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Light utilization is the vital first step of photosynthesis for photoautotrophic organisms. Boosting the growth and yield of photosynthetic organisms is critical in sustainable food and biofuel pro...

Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to mass spectra deposited to public repositories or contained in public spectral libraries. Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.

Edible bird’s nest (EBN) is a glue-like substance deriving from salivary secretion by specific swiftlets, and protein is considered as the main component of EBN. Accounting over 50% by weight, the exact identities of EBN proteins are still not well understood, due to difficulties of extraction, purification and identification. By using EBN proteins as antigens, 31 monoclonal antibodies specifically against the proteins were generated. The proteins of EBN were subjected to identification by shotgun proteomics. Six protein identities were revealed, including acidic mammalian chitinase (AMCase)-like, mucin 5AC-like and ovoinhibitor-like proteins. In parallel, the monoclonal antibodies were used to immunoprecipitate proteins from EBN extract, and subsequently the precipitated product(s) was identified. AMCase-like protein was most frequently precipitated by the antibodies. The existence of AMCase-like protein in EBN was further verified by: (i) recognition of chicken AMCase by our anti-EBN antibodies; and (ii) recognition of EBN AMCase-like protein by a commercial anti-AMCase antibody. The antibody was highly sensitive and selective to AMCase-like protein in EBN products, with limit of detection at 0.01 μg/mL in ELISA test. Thus, AMCase-like protein, or its antibody, could be used as a new quality control marker for EBN.

Recently, data-independent acquisition (DIA) MS has gained popularity as a qualitative-quantitative workflow for proteomics. One outstanding problem in the analysis of DIA-MS data is alignment of chromatographic retention times across multiple samples, which facilitates peptide identification and accurate quantification. Here, we present a novel hybrid (profile-based and feature-based) algorithm for LC-MS alignment and test it on sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) (a type of DIA) data. Our algorithm uses a profile-based dynamic time warping algorithm to obtain a coarse alignment and corrects large retention time shifts, and then uses a feature-based bipartite matching algorithm to match feature to feature at a fine scale. We evaluated our method by comparing our aligned feature pairs to peptide identification results of pseudo-MS2 spectra exported by DIA-Umpire, a recently reported tool for deconvoluting DIA-MS data. We proposed that our method can be used to align DIA-MS data prior to identification, and the alignment can be used to delete noise peaks or screen for differentially changed features. We found that a simple alignment-enabled denoising scheme can reduce the number of pseudo-MS2 spectra exported by DIA-Umpire by up to around 40%, while retaining a comparable number of identifications. Finally, we demonstrated the utility of our tool for accurate label-free relative quantification across multiple SWATH runs.

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 are freely accessible as a resource at http://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.

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.

Identifying the species on which hematophagous arthropods feed is crucial for studying the factors that affect pathogen distributions and that can aid public health. Here we describe a protocol to identify the species a parasitic arthropod has previously fed upon by identifying the source of the remnants of a previous blood meal via shotgun proteomics and spectral matching. The protocol is a nontargeted approach that uses the entire detected blood proteome for source identification; it does not require a priori knowledge of genome or protein sequences. Instead, reference spectral libraries are compiled from the blood of multiple host species by using SpectraST, which takes ~4 d; the identification of the species from which a previous blood meal of a hematophagous arthropod was taken is achieved with spectral matching against the reference spectral libraries, which takes approximately another 4 d. This method is robust against random degradation of the blood meal and can identify unknown blood remnants months after the feeding event.

Spectral library searching is a maturing approach for peptide identification from MS/MS, offering an alternative to traditional sequence database searching. Spectral library searching relies on direct spectrum-to-spectrum matching between the query data and the spectral library, which affords better discrimination of true and false matches, leading to improved sensitivity. However, due to the inherent diversity of the peak location and intensity profiles of real spectra, the resulting similarity score distributions often take on unpredictable shapes. This makes it difficult to model the scores of the false matches accurately, necessitating the use of decoy searching to sample the score distribution of the false matches. Here, we refined the similarity scoring in spectral library searching to enable the validation of spectral search results without the use of decoys. We rank-transformed the peak intensities to standardize all spectra, making it possible to fit a parametric distribution to the scores of the nontop-scoring spectral matches. The statistical significance of the top-scoring match can then be estimated in a rigorous manner according to Extreme Value Theory. The overall result is a more robust and interpretable measure of the quality of the spectral match, which can be obtained without decoys. We tested this refined similarity scoring function on real datasets and demonstrated its effectiveness. This approach reduces search time, increases sensitivity, and extends spectral library searching to situations where decoy spectra cannot be readily generated, such as in searching unidentified and nonpeptide spectral libraries.

With the rapid accumulation of data from shotgun proteomics experiments, it has become feasible to build comprehensive and high-quality spectral libraries of tandem mass spectra of peptides. A spectral library condenses experimental data into a retrievable format and can be used to aid peptide identification by spectral library searching. A key step in spectral library building is spectrum denoising, which is best accomplished by merging multiple replicates of the same peptide ion into a consensus spectrum. However, this approach cannot be applied to "singleton spectra," for which only one observed spectrum is available for the peptide ion. We developed a method, based on a Bayesian classifier, for denoising peptide tandem mass spectra. The classifier accounts for relationships between peaks, and can be trained on the fly from consensus spectra and immediately applied to denoise singleton spectra, without hard-coded knowledge about peptide fragmentation. A linear regression model was also trained to predict the number of useful "signal" peaks in a spectrum, thereby obviating the need for arbitrary thresholds for peak filtering. This Bayesian approach accumulates weak evidence systematically to boost the discrimination power between signal and noise peaks, and produces readily interpretable conditional probabilities that offer valuable insights into peptide fragmentation behaviors. By cross validation, spectra denoised by this method were shown to retain more signal peaks, and have higher spectral similarities to replicates, than those filtered by intensity only.

Intact glycopeptide MS analysis to reveal site-specific protein glycosylation is an important frontier of proteomics. However, computational tools for analyzing MS/MS spectra of intact glycopeptides are still limited and not well-integrated into existing workflows. In this work, a new computational tool which combines the spectral library building/searching tool, SpectraST (Lam et al. Nat. Methods 2008 , 5 , 873–875), and the glycopeptide fragmentation prediction tool, MassAnalyzer (Zhang et al. Anal. Chem. 2010 , 82 , 10194–10202) for intact glycopeptide analysis has been developed. Specifically, this tool enables the determination of the glycan structure directly from low-energy collision-induced dissociation (CID) spectra of intact glycopeptides. Given a list of possible glycopeptide sequences as input, a sample-specific spectral library of MassAnalyzer-predicted spectra is built using SpectraST. Glycan identification from CID spectra is achieved by spectral library searching against this library, in which both m / z and intensity information of the possible fragmentation ions are taken into consideration for improved accuracy. We validated our method using a standard glycoprotein, human transferrin, and evaluated its potential to be used in site-specific glycosylation profiling of glycoprotein datasets from LC-MS/MS. In addition, we further applied our method to reveal, for the first time, the site-specific N -glycosylation profile of recombinant human acetylcholinesterase expressed in HEK293 cells. For maximum usability, SpectraST is developed as part of the Trans-Proteomic Pipeline (TPP), a freely available and open-source software suite for MS data analysis.

Spectral library searching is a promising alternative to sequence database searching in peptide identification from MS/MS spectra. The key advantage of spectral library searching is the utilization of more spectral features to improve score discrimination between good and bad matches, and hence sensitivity. However, the coverage of reference spectral library is limited by current experimental and computational methods. We developed a computational approach to expand the coverage of spectral libraries with semi-empirical spectra predicted from perturbing known spectra of similar sequences, such as those with single amino acid substitutions. We hypothesized that the peptide of similar sequences should produce similar fragmentation patterns, at least in most cases. Our results confirm our hypothesis and specify when this approach can be applied. In actual spectral searching of real data sets, the sensitivity advantage of spectral library searching over sequence database searching can be mostly retained even when all real spectra are replaced by semi-empirical ones. We demonstrated the applicability of this approach by detecting several known non-synonymous single-nucleotide polymorphisms in three large human data sets by spectral searching.

The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of tandem mass spectrometry datasets. 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 dataset, demonstrating that the set up and use of the tools is straightforward and well supported and does not require specialized informatics resources or knowledge.

Electron transfer dissociation (ETD) is an alternative fragmentation technique to collision induced dissociation (CID) that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data, or the development of specialized tools. We have adapted the Trans-Proteomic Pipeline (TPP) to process ETD data. Specifically, we have added support for fragment ion spectra from high charge precursors, compatibility with charge-state estimation algorithms, provisions for the use of the Lys-C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing datasets from several different types of ETD instruments and demonstrate that application of the ETD-enhanced TPP can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100% over native output from a single sequence search engine.

We have developed and validated a ubiquitin (Ub) and ubiquitin-like protein (Ubl) spectral library, consisting of 467 consensus spectra (320 unique peptides derived from autophagy-related protein 8, F-adjacent transcript 10, interferon-stimulated gene 15 kDa protein, neural precursor cell expressed developmentally down-regulated protein 8, small ubiquitin-related modifiers 1-3 and Ub, and nine of the most commonly observed Ub/Ub1 chain linkages). The use of the Ub/Ub1 library with a spectral matching tool (SpectraST) yields improved performance over database search engines, and can successfully identify many types of Ub/ Ub1 chain-derived peptides that cannot be identified by standard database search algorithms.

Multiple reaction monitoring mass spectrometry (MRM-MS) is a targeted analysis method that has been increasingly viewed as an avenue to explore proteomes with unprecedented sensitivity and throughput. We have developed a software tool, called MaRiMba, to automate the creation of explicitly defined MRM transition lists required to program triple quadrupole mass spectrometers in such analyses. MaRiMba creates MRM transition lists from downloaded or custom-built spectral libraries, restricts output to specified proteins or peptides, and filters based on precursor peptide and product ion properties. MaRiMba can also create MRM lists containing corresponding transitions for isotopically heavy peptides, for which the precursor and product ions are adjusted according to user specifications. This open-source application is operated through a graphical user interface incorporated into the Trans-Proteomic Pipeline, and it outputs the final MRM list to a text file for upload to MS instruments. To illustrate the use of MaRiMba, we used the tool to design and execute an MRM-MS experiment in which we targeted the proteins of a well-defined and previously published standard mixture.

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.

Data processing is a central and critical component of a successful proteomics experiment, and is often the most time-consuming step. There have been considerable advances in the field of proteomics informatics in the past 5 years, spurred mainly by free and open-source software tools. Along with the gains afforded by new software, the benefits of making raw data and processed results freely available to the community in data repositories are finally in evidence. In this review, we provide an overview of the general analysis approaches, software tools, and repositories that are enabling successful proteomics research via tandem mass spectrometry.

The use of antibacterial drug combinations with synergistic effects is increasingly seen as a critical strategy to combat multi-drug resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). In this work, the proteome responses in MRSA under the stress of a sub-inhibitory dose of a synergistic drug combination of a novel erythromycin derivative, SIPI-8294 and oxacillin, were studied by label-free quantitative proteomics. Several control treatment groups were designed to isolate proteome responses potentially related to the synergy: (1) the non-synergistic drug combination of erythromycin and oxacillin, (2) SIPI-8294 only, (3) oxacillin only and (4) erythromycin only. Results showed that 200 proteins were differentially expressed in SIPI-8294/oxacillin-treated cells. Among these proteins, the level of penicillin binding protein 2a, the protein mainly responsible for oxacillin resistance in MRSA, was four times lower in the SIPI-8294/oxacillin group than in the erythromycin/oxacillin group, suggesting that SIPI-8294 may interfere with this known oxacillin resistance mechanism. Moreover, hierarchical clustering analysis of differentially expressed proteins under different treatments revealed that SIPI-8294/oxacillin elicits very different responses than the individual drugs or the non-synergistic erythromycin/oxacillin combination. Bioinformatic analysis indicated that the synergistic effect can be further traced to a disruption in oxidation-reduction homeostasis and cell wall biosynthesis.

A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same identifiable peptides by sequence database searching methods, which often are time-consuming and error-prone. A more precise and efficient method, in which previously observed and identified peptide MS/MS spectra are catalogued and condensed into searchable spectral libraries to allow new identifications by spectral matching, is seen as a promising alternative. To that end, an open-source, functionally complete, high-throughput and readily extensible MS/MS spectral searching tool, SpectraST, was developed. A high-quality spectral library was constructed by combining the high-confidence identifications of millions of spectra taken from various data repositories and searched using four sequence search engines. The resulting library consists of over 30,000 spectra for Saccharomyces cerevisiae. Using this library, SpectraST vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits. A unique advantage of SpectraST is its full integration into the popular Trans Proteomic Pipeline suite of software, which facilitates user adoption and provides important functionalities such as peptide and protein probability assignment, quantification, and data visualization. This method of spectral library searching is especially suited for targeted proteomics applications, offering superior performance to traditional sequence searching.

The enzyme glucose-6-phosphate dehydrogenase (G6PD) plays an important role in maintaining the level of NADPH and in producing pentose phosphates for nucleotide biosynthesis. It is also of great value as an analytical reagent, being used in various quantitative assays. In searching for new strategies to purify this enzyme, the partitioning of G6PD in two-phase aqueous mixed (nonionic/cationic) micellar systems was investigated both experimentally and theoretically. Our results indicate that the use of a two-phase aqueous mixed micellar system composed of the nonionic surfactant C10E4 (n-decyl tetra(ethylene oxide)) and the cationic surfactant CnTAB (alkyltrimethylammonium bromide, n = 8, 10, or 12) can improve significantly the partitioning behavior of G6PD relative to that obtained in the two-phase aqueous C10E4 micellar system. This improvement can be attributed to electrostatic attractions between the positively charged mixed (nonionic/cationic) micelles and the net negatively charged enzyme G6PD, resulting in the preferential partitioning of G6PD to the top, mixed micelle-rich phase of the two-phase aqueous mixed micellar systems. The effect of varying the cationic surfactant tail length (n = 8, 10, and 12) on the denaturation and partitioning behavior of G6PD in the C10E4 /CnTAB/buffer system was investigated. It was found that C8TAB is the least denaturing to G6PD, followed by C10TAB and C12TAB. However, the C10E4/C12TAB/buffer system generated stronger electrostatic attractions with the net negatively charged enzyme G6PD than the C10E4/C10TAB/buffer and the C10E4/C8TAB/buffer systems, when using the same amount of cationic surfactant. Overall, the two-phase aqueous mixed (C10E4/C10TAB) micellar system yielded the highest G6PD partition coefficient of 7.7, with a G6PD yield in the top phase of 71%, providing the optimal balance between the denaturing effect and the electrostatic attractions for the three cationic surfactants examined. A recently developed theoretical framework to predict protein partition coefficients in two-phase aqueous mixed (nonionic/ionic) micellar systems was implemented, and the theoretically predicted G6PD partition coefficients were found to be in reasonable quantitative agreement with the experimentally measured ones. © 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 82: 445–456, 2003.

A method for clustering billions of unidentified tandem mass spectra from shotgun proteomics experiments offers new ways of storing, organizing and analyzing proteomics data, with potential benefits to the entire proteomics community.

Marine bacteria are the most widely distributed organisms in the ocean environment and produce a wide variety of secondary metabolites. However, traditional screening for bioactive natural compounds is greatly hindered by the lack of a systematic way of cataloguing the chemical profiles of bacterial strains found in nature. Here we present a chemical fingerprint database of marine bacteria based on their secondary metabolite profiles, acquired by high-resolution LC-MS. Till now, 1,430 bacterial strains spanning 168 known species collected from different marine environments were cultured and profiled. Using this database, we demonstrated that secondary metabolite profile similarity is approximately, but not always, correlated with taxonomical similarity. We also validated the ability of this database to find species-specific metabolites, as well as to discover known bioactive compounds from previously unknown sources. An online interface to this database, as well as the accompanying software, is provided freely for the community to use.

Angelica Radix, the roots of the genus Angelica, has been used for more than 2,000 years as a traditional medicine in Eastern Asia. The Chinese Pharmacopoeia records more than 100 herbal formulae containing Angelica roots. There are two common sources of Angelica roots, Angelica sinensis from China and A. gigas from Korea. The two species of Angelica roots differ in their chemical compositions, pharmacological properties and clinical efficacy. 1H-NMR metabolic profiling has recently emerged as a promising quality control method for food and herbal chemistry. We explored the use of 1H-NMR metabolic profiling for the quality control of Angelica Radix. Unlike previous work, we performed the metabolic profiling on hot water extracts, so as to mimic the clinically relevant preparation method. Unsupervised principle component analyses of both the full spectral profile and a selection of targeted molecules revealed a clear differentiation of three types of Angelica roots. In addition, the levels of 13 common metabolites were measured. Statistically significant differences in the levels of glucose, fructose and threonine were found between different sources of Angelica. Ferulic acid, a marker commonly used to evaluate Angelica root, was detected in our samples, but the difference in ferulic acid levels between the samples was not statistically significant. Overall, we successfully applied 1H-NMR metabolic profiling with water extraction to discriminate all three sources of Angelica roots, and obtained quantitative information of many common metabolites.

The identification of phosphorylated proteins remains a challenge in proteomics, partially due to the difficulty in assigning tandem mass (MS/MS) spectra to their originating peptide sequences with correct phosphosite localization. Because of its advantages in efficiency and sensitivity, spectral library searching is a promising alternative to conventional sequence database searching. Our work aims to construct the largest collision-induced dissociation (CID) MS/MS spectral libraries of phosphorylated peptides in human (Homo sapiens) and four model organisms (Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) to date, to facilitate phosphorylated peptide identification by spectral library searching. We employed state-of-the-art search methods to published data and applied two recently published phosphorylation site localization tools (PhosphoRS and PTMProphet) to ascertain the phosphorylation sites. To further increase the coverage of this library, we predicted "semi-empirical" spectra for peptides containing known phosphorylation sites from the corresponding template unphosphorylated peptide spectra. The performance of the spectral libraries built were evaluated and found to be superior to conventional database searching in terms of sensitivity. Updated spectral libraries of phosphorylated peptides are made freely available for use with the spectral search engine SpectraST. The work flow being developed will be used to continuously update the libraries when new data become available.

The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules, and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery, and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies. DOI: http://dx.doi.org/10.7554/eLife.07661.001, eLife digest The cells of the immune system protect us by recognizing telltale molecules produced by damaged and diseased cells, or by infection-causing microorganisms (which are also called pathogens). To help with this process, the cells in our bodies display small fragments of proteins (called peptides) on their surface that are then checked by the immune cells. Collectively, these peptides are referred to as the ‘immunopeptidome’, and deciphering the complexity of the human immunopeptidome is important for both basic research and medical science. Such an achievement would help to guide the development of next-generation vaccines and therapies against autoimmune disorders, infectious diseases and cancers. In the past, immune peptides were mostly identified using a technique that is commonly called ‘shotgun’ mass spectrometry. However, this approach doesn't always provide reproducible results. In 2012, researchers reported the development of a new approach—which they called ‘SWATH’ mass spectrometry—that could yield more reproducible data. Now, Caron et al.—including many of the researchers involved in the 2012 study—have developed a large collection of standardized tests that use SWATH mass spectrometry to analyze the human immunopeptidome. The workflow and the computational and data resources developed as part of this international effort are the first steps toward highly reproducible and measurable analyses of the immunopeptidome across many samples. Moreover, the large repository of assays generated by the project has been made public and will serve a large community of researchers, which should enable better collaborations. In the future, SWATH mass spectrometry could be used as a robust technology for the reproducible detection and measurement of pathogen-specific or cancer-specific immune peptides. This could greatly help in the design of personalized immune-based therapies. DOI: http://dx.doi.org/10.7554/eLife.07661.002