PNNL

Abstract

To identify peptides and proteins from the large number of fragmentation spectra in mass spectrometrybased proteomics, researches commonly employ so called database search engines. Additionally, postprocessors like Percolator have been used on the results from such search engines, to assess confidence, infer peptides and generally increase the number of identifications. A recent search engine, MS-GF+, has previously been showed to out-perform these classical search engines in terms of the number of identified spectra. However, MS-GF+ generates only limited statistical estimates of the results, hence hampering the biological interpretation. Here, we enabled Percolator-processing for MS-GF+ output, and observed an increased number of identified peptides for a wide variety of datasets. In addition, Percolator directly reports false discovery rate estimates, such as q values and posterior error probabilities, as well as p values, for peptide-spectrum matches, peptides and proteins, functions useful for the whole proteomics community.