The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein-level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.

The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.

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...

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include mass spectrometry to define protein sequence, protein:protein interactions, and protein post-translational modifications. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative mass spectrometry proteomics. It supports all major operating systems and instrument vendors via open data formats. Here we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of tandem mass spectrometry datasets, as well as some major upcoming features.

Summary The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell-type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations and post-translational modifications. The data is freely accessible as a resource at www.srmatlas.org, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

Epithelial-mesenchymal transition (EMT) is a highly con- Epithelial-mesenchymal transition (EMT)1 is a cellular process whereby otherwise sessile epithelial cells undergo a shift in plasticity and acquire the ability to disseminate (1-6). Hallmarks of EMT include diminished expression of cell-cell contact and adhesion components (e.g. E-cadherin), diminished expression of cell-matrix components, decreased expression of components involved in cell polarity, elevated expression of proteins involved in cytoskeleton remodelling (e.g. vimentin), and increased expression of various matrix metalloproteinFrom the ‡Department of Biochemistry,

A crucial component of the analysis of shotgun proteomics datasets is the search engine, an algorithm that attempts to identify the peptide sequence from the parent molecular ion that produced each fragment ion spectrum in the dataset. There are many different search engines, both commercial and open source, each employing a somewhat different technique for spectrum identification. The set of high-scoring peptide-spectrum matches for a defined set of input spectra differs markedly among the various search engine results; individual engines each provide unique correct identifications among a core set of correlative identifications. This has led to the approach of combining the results from multiple search engines to achieve improved analysis of each dataset. Here we review the techniques and available software for combining the results of multiple search engines and briefly compare the relative performance of these techniques. The most commonly used proteomics approach, shotgun proteomics, has become an invaluable tool for the highthroughput characterization of proteins in biological samples (1). This workflow relies on the combination of protein digestion, liquid chromatography (LC) 1 separation, tandem mass spectrometry (MS/MS), and sophisticated data analysis in its aim to derive an accurate and complete set of peptides and their inferred proteins that are present in the sample being studied. Although many variations are possible, the typical workflow begins with the digestion of proteins into peptides with a protease, typically trypsin. The resulting peptide mixture is first separated via LC and then subjected to mass spectrometry (MS) analysis. The MS instrument acquires fragment ion spectra on a subset of the peptide precursor ions that it measures. From the MS/MS spectra that measure the abundance and mass of the peptide ion fragments, peptides present in the mixture are identified and proteins are inferred by means of downstream computational analysis.The informatics component of the shotgun proteomics workflow is crucial for proper data analysis (2), and a wide variety of tools have emerged for this purpose (3). The typical informatics workflow can be summarized in a few steps: conversion from vendor proprietary formats to an open format, high-throughput interpretation of the MS/MS spectra with a search engine, and statistical validation of the results with estimation of the false discovery rate at a selected score threshold. Various tools for measuring relative peptide abundances may be applied, dependent on the type of quantitation technique applied in the experiment. Finally, the proteins present, and their abundance in the sample, are inferred based on the peptide identifications.One of the most computationally intensive and diverse steps in the computational analysis workflow is the use of a search engine to interpret the MS/MS spectra in order to determine the best matching peptide ion identifications (4), termed peptide-spectrum matches (PSMs). There are three main types of engines: sequence searc...

The kidney, urine, and plasma proteomes are intimately related: proteins and metabolic waste products are filtered from the plasma by the kidney and excreted via the urine, while kidney proteins may be secreted into the circulation or released into the urine. Shotgun proteomics datasets derived from human kidney, urine, and plasma samples were collated and processed using a uniform software pipeline, and relative protein abundances were estimated by spectral counting. The resulting PeptideAtlas builds yielded 4005, 2491, and 3553 nonredundant proteins at 1% FDR for the kidney, urine, and plasma proteomes, respectively—for kidney and plasma, the largest high-confidence protein sets to date. The same pipeline applied to all available human data yielded a 2013 Human PeptideAtlas build containing 12,644 nonredundant proteins and at least one peptide for each of ~14,000 Swiss-Prot entries, an increase over 2012 of ~7.5% of the predicted human proteome. We demonstrate that abundances are correlated between plasma and urine, examine the most abundant urine proteins not derived from either plasma or kidney, and consider the biomarker potential of proteins associated with renal decline. This analysis forms part of the Biology and Disease-driven Human Proteome Project (B/D-HPP) and a contribution to the Chromosome-centric Human Proteome Project (C-HPP) special issue.