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

By David D. Shteynberg, Eric W. Deutsch, Henry H. N. Lam, Jimmy K. Eng, Zhi Sun, Natalie I. Tasman, Luis Mendoza, Rob Moritz, R. Aebersold, Alexey I. Nesvizhskii. Molecular & Cellular Proteomics, 2011

533 citations recorded by the source corpus.

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 organism-specific composite data sets.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/84578ee8c68ecdfab7c0f85bfd5e576b5b20347c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Spectroscopy

Citations in the corpus

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets (2015, background): “Although other more sophisticated strategies exist for calculating protein scores (22, 28), using the best peptide hit or the sum of peptide scores are common practice in the proteomics community.”
Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database (2012, background): “A related but different problem arises when combining the search scores from multiple search engines obtained for the same data set (33, 34).”
Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics (2015, methodology): “The iProphet tool [25] takes one or more pepXML files from PeptideProphet and refines the probabilities based on many lines of corroborating evidence.”
Proteogenomics: concepts, applications, and computational strategies (2014, background): “…a particular ‘event’ (such as a novel coding region or a splice junction) is identified from multiple peptide ions and/ or multiple unique peptides, the posterior probabilities of the supporting identifications can in principle be combined to calculate the probability score for that event 53,54 .”
DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015, methodology): “As previously shown for DDA data, combining DIA pseudo-MS/ MS search results from multiple search engines with iProphet 24 led to a consistent increase in the number of peptide ions and proteins identified at a given FDR ( Supplementary Table 1 ).”