Abstract
In this study we investigated the performance of a computational pipeline for protein identification and label free quantification (LFQ) of LC-MS/MS data sets from experimental animal tissue samples, as well as the impact of its specific peptide search combinatorial approach. The full pipeline workflow was composed of peptide search engine adapters based on different identification algorithms, in the frame of the open-source OpenMS software running within the KNIME analytics platform. Two different in silico tryptic digestion, database-search assisted approaches (X!Tandem and MS-GF+), de novo peptide sequencing based on Novor and consensus library search (SpectraST), were tested for the processing of LC-MS/MS raw data files obtained from proteomic LC-MS experiments done on proteolytic extracts from mouse ex vivo liver samples. The results from proteomic LFQ were compared to those based on the application of the two software tools MaxQuant and Proteome Discoverer for protein inference and label-free data analysis in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD025097.