Getting Started in Computational Mass Spectrometry–Based Proteomics
Figure 2
Example of a proteomic workflow using database-based identification and label-free quantification.
(A) Identification of MS/MS spectra. Experimental spectra are compared to peptides in a database, and the best-scoring PSMs are reported while controlling the FDR. Protein sequences are identified from the peptides. (B) Label-free quantification. Features in LC-MS runs (shown with circles) are located, quantified, and aligned across runs. (C) LC-MS features are annotated with peptide sequences when identifications are available (shown with filled circles). The annotations are used to optimize the alignment of features across runs. The list of quantified, identified, and aligned features is then subjected to transformation, normalization, and summarization. (D) The list of features is used as input to machine learning, functional annotation, and data integration steps.