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PowderCell 2.0 for Windows
(1998)
PowderCell contains a comfortable, user friendly visualization and modification tool for crystal structures. It provides on-line calculation of the corresponding powder diffraction patterns simulating a variety of experimental conditions. The common ICSD and Shelx file formats are supported for importing crystal structure information. It has control of automatic cell transformation and also derivation of subgroups. More than 740 different settings of the 230 space-group types are supported. Up to ten crystal structures can be considered simultaneously. A full pattern refinement enables the direct comparison with experimental diffractograms for quantitative phase analysis, lattice parameter refinement, polynomial background estimation, etc.
Gas chromatography using atmospheric pressure chemical ionization coupled to mass spectrometry (GC/APCI-MS) is an emerging metabolomics platform, providing much-enhanced capabilities for structural mass spectrometry as compared to traditional electron ionization (EI)-based techniques. To exploit the potential of GC/APCI-MS for more comprehensive metabolite annotation, a major bottleneck in metabolomics, we here present the novel R-based tool InterpretMSSpectrum assisting in the common task of annotating and evaluating in-source mass spectra as obtained from typical full-scan experiments. After passing a list of mass-intensity pairs, InterpretMSSpectrum locates the molecular ion (M0), fragment, and adduct peaks, calculates their most likely sum formula combination, and graphically summarizes results as an annotated mass spectrum. Using (modifiable) filter rules for the commonly used methoximated-trimethylsilylated (MeOx-TMS) derivatives, covering elemental composition, typical substructures, neutral losses, and adducts, InterpretMSSpectrum significantly reduces the number of sum formula candidates, minimizing manual effort for postprocessing candidate lists. We demonstrate the utility of InterpretMSSpectrum for 86 in-source spectra of derivatized standard compounds, in which rank-1 sum formula assignments were achieved in 84% of the cases, compared to only 63% when using mass and isotope information on the M0 alone. We further use, for the first time, automated annotation to evaluate the purity of pseudospectra generated by different metabolomics preprocessing tools, showing that automated annotation can serve as an integrative quality measure for peak picking/deconvolution methods. As an R package, InterpretMSSpectrum integrates flexibly into existing metabolomics pipelines and is freely available from CRAN (https://cran.r-project.org/).
Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.
The test material EDS-TM001 together with an accompanying software package, “EDX spectrometer check”, have been made available in 2009 by BAM to be employed by EDS (energy-dispersive X-ray spectrometer) users to check the performance of an EDS attached to the SEM. Particularly for test laboratories operating under accreditation schemes like ISO/IEC 17025, a periodical control of the critical instrumental parameters in end-user laboratories is required. With EDS-TM001 or EDS-TM002 (second generation) test material, this periodical check is simplified to the acquisition of only one 10 kV spectrum. The software “EDX spectrometer check” is destined to evaluate automatically this spectrum and determine the performance of the EDS in terms of energy resolution and calibration, as well as possible alteration of low-energy Efficiency due to detector contamination. Energy resolution can be compared with the specified values according to the international ISO standard ISO 15632:2012. EDS-TM is a synthetic material consisting of a 6 μm thick layer of C, Al, Mn, Cu and Zr deposited on a steel (in case of EDS-TM001) or silicon (in case of EDS-TM002) substrate. The chemical composition of EDS-TM was chosen such as to give nearly equal intensities of the low energy lines in a 10 kV spectrum, thus, making it very sensitive against spectrometer efficiency changes. Meanwhile, about 150 laboratories use the EDS-TM001 or EDS-TM002 test material for the periodical check of their EDS. A detailed description of the test material and software together with examples of application was published recently. New results and gained experiences will be presented as well. When the FWHM of the X-ray lines in the EDS-TM spectrum are determined, the spectrum background must be subtracted accurately. The applied physical background subtraction procedure is robust and takes into account the transmission of the detector window. While the previous version considers only Moxtek AP windows, the new version includes selection of silicon Nitride window and the case of windowless detector. Moreover, the new version allows importing of spectra in Bruker spx-format and EMSA/MSA files from EDAX TEAM software.
Windowless detectors have been also tested demonstrating long-term stability after repeated heating and cooling cycles. In this case, the appropriate Background subtraction is decisive for accurate detector characterization. Detailed results will be presented.
No matter whether you are doing X-ray diffraction or scattering, at wide or small angles, in a lab or at a synchrotron, you will need a bespoke sequence of up to twenty different corrections before it gets about right (at our latest count). Our library now does this automatically and reliably to get you the data you deserve – in absolute units and with uncertainty estimates – without the pain. This talk will highlight the development of the modular library, the sequence and its foundation, show its applicability to real-world datasets, and highlight a possible way forward
Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN (https://cran.r-project.org/) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays.
Although metaproteomics, the study of the collective proteome of microbial communities, has become increasingly powerful and popular over the past few years, the field has lagged behind on the availability of user-friendly, end-to-end pipelines for data analysis. We therefore describe the Connection from two commonly used metaproteomics data processing tools in the field, MetaProteomeAnalyzer and PeptideShaker, to Unipept for downstream analysis.
Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation.