Abstract
The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding ...members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs ...of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters).
The software is freely available at github.com/BioContainers/.
yperez@ebi.ac.uk.
The first stable version of the Proteomics Standards Initiative mzIdentML open data standard (version 1.1) was published in 2012—capturing the outputs of peptide and protein identification software. ...In the intervening years, the standard has become well-supported in both commercial and open software, as well as a submission and download format for public repositories. Here we report a new release of mzIdentML (version 1.2) that is required to keep pace with emerging practice in proteome informatics. New features have been added to support: (1) scores associated with localization of modifications on peptides; (2) statistics performed at the level of peptides; (3) identification of cross-linked peptides; and (4) support for proteogenomics approaches. In addition, there is now improved support for the encoding of de novo sequencing of peptides, spectral library searches, and protein inference. As a key point, the underlying XML schema has only undergone very minor modifications to simplify as much as possible the transition from version 1.1 to version 1.2 for implementers, but there have been several notable updates to the format specification, implementation guidelines, controlled vocabularies and validation software. mzIdentML 1.2 can be described as backwards compatible, in that reading software designed for mzIdentML 1.1 should function in most cases without adaptation. We anticipate that these developments will provide a continued stable base for software teams working to implement the standard. All the related documentation is accessible at http://www.psidev.info/mzidentml.
The original PRIDE Inspector tool was developed as an open source standalone tool to enable the visualization and validation of mass-spectrometry (MS)-based proteomics data before data submission or ...already publicly available in the Proteomics Identifications (PRIDE) database. The initial implementation of the tool focused on visualizing PRIDE data by supporting the PRIDE XML format and a direct access to private (password protected) and public experiments in PRIDE.
The ProteomeXchange (PX) Consortium has been set up to enable a better integration of existing public proteomics repositories, maximizing its benefit to the scientific community through the implementation of standard submission and dissemination pipelines. Within the Consortium, PRIDE is focused on supporting submissions of tandem MS data. The increasing use and popularity of the new Proteomics Standards Initiative (PSI) data standards such as mzIdentML and mzTab, and the diversity of workflows supported by the PX resources, prompted us to design and implement a new suite of algorithms and libraries that would build upon the success of the original PRIDE Inspector and would enable users to visualize and validate PX “complete” submissions. The PRIDE Inspector Toolsuite supports the handling and visualization of different experimental output files, ranging from spectra (mzML, mzXML, and the most popular peak lists formats) and peptide and protein identification results (mzIdentML, PRIDE XML, mzTab) to quantification data (mzTab, PRIDE XML), using a modular and extensible set of open-source, cross-platform libraries. We believe that the PRIDE Inspector Toolsuite represents a milestone in the visualization and quality assessment of proteomics data. It is freely available at http://github.com/PRIDE-Toolsuite/.
The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully ...supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.
Funding: This study was supported by Wellcome Trust grant number WT101477MA (http://www.wellcome.ac.uk/), BBSRC grant numbers BB/K01997X/1, BB/I00095X/1, BB/L024225/1 and BB/L002817/1 ...(http://www.bbsrc.ac.uk/), BMBF grant de.NBI - German Network for Bioinformatics Infrastructure (FKZ031 A 534A) (https://www.denbi.de/), NIH grant numbers R01-GM-094231 and R01-EB-017205 (http://www.nih.gov/), EPSRC reference EP/M022641/1 (https://www.epsrc.ac.uk), NSF grant number 1252893 (http://www.nsf.gov/), and Novo Nordisk Foundation (http://www.novonordiskfonden.dk/en). GitHub relies, at its core, on the well-known and open-source version control system Git, originally designed by Linus Torvalds for the development of the Linux kernel and now developed and maintained by the Git community. ...we would like to recommend some examples of bioinformatics repositories in GitHub (Table 1) and some useful training materials, including workshops, online courses, and manuscripts (Table 2).
In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be ...represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.
Objective
Sporadic inclusion body myositis (sIBM) pathogenesis is unknown; however, rimmed vacuoles (RVs) are a constant feature. We propose to identify proteins that accumulate within RVs.
Methods
...RVs and intact myofibers were laser microdissected from skeletal muscle of 18 sIBM patients and analyzed by a sensitive mass spectrometry approach using label‐free spectral count‐based relative protein quantification. Whole exome sequencing was performed on 62 sIBM patients. Immunofluorescence was performed on patient and mouse skeletal muscle.
Results
A total of 213 proteins were enriched by >1.5 ‐fold in RVs compared to controls and included proteins previously reported to accumulate in sIBM tissue or when mutated cause myopathies with RVs. Proteins associated with protein folding and autophagy were the largest group represented. One autophagic adaptor protein not previously identified in sIBM was FYCO1. Rare missense coding FYCO1 variants were present in 11.3% of sIBM patients compared with 2.6% of controls (p = 0.003). FYCO1 colocalized at RVs with autophagic proteins such as MAP1LC3 and SQSTM1 in sIBM and other RV myopathies. One FYCO1 variant protein had reduced colocalization with MAP1LC3 when expressed in mouse muscle.
Interpretation
This study used an unbiased proteomic approach to identify RV proteins in sIBM that included a novel protein involved in sIBM pathogenesis. FYCO1 accumulates at RVs, and rare missense variants in FYCO1 are overrepresented in sIBM patients. These FYCO1 variants may impair autophagic function, leading to RV formation in sIBM patient muscle. FYCO1 functionally connects autophagic and endocytic pathways, supporting the hypothesis that impaired endolysosomal degradation underlies the pathogenesis of sIBM. Ann Neurol 2017;81:227–239
The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by ...providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library.
The software is freely available at https://github.com/PRIDE-Utilities/ms-data-core-api.
Supplementary data are available at Bioinformatics online
juan@ebi.ac.uk.
The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS; eubic-ms.org) was founded in 2014 to unite European computational mass spectrometry researchers and proteomics bioinformaticians ...working in academia and industry. EuBIC-MS maintains educational resources (proteomics-academy.org) and organises workshops at national and international conferences on proteomics and mass spectrometry. Furthermore, EuBIC-MS is actively involved in several community initiatives such as the Human Proteome Organization's Proteomics Standards Initiative (HUPO-PSI). Apart from these collaborations, EuBIC-MS has organised two Winter Schools and two Developers' Meetings that have contributed to the strengthening of the European mass spectrometry network and fostered international collaboration in this field, even beyond Europe. Moreover, EuBIC-MS is currently actively developing a community-driven standard dedicated to mass spectrometry data annotation (SDRF-Proteomics) that will facilitate data reuse and collaboration. This manuscript highlights what EuBIC-MS is, what it does, and what it already has achieved. A warm invitation is extended to new researchers at all career stages to join the EuBIC-MS community on its Slack channel (eubic.slack.com).
PDF
