Uwe Schmitt

TL;DR: OpenMS 2.0 is presented, a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data.

TL;DR: It is shown that optimal tradeoffs between sensitivity and specificity require careful considerations of the relationship between proteins in the samples and proteins represented in the spectral library, and proposed the application of a global analyte constraint to prevent the accumulation of false positives across large-scale data sets.

TL;DR: The pyOpenMS framework allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++.

TL;DR: The algorithm DIANA and the classifier PyProphet are presented, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis, and are capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset.

TL;DR: In this paper, a dynamic inverse problem with temporal a priori information is studied, where the investigated object is allowed to change during the measurement procedure, and temporal smoothness is used as a quite general, but for many applications sufficient, a priora information is required.