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Eingeladener Vortrag
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Im Rahmen der von der BAM und anderen Partnern ins Leben gerufenen Initiative „Digitale Qualitätsinfrastruktur“ liegt der Fokus u.a. auf der Entwicklung von neuen Zertifizierungsworkflows. Dabei ist eine besondere Bedeutung dem Zusammenführen von Operational Technology (OT) und Informationstechnik (IT) beizumessen. Zur Datenintegration aus der Sensor-Feldebene einer Versuchs-wasserstofftankstelle wurde eine solche Infrastruktur zunächst in einer Laborumgebung aufgebaut.
The statistical tool eCerto was developed for the evaluation of measurement data to assign property values and associated uncertainties of reference materials. The analysis is based on collaborative studies of expert laboratories and was implemented using the R software environment. Emphasis was put on comparability of eCerto with SoftCRM, a statistical tool based on the certification strategy of the former Community Bureau of Reference. Additionally, special attention was directed towards easy usability from data collection through processing, archiving, and reporting. While the effects of outlier removal can be flexibly explored, eCerto always retains the original data set and any manipulation such as outlier removal is (graphically and tabularly) documented adequately in the report. As a major reference materials producer, the Bundesanstalt für Materialforschung und -prüfung (BAM) developed and will maintain a tool to meet the needs of modern data processing, documentation requirements, and emerging fields of RM activity. The main features of eCerto are discussed using previously certified reference materials.
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.
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.
McSAS3 is a refactored software package for fitting large batches of (X-ray or Neutron) scattering data. It uses a Monte-Carlo acceptance-rejection algorithm to optimize model parameters - ideal for analysis of size-disperse scatterers.
The refactored code can exploit multiprocessing, traceably stores (multiple) results in the output file, and allows for re-histogramming of previous optimizations. Besides analysis of large batches, it can also be integrated in automated data processing pipelines.
The live demonstration will show how to use the software, what its limitations are, and what outcomes can look like for batches of results.
Simulates X-ray and Neutron scattering patterns from arbitrary shapes defined by STL files.
Features:
- Uses multithreading to compute a number of independent solutions, then uses the variance of the results to estimate an uncertainty on the output.
- Can be launched from the command line using an excel sheet to define settings, or from a jupyter notebook.
- Outputs scattering patterns in absolute units if the contrast is set.
- A Gaussian size distribution is available, where the relative scaling of objects for each repetion can be varied. Recommended to be used with limited width (max. 10%) to avoid artefacts.
- Writes results with settings to an archival HDF5 file.
Application examples:
This software has been used in several studies to date. For example, it has been used here to simulate a model scattering pattern for a cuboid shape, which was then fed forward into the McSAS3 analysis program for analyzing scattering patterns of polydisperse cuboids. A second use is here, where it was used for the modeling of flattened helices. In this paper, scattering pattern features could be matched with particular morphological changes in the structure. Lastly, this paper has an example where it was used to validate the analytical analysis model, and explore the realistic limits of application of the analytical model.
The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.
IsoCor
(2022)
Despite numerous advantages offered by hyphenation of chromatography and electrokinetic separation methods with multicollector (MC) ICP-MS for isotope analysis, the main limitation of such systems is the decrease in precision and increase in uncertainty due to generation of short transient signals. To minimize this limitation, most authors compare several isotope ratio calculation methods and establish a multi-step data processing routine based on the precision and accuracy of the methods. However, to the best of our knowledge, there is no universal data processing tool available that incorporates all important steps of the treatment of the transient signals. Thus, we introduce a data processing application (App) IsoCor that facilitates automatic calculation of isotope ratios from transient signals and eases selection of the most suitable method. The IsoCor App performs baseline subtraction, peak detection, mass bias correction, isotope ratio calculation and delta calculation. The feasibility and reliability of the App was proven by reproducing the results from isotope analysis of three elements (neodymium, mercury and sulfur) measured on-line via hyphenated systems. The IsoCor App provides trackability of the results to ensure quality control of the analysis.
ABID
(2022)
In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context.
MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour
(2022)
During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used 35 monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied to the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 60 min and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 degrees C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context.
A software toolbox is introduced that addresses several needs common to computed tomography (CT). Built for the WIPANO CTSimU project to serve as the reference implementation for its image processing and evaluation tasks, it provides a Python 3 interface that is adaptable to many conceivable applications. Foremost, the toolbox features a pipeline architecture for sequential 2D image processing tasks, such as flat field corrections and image binning, and enables the user to create their own processing modules. Beyond that, it provides means to measure line profiles and image quality assessment algorithms to calculate modulation transfer functions (MTF) or to determine the interpolated basic spatial resolution (iSRb) using a duplex wire image. It can also be used to calculate projection matrices for the reconstruction of scans with arbitrary industrial CT geometries and trajectories. The CTSimU project defined a framework of projection- and volume-based test scenarios for the qualification of radiographic simulation software towards its use in dimensional metrology. The toolbox implements the necessary evaluation routines and generates reports for all projection-based tests.
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
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.
The SPONGE
(2020)
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
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.