Spectral prediction features as a solution for the search space size problem in proteogenomics

Proteogenomics approaches often struggle with the distinction between right and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for spectral matching in a proteogenomic context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator post-processing tool for protein databases constructed from RNA-seq and ribosome profiling analyses. The presented results provide evidence that this approach enhances the peptide identification power in a proteogenomic setting and in the meantime they lead to the validation of new proteoforms with elevated stringency. In this online repository, we submitted the conventional proteomic search results with MaxQuant against the custom nanopore RNA-seq-based search space. All other results can be found in the supplemental materials of the manuscript, in SRA (sequencing data) or under ProteomeXChange Project PXD011353 (as this is original data from a previuos paper).