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A crucial part of a successful systems biology experiment is an assay that provides reliable, quantitative measurements for each of the components in the system being studied. For proteomics to be a key part of such studies, it must deliver accurate quantification of all the components in the system for each tested perturbation without any gaps in the data. This will require a new approach to proteomics that is based on emerging targeted quantitative mass spectrometry techniques. The PeptideAtlas Project comprises a growing, publicly accessible database of peptides identified in many tandem mass spectrometry proteomics studies and software tools that allow the building of PeptideAtlas, as well as its use by the research community. Here, we describe the PeptideAtlas Project, its contents and components, and show how together they provide a unique platform to select and validate mass spectrometry targets, thereby allowing the next revolution in proteomics.

Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

High-throughput peptide synthesis and mass spectrometry are used to generate a near-complete reference map of the Saccharomyces cerevisiae proteome; two versions of the map (supporting discovery- and hypothesis-driven proteomics) are then applied to a protein-based quantitative trait locus analysis. A global map of yeast proteins Complete 'gold standard' reference maps of the components within a system are valuable resources for a research community. This paper presents one such resource, a complete mass-spectrometric reference map of the budding yeast Saccharomyces cerevisiae . The map comes in two versions — one for discovery-driven (shotgun) and the other for hypothesis-driven (targeted) proteomic measurements — and will support most studies performed with contemporary proteomic technologies. The maps provide essentially a set of highly specific assays for the detection and quantification of every yeast protein in any sample, and their value is demonstrated here in a protein quantitative trait locus analysis. Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage 1 , 2 , 3 . Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) 3 , 4 and the other supporting hypothesis-driven (targeted) 5 , 6 proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis 7 , which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

The ProteomeTools project provides the proteomics community with a physical synthetic tryptic peptide resource and a digital LC-MS/MS data resource covering all human proteins. We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography–tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org ) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.

A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson's disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson's disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer's, Huntington's, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Proteins by numbers Absolute protein quantification via the measurement of protein copy numbers has the potential to provide important information about biological processes, but apart from the special case of brewers yeast, this has not proved possible using standard proteomics procedures. Now, with the leptospirosis pathogen Leptospira interrogans as the first target, a new mass spectrometry-based strategy that should be generally applicable to many other biological systems has been used to determine absolute protein abundance for a significant fraction of a proteome. The results show how L. interrogans adapts to changing environments by adjusting proteome homeostasis while the total protein copy numbers remain constant. Absolute protein concentration measurements of a considerable fraction of the proteome have, until now, only been derived from genetically altered Saccharomyces cerevisiae cells, using a technique that is not directly portable from yeast to other species. A mass-spectrometry-based method is now used to determine the absolute protein abundance for a significant fraction of the proteome of the human pathogen Leptospira interrogans . Mass-spectrometry-based methods for relative proteome quantification have broadly affected life science research. However, important research directions, particularly those involving mathematical modelling and simulation of biological processes, also critically depend on absolutely quantitative data—that is, knowledge of the concentration of the expressed proteins as a function of cellular state. Until now, absolute protein concentration measurements of a considerable fraction of the proteome (73%) have only been derived from genetically altered Saccharomyces cerevisiae cells 1 , a technique that is not directly portable from yeast to other species. Here we present a mass-spectrometry-based strategy to determine the absolute quantity, that is, the average number of protein copies per cell in a cell population, for a large fraction of the proteome in genetically unperturbed cells. Applying the technology to the human pathogen Leptospira interrogans , a spirochete responsible for leptospirosis 2 , we generated an absolute protein abundance scale for 83% of the mass-spectrometry-detectable proteome, from cells at different states. Taking advantage of the unique cellular dimensions of L. interrogans , we used cryo-electron tomography morphological measurements to verify, at the single-cell level, the average absolute abundance values of selected proteins determined by mass spectrometry on a population of cells. Because the strategy is relatively fast and applicable to any cell type, we expect that it will become a cornerstone of quantitative biology and systems biology.

Spectral searching, based on matching experimental peptide spectra to reference spectral libraries, is gaining interest as an alternative to traditional sequence-database searching in mass spectrometry–based proteomics. A software tool, SpectraST, now allows users to build their own high-quality spectral libraries from raw data. Spectral searching has drawn increasing interest as an alternative to sequence-database searching in proteomics. We developed and validated an open-source software toolkit, SpectraST, to enable proteomics researchers to build spectral libraries and to integrate this promising approach in their data-analysis pipeline. It allows individual researchers to condense raw data into spectral libraries, summarizing information about observed proteomes into a concise and retrievable format for future data analyses.

A broad range of mass spectrometers are used in mass spectrometry (MS)-based proteomics research. Each type of instrument possesses a unique design, data system and performance specifications, resulting in strengths and weaknesses for different types of experiments. Unfortunately, the native binary data formats produced by each type of mass spectrometer also differ and are usually proprietary. The diverse, nontransparent nature of the data structure complicates the integration of new instruments into preexisting infrastructure, impedes the analysis, exchange, comparison and publication of results from different experiments and laboratories, and prevents the bioinformatics community from accessing data sets required for software development. Here, we introduce the 'mzXML' format, an open, generic XML (extensible markup language) representation of MS data. We have also developed an accompanying suite of supporting programs. We expect that this format will facilitate data management, interpretation and dissemination in proteomics research.

Understanding how proteins and their complex interaction networks convert the genomic information into a dynamic living organism is a fundamental challenge in biological sciences. As an important step towards understanding the systems biology of a complex eukaryote, we cataloged 63% of the predicted Drosophila melanogaster proteome by detecting 9,124 proteins from 498,000 redundant and 72,281 distinct peptide identifications. This unprecedented high proteome coverage for a complex eukaryote was achieved by combining sample diversity, multidimensional biochemical fractionation and analysis-driven experimentation feedback loops, whereby data collection is guided by statistical analysis of prior data. We show that high-quality proteomics data provide crucial information to amend genome annotation and to confirm many predicted gene models. We also present experimentally identified proteotypic peptides matching ∼50% of D. melanogaster gene models. This library of proteotypic peptides should enable fast, targeted and quantitative proteomic studies to elucidate the systems biology of this model organism.