Harald Barsnes

The Proteomics Identifications database: 2010 update

The Proteomics Identifications database (PRIDE, http://www.ebi.ac.uk/pride) at the European Bioinformatics Institute has become one of the main repositories of mass spectrometry-derived proteomics data. For the last 2 years, PRIDE data holdings have grown substantially, comprising 60 different species, more than 2.5 million protein identifications, 11.5 million peptides and over 50 million spectra by September 2009. We here describe several new and improved features in PRIDE, including the revised submission process, which now includes direct submission of fragment ion annotations. Correspondingly, it is now possible to visualize spectrum fragmentation annotations on tandem mass spectra, a key feature for compliance with journal data submission requirements. We also describe recent developments in the PRIDE BioMart interface, which now allows integrative queries that can join PRIDE data to a growing number of biological resources such as Reactome, Ensembl, InterPro and UniProt. This ability to perform extremely powerful across-domain queries will certainly be a cornerstone of future bioinformatics analyses. Finally, we highlight the importance of data sharing in the proteomics field, and the corresponding integration of PRIDE with other databases in the ProteomExchange consortium.

PeptideShaker enables reanalysis of MS-derived proteomics data sets

VOLUME 33 NUMBER 1 JANUARY 2015 NATURE BIOTECHNOLOGY methods (Supplementary Note 1). The reliability of all statistical metrics has been validated in detail using the complex Pyrococcus furiosus standard with an entrapment database for FDR accuracy verification14 (Supplementary Note 1). PeptideShaker’s user-oriented interface is divided into nine linked tabs, such that selections in any one tab are automatically propagated to the other tabs. The initial display is the ‘Overview’ tab (Fig. 2), which shows a single interactive view that includes identified proteins, peptides and spectra. Additional tabs feature specific aspects of a typical proteomics analysis pipeline, including spectrum identification details (with an emphasis on multiple search engine comparison), protein fractionation analysis, modification site localization analysis, protein three-dimensional structures (with mapped modifications), link to functional annotation resources, gene ontology analysis, identification validation and quality control (Supplementary Note 1). Numerous visualizations are provided in PeptideShaker to help the user understand the significance of the underlying data. For example, the software takes advantage of the identification multiplicity typical of proteomics experiments by visualizing multiple recorded PSMs15 for a given peptide or by displaying posttranslational modification localization both within and across spectra (Supplementary Note 1). Moreover, PeptideShaker provides chromosome and gene mapping, modification analysis and intuitive coverage annotation on the sequence of every identified protein, meeting the goals of the Human Proteome Project16. Results from PeptideShaker can be readily submitted to PRIDE and ProteomeXchange using the built-in PRIDE and mzIdentML17 exports; at the time of writing, this has already resulted in 52 publicly available PeptideShaker-derived ProteomeXchange assays. Other export options include spreadsheet-compatible text files, a descriptive certificate of analysis, highresolution image formats for all displayed graphics, exports to common graph database formats including Cytoscape (http:// cytoscape.org), and export to the widely used Nonlinear (http://www.nonlinear.com) Progenesis liquid chromatography (LC)-MS package for label-free quantification. To the Editor: Mass spectrometry (MS)-based proteomics is commonly used to identify and quantify the hundreds to thousands of proteins that are present in complex biological samples. Widespread data sharing via publicly accessible repositories, such as PRIDE1, has now become standard practice, aided by robust user-oriented tools for data submission2,3 and inspection4 and bolstered by the advent of the ProteomeXchange initiative5. Importantly, repository data sharing has enabled the first high-profile studies in which the repurposing of publicly available proteomics data has revealed new biological insights6,7. However, proteomics data processing and (re-)analysis currently remain far from routine practice. There is a pressing need for a user-friendly, open source tool that empowers users to carry out state-of-the-art proteomics data analysis at any stage in the data life cycle8,9. To maximize the value of public proteomics data, reuse and repurposing must become straightforward, allowing the completion of the proteomics data cycle. Here we describe PeptideShaker (http://peptideshaker.googlecode.com), a proteomics informatics software that can be used at any stage in the proteomics data cycle for the analysis and interpretation of primary data, enabling data sharing and dissemination and re-analysis of publicly available proteomics data. Importantly, PeptideShaker can work with the combined output of multiple identification algorithms (Fig. 1). To identify peptides and proteins PeptideShaker uses the target-decoy search strategy10 to estimate posterior error probabilities and uses these to unify the peptide-to-spectrum match (PSM) lists of different search engines, thus increasing the confidence and sensitivity of hits compared with single-search-engine processing11,12. PeptideShaker provides statistical confidence estimates for each peptide and protein, taking into account protein inference issues13. Furthermore, as well as providing false discovery rates (FDRs) at the PSM, peptide and protein levels, PeptideShaker calculates reliable false negative rates (FNRs), providing the user with a novel and highly useful interface to filter results according to an FDR-versus-FNR cost-benefit rationale that has so far been absent from proteomics. This filter for specificity and sensitivity includes interactive graphs providing immediate feedback on the values of FDR and FNR for PSMs, peptides and proteins at any chosen threshold. In addition to these identification reliability measures, PeptideShaker also provides confident modification site inference using the latest localization PeptideShaker enables reanalysis of MS-derived proteomics data sets

SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches

The identification of proteins by mass spectrometry is a standard technique in the field of proteomics, relying on search engines to perform the identifications of the acquired spectra. Here, we present a user‐friendly, lightweight and open‐source graphical user interface called SearchGUI (http://searchgui.googlecode.com), for configuring and running the freely available OMSSA (open mass spectrometry search algorithm) and X!Tandem search engines simultaneously. Freely available under the permissible Apache2 license, SearchGUI is supported on Windows, Linux and OSX.

The Ontology Lookup Service: bigger and better

The Ontology Lookup Service (OLS; http://www.ebi.ac.uk/ols) has been providing several means to query, browse and navigate biomedical ontologies and controlled vocabularies since it first went into production 4 years ago, and usage statistics indicate that it has become a heavily accessed service with millions of hits monthly. The volume of data available for querying has increased 7-fold since its inception. OLS functionality has been integrated into several high-usage databases and data entry tools. Improvements in the data model and loaders, as well as interface enhancements have made the OLS easier to use and capture more annotations from the source data. In addition, newly released software packages now provide easy means to fully integrate OLS functionality in external applications.

The PRoteomics IDEntification (PRIDE) Converter 2 Framework: An Improved Suite of Tools to Facilitate Data Submission to the PRIDE Database and the ProteomeXchange Consortium

The original PRIDE Converter tool greatly simplified the process of submitting mass spectrometry (MS)-based proteomics data to the PRIDE database. However, after much user feedback, it was noted that the tool had some limitations and could not handle several user requirements that were now becoming commonplace. This prompted us to design and implement a whole new suite of tools that would build on the successes of the original PRIDE Converter and allow users to generate submission-ready, well-annotated PRIDE XML files. The PRIDE Converter 2 tool suite allows users to convert search result files into PRIDE XML (the format needed for performing submissions to the PRIDE database), generate mzTab skeleton files that can be used as a basis to submit quantitative and gel-based MS data, and post-process PRIDE XML files by filtering out contaminants and empty spectra, or by merging several PRIDE XML files together. All the tools have both a graphical user interface that provides a dialog-based, user-friendly way to convert and prepare files for submission, as well as a command-line interface that can be used to integrate the tools into existing or novel pipelines, for batch processing and power users. The PRIDE Converter 2 tool suite will thus become a cornerstone in the submission process to PRIDE and, by extension, to the ProteomeXchange consortium of MS-proteomics data repositories.

compomics-utilities: an open-source Java library for computational proteomics

BackgroundThe growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool.ResultsIn order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development.ConclusionsAs a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.

ms_lims, a simple yet powerful open source laboratory information management system for MS-driven proteomics.

MS‐based proteomics produces large amounts of mass spectra that require processing, identification and possibly quantification before interpretation can be undertaken. High‐throughput studies require automation of these various steps, and management of the data in association with the results obtained. We here present ms_lims (http://genesis.UGent.be/ms_lims), a freely available, open‐source system based on a central database to automate data management and processing in MS‐driven proteomics analyses.

In-depth Characterization of the Cerebrospinal Fluid (CSF) Proteome Displayed Through the CSF Proteome Resource (CSF-PR)

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.

IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT.

Isobaric peptide labeling plays an important role in relative quantitative comparisons of proteomes. Isobaric labeling techniques utilize MS/MS spectra for relative quantification, which can be either based on the relative intensities of reporter ions in the low mass region (iTRAQ and TMT) or on the relative intensities of quantification signatures throughout the spectrum due to isobaric peptide termini labeling (IPTL). Due to the increased quantitative information found in MS/MS fragment spectra generated by the recently developed IPTL approach, new software was required to extract the quantitative information. IsobariQ was specifically developed for this purpose; however, support for the reporter ion techniques iTRAQ and TMT is also included. In addition, to address recently emphasized issues about heterogeneity of variance in proteomics data sets, IsobariQ employs the statistical software package R and variance stabilizing normalization (VSN) algorithms available therein. Finally, the functionality of IsobariQ is validated with data sets of experiments using 6-plex TMT and IPTL. Notably, protein substrates resulting from cleavage by proteases can be identified as shown for caspase targets in apoptosis.

Discovery and initial verification of differentially abundant proteins between multiple sclerosis patients and controls using iTRAQ and SID-SRM.

In the present study, we aimed to discover cerebrospinal fluid (CSF) proteins with significant abundance difference between early multiple sclerosis patients and controls, and do an initial verification of these proteins using selected reaction monitoring (SRM). iTRAQ and Orbitrap MS were used to compare the CSF proteome of patients with clinically isolated syndrome (CIS) (n=5), patients with relapsing-remitting multiple sclerosis that had CIS at the time of lumbar puncture (n=5), and controls with other inflammatory neurological disease (n=5). Of more than 1200 identified proteins, five proteins were identified with significant abundance difference between the patients and controls. In the initial verification using SRM we analyzed a larger patient and control cohort (n=132) and also included proteins reported as differentially abundant in multiple sclerosis in the literature. We found significant abundance difference for 11 proteins after verification, of which the five proteins alpha-1-antichymotrypsin, contactin-1, apolipoprotein D, clusterin, and kallikrein-6 were significantly differentially abundant in several of the group comparisons. This initial study form the basis for further biomarker verification studies in even larger sample cohorts, to determine if these proteins have relevance as diagnostic or prognostic biomarkers for multiple sclerosis.