. To circumvent metabolite leakage, samples were directly quenched and analyzed without separation of the medium. The growth medium was adapted to this rapid sampling protocol to avoid interference with GCxGC-TOF-MS analysis. To analyse batches of samples a new software tool, MetMax, was implemented which extracts the isotopomer matrix from stable isotope labelling experiments together with the first and second retention index (RI1 and RI2). To exploit RI1 and RI2 for metabolite identification we used the Golm metabolome database (GMD [1] with RI1/ RI2-reference spectra and new search algorithms. Using those techniques we analysed the dynamics of (CO2)-C-13 and C-13- acetate uptake in Chlamydomonas reinhardtii cells in two different steady states namely photoautotrophic and mixotrophic growth conditions.},
  language  = {en}
}