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Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements., (© 2022 The Authors. Proteomics published by Wiley-VCH GmbH.)

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Public phosphorylation databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available mass spectrometry (MS) data. However, there is no database-level control for false discovery of sites, likely leading to the overestimation of true phosphosites. By profiling the human phosphoproteome, we estimate the false discovery rate (FDR) of phosphosites and predict a more realistic count of true identifications. We rank sites into phosphorylation likelihood sets and analyze them in terms of conservation across 100 species, sequence properties, and functional annotations. We demonstrate significant differences between the sets and develop a method for independent phosphosite FDR estimation. Remarkably, we report estimated FDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr) sites, respectively, that are supported by only a single piece of identification evidence─the majority of sites in PSP. We estimate that around 62 000 Ser, 8000 Thr, and 12 000 Tyr phosphosites in the human proteome are likely to be true, which is lower than most published estimates. Furthermore, our analysis estimates that 86 000 Ser, 50 000 Thr, and 26 000 Tyr phosphosites are likely false-positive identifications, highlighting the significant potential of false-positive data to be present in phosphorylation databases.

In this chapter, we introduce the bioinformatics methods associated with studying histidine phosphorylation (pHis) by LC-MS/MS. We describe methods for converting and preprocessing raw data from MS instruments, the method of searching MS data against a sequence database and scoring the confidence associated with localizing the modification site on the peptide sequence. We also describe methods for performing pathway enrichment once a set of pHis-containing proteins have been identified to understand the putative functions of modified proteins. Several of the methods are relatively straightforward to run but require some theoretical knowledge to optimize parameters and correctly interpret outputs. We also describe some of the theory underpinning statistical considerations, to assist correct usage and interpretation of these bioinformatics methods.

Phosphorylation is a key regulator of protein function under (patho)physiological conditions, and defining site-specific phosphorylation is essential to understand basic and disease biology. In vertebrates, the investigative focus has primarily been on serine, threonine and tyrosine phosphorylation, but mounting evidence suggests that phosphorylation of other "non-canonical" amino acids also regulates critical aspects of cell biology. However, standard methods of phosphoprotein characterisation are largely unsuitable for the analysis of non-canonical phosphorylation due to their relative instability under acidic conditions and/or elevated temperature. Consequently, the complete landscape of phosphorylation remains unexplored. Here, we report an unbiased phosphopeptide enrichment strategy based on strong anion exchange (SAX) chromatography (UPAX), which permits identification of histidine (His), arginine (Arg), lysine (Lys), aspartate (Asp), glutamate (Glu) and cysteine (Cys) phosphorylation sites on human proteins by mass spectrometry-based phosphoproteomics. Remarkably, under basal conditions, and having accounted for false site localisation probabilities, the number of unique non-canonical phosphosites is approximately one-third of the number of observed canonical phosphosites. Our resource reveals the previously unappreciated diversity of protein phosphorylation in human cells, and opens up avenues for high-throughput exploration of non-canonical phosphorylation in all organisms., (© 2019 The Authors. Published under the terms of the CC BY 4.0 license.)

The mzQuantML data standard was designed to capture the output of quantitative software in proteomics, to support submissions to public repositories, development of visualization software and pipeline/modular approaches. The standard is designed around a common core that can be extended to support particular types of technique through the release of semantic rules that are checked by validation software. The first release of mzQuantML supported four quantitative proteomics techniques via four sets of semantic rules: (i) intensity-based (MS(1) ) label free, (ii) MS(1) label-based (such as SILAC or N(15) ), (iii) MS(2) tag-based (iTRAQ or tandem mass tags), and (iv) spectral counting. We present an update to mzQuantML for supporting SRM techniques. The update includes representing the quantitative measurements, and associated meta-data, for SRM transitions, the mechanism for inferring peptide-level or protein-level quantitative values, and support for both label-based or label-free SRM protocols, through the creation of semantic rules and controlled vocabulary terms. We have updated the specification document for mzQuantML (version 1.0.1) and the mzQuantML validator to ensure that consistent files are produced by different exporters. We also report the capabilities for production of mzQuantML files from popular SRM software packages, such as Skyline and Anubis., (© 2015 The Authors. PROTEOMICS Published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Objective: To describe the goals of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization, the methods that the PSI has employed to create data standards, the resulting output of the PSI, lessons learned from the PSI's evolution, and future directions and synergies for the group., Materials and Methods: The PSI has 5 categories of deliverables that have guided the group. These are minimum information guidelines, data formats, controlled vocabularies, resources and software tools, and dissemination activities. These deliverables are produced via the leadership and working group organization of the initiative, driven by frequent workshops and ongoing communication within the working groups. Official standards are subjected to a rigorous document process that includes several levels of peer review prior to release., Results: We have produced and published minimum information guidelines describing what information should be provided when making data public, either via public repositories or other means. The PSI has produced a series of standard formats covering mass spectrometer input, mass spectrometer output, results of informatics analysis (both qualitative and quantitative analyses), reports of molecular interaction data, and gel electrophoresis analyses. We have produced controlled vocabularies that ensure that concepts are uniformly annotated in the formats and engaged in extensive software development and dissemination efforts so that the standards can efficiently be used by the community.Conclusion In its first dozen years of operation, the PSI has produced many standards that have accelerated the field of proteomics by facilitating data exchange and deposition to data repositories. We look to the future to continue developing standards for new proteomics technologies and workflows and mechanisms for integration with other omics data types. Our products facilitate the translation of genomics and proteomics findings to clinical and biological phenotypes. The PSI website can be accessed at http://www.psidev.info., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Controlled vocabularies (CVs), i.e. a collection of predefined terms describing a modeling domain, used for the semantic annotation of data, and ontologies are used in structured data formats and databases to avoid inconsistencies in annotation, to have a unique (and preferably short) accession number and to give researchers and computer algorithms the possibility for more expressive semantic annotation of data. The Human Proteome Organization (HUPO)-Proteomics Standards Initiative (PSI) makes extensive use of ontologies/CVs in their data formats. The PSI-Mass Spectrometry (MS) CV contains all the terms used in the PSI MS-related data standards. The CV contains a logical hierarchical structure to ensure ease of maintenance and the development of software that makes use of complex semantics. The CV contains terms required for a complete description of an MS analysis pipeline used in proteomics, including sample labeling, digestion enzymes, instrumentation parts and parameters, software used for identification and quantification of peptides/proteins and the parameters and scores used to determine their significance. Owing to the range of topics covered by the CV, collaborative development across several PSI working groups, including proteomics research groups, instrument manufacturers and software vendors, was necessary. In this article, we describe the overall structure of the CV, the process by which it has been developed and is maintained and the dependencies on other ontologies. Database URL: http://psidev.cvs.sourceforge.net/viewvc/psidev/psi/psi-ms/mzML/controlledVocabulary/psi-ms.obo.

The Human Proteome Organisation Proteomics Standards Initiative (HUPO-PSI) was established in 2002 with the aim of defining community standards for data representation in proteomics and facilitating data comparison, exchange and verification. Over the last 10 years significant advances have been made, with common data standards now published and implemented in the field of both mass spectrometry and molecular interactions. The 2012 meeting further advanced this work, with the mass spectrometry groups finalising approaches to capturing the output from recent developments in the field, such as quantitative proteomics and SRM. The molecular interaction group focused on improving the integration of data from multiple resources. Both groups united with a guest work track, organized by the HUPO Technology/Standards Committee, to formulate proposals for data submissions from the HUPO Human Proteome Project and to start an initiative to collect standard experimental protocols., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

This book is part of the Methods in Molecular Biology series, and provides a general overview of computational approaches used in proteome research. In this chapter, we give an overview of the scope of the book in terms of current proteomics experimental techniques and the reasons why computational approaches are needed. We then give a summary of each chapter, which together provide a picture of the state of the art in proteome bioinformatics research.

Background: Although the genomes of many of the most important human and animal pathogens have now been sequenced, our understanding of the actual proteins expressed by these genomes and how well they predict protein sequence and expression is still deficient. We have used three complementary approaches (two-dimensional electrophoresis, gel-liquid chromatography linked tandem mass spectrometry and MudPIT) to analyze the proteome of Toxoplasma gondii, a parasite of medical and veterinary significance, and have developed a public repository for these data within ToxoDB, making for the first time proteomics data an integral part of this key genome resource., Results: The draft genome for Toxoplasma predicts around 8,000 genes with varying degrees of confidence. Our data demonstrate how proteomics can inform these predictions and help discover new genes. We have identified nearly one-third (2,252) of all the predicted proteins, with 2,477 intron-spanning peptides providing supporting evidence for correct splice site annotation. Functional predictions for each protein and key pathways were determined from the proteome. Importantly, we show evidence for many proteins that match alternative gene models, or previously unpredicted genes. For example, approximately 15% of peptides matched more convincingly to alternative gene models. We also compared our data with existing transcriptional data in which we highlight apparent discrepancies between gene transcription and protein expression., Conclusion: Our data demonstrate the importance of protein data in expression profiling experiments and highlight the necessity of integrating proteomic with genomic data so that iterative refinements of both annotation and expression models are possible.

Cyanobacteria were among the oldest organisms to undertake oxygenic photosynthesis and have an essential impact on the atmosphere and carbon/nitrogen cycles on the planet. The thylakoid membrane of cyanobacteria represents an intricate compartment that houses a variety of multi-component (pigment–)protein complexes, assembly factors, and regulators, as well as transporters involved in photosynthetic light reactions, and respiratory electron transport. How these protein components are incorporated into membranes during thylakoid formation and how individual complexes are regulated to construct the functional machinery remains elusive. Here, we carried out an in-depth statistical analysis of the thylakoid proteome data obtained during light-induced thylakoid membrane biogenesis in the model cyanobacterium Synechococcus elongatus PCC 7942. A total of 1581 proteins were experimentally quantified, among which 457 proteins demonstrated statistically significant variations in abundance at distinct thylakoid biogenesis stages. Gene Ontology and KEGG enrichment analysis revealed that predominantly photosystems, light-harvesting antennae, ABC transporters, and pathway enzymes involved in oxidative stress responses and protein folding exhibited notable alternations in abundance between high light and growth light. Moreover, through cluster analysis the 1581 proteins were categorized into six distinct clusters that have significantly different trajectories of the change in their abundance during thylakoid development. Our study provides insights into the physiological regulation for the membrane integration of protein components and functionally linked complexes during the cyanobacterial TM biogenesis process. The findings and analytical methodologies developed in this study may be valuable for studying the global responses of TM biogenesis and photosynthetic acclimation in plants and algae. [ABSTRACT FROM AUTHOR]