The Service Center BioInfra.Prot provides numerous bioinformatics tools and workflows to answer common questions regarding identification, quantification and statistical analysis of investigated molecules (proteins) in the field of medicine and life sciences.
In addition, we have been providing tailor-made bioinformatics solutions including machine learning and support in the interpretation of results for scientific publications (Turewicz et al. 2017). Please feel free to contact us at any time. Within the scope of our tool and workflow development we pay great attention to user-friendliness. Therefore we offer our tools and workflows mostly for different software environments (R, Java, KNIME, etc.). The preferred environment for users with little or no background in bioinformatics is KNIME, a free software for interactive data analysis, which allows the integration of numerous methods through its modular pipelining concept.
Workflow name: PIA
PIA is a toolbox for MS based protein inference and identification analysis. PIA allows you to inspect the results of common proteomics spectrum identification search engines, combine them semlessly and conduct statistical analyses. The main focus of PIA lays on the integrated inference algorithms, i.e. concluding the proteins from a set of identified spectra. But it also allows you to inspect your peptide spectrum matches, calculate FDR values across different search engine results and visualize the correspondence between PSMs, peptides and proteins.
Workflow name: Calibra Curve
A highly useful and flexible tool for calibration of targeted MS‐based measurements. CalibraCurve enables an automated batch-mode determination of dynamic linear ranges and quantification limits for both targeted proteomics and similar assays. The software uses a variety of measures to assess the accuracy of the calibration and provides intuitive visualizations.
Workflow name: Differential analysis of protein data
This workflow compares protein measurements of two sample groups using a t-test. The resulting p-values are corrected for multiple testing and visualized together with the fold changes in a volcano plot.