iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates

Abstract

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline , it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organismspecific composite data sets. A combination of protein digestion, liquid chromatography and tandem mass spectrometry (LC-MS/MS) 1 , often referred to as shotgun proteomics, has become a robust and powerful proteomics technology. Protein samples are digested into peptides, typically using trypsin. The resulting peptides are then separated and subjected to mass spectrometric (MS) analysis, whereby a subset of the available precursor ions are sampled by the MS instrument, isolated and further fragmented in the gas phase to generate fragment ion spectra (MS/MS spectra). From these spectra, the peptides and then the proteins present in the sample and, in conjunction with quantification strategies, their relative or absolute quantities can be determined (1).The volume of data generated in proteomic experiments has been growing steadily over the past decade. This has been aided by the rapid progress made in several facets of proteomics technology, including improved sample preparation and labeling techniques and faster, more sen...

Modulation of the Chromatin Phosphoproteome by the Haspin Protein Kinase

“…The data were searched allowing phosphorylation (ϩ79.9663 Daltons) of serine, threonine, and tyrosine as a variable modification and carboxy-amidomethylation of cysteine (ϩ57.0214 Daltons) residues as a fixed modification. Finally, The identification results were statistically analyzed with the PeptideProphet algorithm (v 4.6) (27) and the results combined using the iProphet algorithm (28). In all the data sets presented, the FDR was maintained below 1%, this was based on the number of the decoy hits at the PeptideProphet cut-off score used.…”

Inference and Validation of Protein Identifications

“…Other approaches perform inference in a single step by jointly fitting a probabilistic model to establish peptide spectrum matches and protein identifications at the same time (8). To benefit from multiple database search engines, a recently proposed method performs protein inference from a list of nonredundant peptides (31). Spectral alignment approaches take a special position and start off from the raw mass spectrometrical data and de novo assemble (partial) protein sequences by aligning fragment ion spectra of overlapping peptides without resorting to sequence databases (32).…”

Isolation of the protein and RNA content of active sites of transcription from mammalian cells

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