Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatographymass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. Meanwhile its data processing remains tedious and dependent on user interventions.As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to automatically 1/23 . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/138503 doi: bioRxiv preprint first posted online May. 16, 2017; analyze 1D NMR spectra uses wavelets to efficiently summarize the raw spectra with minimal information loss or user interaction. The tool offers a fast and easy workflow that starts with the common approach of peak-picking, followed by grouping. This yields a matrix consisting of features, samples and peak values that can be conveniently processed either by using included multivariate statistical functions or by using many other recently developed methods for NMR data analysis. speaq 2.0 facilitates robust and high-throughput metabolomics based on 1D NMR but is also compatible with other NMR frameworks or complementary LC-MS workflows. The methods are benchmarked using two publicly available datasets. speaq 2.0 is distributed through the existing speaq R package to provide a complete solution for NMR data processing. The package and the code for the presented case studies are freely available on CRAN (https://cran.r-project.org/package=speaq) and GitHub (https://github.com/beirnaert/speaq).
Author summaryWe present speaq 2.0: a user friendly workflow for processing NMR spectra quickly and easily. By limiting the need for user interaction and allowing the construction of workflows by combining R functions, metabolomics data analysis becomes fully reproducible and shareable. Such advances are critical for the future of the metabolomics field as it needs to move towards a fully open-science approach. This is no trivial goal as many researchers are still using black-box commercial software that often requires manually doing several steps, thus hampering reproducibility. To encourage the shift towards open source, we deliberately made our method usable for anyone with the most basic of R experience, something that is easily acquired. speaq 2.0 allows a stand-alone analysis from spectra to statistical analysis.In addition, the package can be combined with existing tools to improve performance, as it offers a superior peak picking method compared to the standard binning approach.
PostersAfter treating the ground cells three times with methanol (1 g cells with 10 vol of methanol) at 95°C for 1 h, the polysaccharides were extracted by stirring the cells with water (1 g of cells with 20 vol of water) at 4°C for 18 h. The extract was centrifuged and treated with 4 vol of aqueous 96% ethanol. The crude polysaccharides were collected. The extraction of the cells was repeated two times. All precipitates were dehydrated with diethyl ether to give an amorphous yellow-brown powder.The latter is soluble in water, non-reducing, gives no colour with iodine, and contains proteins. The polysaccharide content was about 14% (DW) for callus and hairy roots.The crude polysaccharides were hydrolysed with 2 N H2504 at 100°C for 6h. After cooling the acid was neutralized with BaCO3. The residue was analysed by PC, TLC, and HPLC.
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