Label-Free Quantitative Shotgun Proteomics Using Normalized Spectral Abundance Factors

Abstract

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.

Quantitative proteomic analysis of paired colorectal cancer and non-tumorigenic tissues reveals signature proteins and perturbed pathways involved in CRC progression and metastasis

“…This was enabled using subsequent data handling in the R Scrappy analysis pipeline as previously described [30]. Several variables were tested to identify differentially expressed proteins: CRC tumor versus non-tumor tissues, early versus late stage CRC tumor tissues, and EGFR + versus EGFR − CRC tissues.…”

“…Reverse database searching (decoy) was used to estimate the false discovery rate (FDR) of peptide identifications (calculated at b 1%). The peptide/protein identification were further analyzed using various in-house and online functional annotation and bioinformatics tools for protein pathway analysis including Scrappy software package [30], PloGO R package [31], Ingenuity Pathway Analysis (IPA) (http://www.ingenuity.com/products/ipa), DAVID Functional Annotation Tool (http://david.abcc.ncifcrf.gov/) and KEGG pathways (http://www.genome.jp/kegg/pathway.html). All proteomics data presented in this work have been deposited to the ProteomeXchange with identifier PXD001676.…”

The generation gap: Proteome changes and strain variation during encystation in Giardia duodenalis

“…Statistical analysis was performed between cysts and trophozoites of each strain, as well as cysts from both strains using the Scrappy software package [28]. Student's t tests were performed on log-transformed NSAF data to determine statistical significance of differential abundance, and proteins with a t test p-value less than 0.05 were considered to be differentially expressed.…”

“…The 9 lists of identified proteins from the merged output of the GPM constituted low-stringency protein and peptide information, which was transformed into lists of high stringency reproducibly identified proteins using the Scrappy software package [28,29]. Proteins were defined as reproducibly identified only when present in all three replicates with a total spectral count (SpC) of ≥5 [29].…”

“…To calculate NSAF values the spectral count (the total number of MS/MS spectra) was divided by the protein length (L), and normalized to the sum of SpC/L for all proteins in the experiment. To compensate for null values and to allow log transformation prior to statistical analysis, a spectral fraction of 0.5 was added to all SpC values [28].…”