Software

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, if class labels are inhomogeneously distributed between cohorts, their accuracy may drop. Flimma addresses this issue by implementing the state-of-the-art workflow limma voom in a privacy-preserving manner, i.e. patient data never leaves its source site. Flimma results are identical to those generated by limma voom on combined datasets even in imbalanced scenarios where meta-analysis approaches fail. More

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.More

ProteomicsDB was initially developed to enable the real-time exploration of large collections of quantitative mass spectrometry-based proteomics data. Today, it hosts and provides access to a large variety of different data types such as RNA-Seq expression data (transcriptomics), drug-target interactions (drugomics), cell line viability data (phenomics), protein turnover data, meltome data, and protein-protein-interaction data. The central goal of ProteomicsDB is to provide online tools (analytics) to allow the real-time interaction with large amounts of integrated data as exemplified by its interactive heat map functionality as well as drug sensitivity prediction.More

In bottom-up mass-spectrometry-based proteomics, the identification and quantification of peptides for protein inference and abundance estimation heavily rely on sequence database searching or spectral library matching. In order to realize the full potential of these approaches, accurate models are required that can predict the fragment ion intensities and retention time of peptides. Prosit, a deep neural network, is such a model that was trained on data from the ProteomeTools project. Integrating Prosit into database search pipelines can lead to more identifications at >10× lower false discovery rates. Additionally, Prosit can be used to generate in silico spectral libraries for the analysis of data-independent acquisition experiments.More