Label-free quantification of shotgun proteomics data is a frequently used strategy, offering high dynamic range, sensitivity, and the ability to compare a high number of samples without additional labeling effort. Here, we present a bioinformatics approach that significantly improves label-free quantification results. We employ Percolator to assess the quality of quantified peptides. This allows to extract accurate and reliable quantitative results based on false discovery rate. Benchmarking our approach on previously published public data shows that it considerably outperforms currently available algorithms. apQuant is available free of charge as a node for Proteome Discoverer.
Keywords: computational proteomics; data analysis pipeline; label-free quantification; mass spectrometry; quantitative proteomics.