multiplierz: an extensible API based desktop environment for proteomics data analysis

Background  

Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge.     

mzServer: web-based programmatic access for mass spectrometry data analysis

Continued progress toward systematic generation of large-scale and comprehensive proteomics data in the context of biomedical research will create project-level data sets of unprecedented size and ultimately overwhelm current practices for results validation that are based on distribution of native or surrogate mass spectrometry files. Moreover, the majority of proteomics studies leverage discovery-mode MS/MS analyses, rendering associated data-reduction efforts incomplete at best, and essentially ensuring future demand for re-analysis of data as new biological and technical information become available. Based on these observations, we propose to move beyond the sharing of interpreted spectra, or even the distribution of data at the individual file or project level, to a system much like that used in high-energy physics and astronomy, whereby raw data are made programmatically accessible at the site of acquisition. Toward this end we have developed a web-based server (mzServer), which exposes our common API (mzAPI) through very intuitive (RESTful) uniform resource locators (URL) and provides remote data access and analysis capabilities to the research community. Our prototype mzServer provides a model for lab-based and community-wide data access and analysis.     

From proteomics data representation to public data flow: a report on the HUPO-PSI workshop September 2011, Geneva, Switzerland

The plenary session of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization at the Tenth annual HUPO World Congress updated the delegates on the ongoing activities of this group. The Molecular Interactions workgroup described the success of the PSICQUIC web service, which enables users to access multiple interaction resources with a single query. One user instance is the IMEx Consortium, which uses the service to enable users to access a non-redundant set of protein-protein interaction records. The mass spectrometry data formats, mzML for mass spectrometer output files and mzIdentML for the output of search engines, are now successfully established with increasing numbers of implementations. A format for the output of quantitative proteomics data, mzQuantML, and also TraML, for SRM/MRM transition lists, are both currently nearing completion. The corresponding MIAPE documents are being updated in line with advances in the field, as is the shared controlled vocabulary PSI-MS. In addition, the mzTab format was introduced, as a simpler way to report MS proteomics and metabolomics results. Finally, the ProteomeXchange Consortium, which will supply a single entry point for the submission of MS proteomics data to multiple data resources including PRIDE and PeptideAtlas, is currently being established.     

jmzReader: A Java parser library to process and visualize multiple text and XML-based mass spectrometry data formats

We here present the jmzReader library: a collection of Java application programming interfaces (APIs) to parse the most commonly used peak list and XML-based mass spectrometry (MS) data formats: DTA, MS2, MGF, PKL, mzXML, mzData, and mzML (based on the already existing API jmzML). The library is optimized to be used in conjunction with mzIdentML, the recently released standard data format for reporting protein and peptide identifications, developed by the HUPO proteomics standards initiative (PSI). mzIdentML files do not contain spectra data but contain references to different kinds of external MS data files. As a key functionality, all parsers implement a common interface that supports the various methods used by mzIdentML to reference external spectra. Thus, when developing software for mzIdentML, programmers no longer have to support multiple MS data file formats but only this one interface. The library (which includes a viewer) is open source and, together with detailed documentation, can be downloaded from http://code.google.com/p/jmzreader/.     

FDRAnalysis: a tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines

Confident identification of peptides via tandem mass spectrometry underpins modern high-throughput proteomics. This has motivated considerable recent interest in the postprocessing of search engine results to increase confidence and calculate robust statistical measures, for example through the use of decoy databases to calculate false discovery rates (FDR). FDR-based analyses allow for multiple testing and can assign a single confidence value for both sets and individual peptide spectrum matches (PSMs). We recently developed an algorithm for combining the results from multiple search engines, integrating FDRs for sets of PSMs made by different search engine combinations. Here we describe a web-server and a downloadable application that makes this routinely available to the proteomics community. The web server offers a range of outputs including informative graphics to assess the confidence of the PSMs and any potential biases. The underlying pipeline also provides a basic protein inference step, integrating PSMs into protein ambiguity groups where peptides can be matched to more than one protein. Importantly, we have also implemented full support for the mzIdentML data standard, recently released by the Proteomics Standards Initiative, providing users with the ability to convert native formats to mzIdentML files, which are available to download.     

The jmzQuantML programming interface and validator for the mzQuantML data standard

The mzQuantML standard from the HUPO Proteomics Standards Initiative has recently been released, capturing quantitative data about peptides and proteins, following analysis of MS data. We present a Java application programming interface (API) for mzQuantML called jmzQuantML. The API provides robust bridges between Java classes and elements in mzQuantML files and allows random access to any part of the file. The API provides read and write capabilities, and is designed to be embedded in other software packages, enabling mzQuantML support to be added to proteomics software tools ( http://code.google.com/p/jmzquantml/ ). The mzQuantML standard is designed around a multilevel validation system to ensure that files are structurally and semantically correct for different proteomics quantitative techniques. In this article, we also describe a Java software tool ( http://code.google.com/p/mzquantml‐validator/ ) for validating mzQuantML files, which is a formal part of the data standard.     

Web Resources for Mass Spectrometry-based Proteomics

With the development of high-resolution and high-throughput mass spectrometry(MS)technology, a large quantum of proteomic data is continually being generated. Collecting and sharing these data are a challenge that requires immense and sustained human effort. In this report, we provide a classification of important web resources for MS-based proteomics and present rating of these web resources, based on whether raw data are stored, whether data submission is supported,and whether data analysis pipelines are provided. These web resources are important for biologists involved in proteomics research.     

Enabling cryo-EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in-community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the “resolution revolution,” their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter-protein interactions. Cryo-EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS-derived proteomics data and systematic fitting of AlphaFold-predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo-EM, and AI-guided protein structure prediction.     

Proteomics data repositories: Providing a safe haven for your data and acting as a springboard for further research

Despite the fact that data deposition is not a generalised fact yet in the field of proteomics, several mass spectrometry (MS) based proteomics repositories are publicly available for the scientific community. The main existing resources are: the Global Proteome Machine Database (GPMDB), PeptideAtlas, the PRoteomics IDEntifications database (PRIDE), Tranche, and NCBI Peptidome. In this review the capabilities of each of these will be described, paying special attention to four key properties: data types stored, applicable data submission strategies, supported formats, and available data mining and visualization tools. Additionally, the data contents from model organisms will be enumerated for each resource. There are other valuable smaller and/or more specialized repositories but they will not be covered in this review. Finally, the concept behind the ProteomeXchange consortium, a collaborative effort among the main resources in the field, will be introduced.     

Data‐Independent Acquisition Mass Spectrometry‐Based Proteomics and Software Tools: A Glimpse in 2020

This review provides a brief overview of the development of data‐independent acquisition (DIA) mass spectrometry‐based proteomics and selected DIA data analysis tools. Various DIA acquisition schemes for proteomics are summarized first including Shotgun‐CID, DIA, MS^E, PAcIFIC, AIF, SWATH, MSX, SONAR, WiSIM, BoxCar, Scanning SWATH, diaPASEF, and PulseDIA, as well as the mass spectrometers enabling these methods. Next, the software tools for DIA data analysis are classified into three groups: library‐based tools, library‐free tools, and statistical validation tools. The approaches are reviewed for generating spectral libraries for six selected library‐based DIA data analysis software tools which are tested by the authors, including OpenSWATH, Spectronaut, Skyline, PeakView, DIA‐NN, and EncyclopeDIA. An increasing number of library‐free DIA data analysis tools are developed including DIA‐Umpire, Group‐DIA, PECAN, PEAKS, which facilitate identification of novel proteoforms. The authors share their user experience of when to use DIA‐MS, and several selected DIA data analysis software tools. Finally, the state of the art DIA mass spectrometry and software tools, and the authors’ views of future directions are summarized.     

The proteomics standards initiative

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Progress has been made in the development of common standards for data exchange in the fields of both mass spectrometry and protein-protein interaction. A proteomics-specific extension is being created for the emerging American Society for Tests and Measurements mass spectrometry standard, which will be supported by manufacturers of both hardware and software. A data model for proteomics experimentation is under development and discussions on a public repository for published proteomics data are underway. The Protein-Protein Interactions group expects to publish the Level 1 PSI data exchange format for protein-protein interactions soon and discussions as to the content of Level 2 have been initiated.     

Getting a grip on proteomics data – Proteomics Data Collection (ProDaC)

In proteomics, rapid developments in instrumentation led to the acquisition of increasingly large data sets. Correspondingly, ProDaC was founded in 2006 as a Coordination Action project within the 6th European Union Framework Programme to support data sharing and community‐wide data collection. The objectives of ProDaC were the development of documentation and storage standards, setup of a standardized data submission pipeline and collection of data. Ending in March 2009, ProDaC has delivered a comprehensive toolbox of standards and computer programs to achieve these goals.     

Proposal for a standard representation of two-dimensional gel electrophoresis data

The global analysis of proteins is now feasible due to improvements in techniques such as two-dimensional gel electrophoresis (2-DE), mass spectrometry, yeast two-hybrid systems and the development of bioinformatics applications. The experiments form the basis of proteomics, and present significant challenges in data analysis, storage and querying. We argue that a standard format for proteome data is required to enable the storage, exchange and subsequent re-analysis of large datasets. We describe the criteria that must be met for the development of a standard for proteomics. We have developed a model to represent data from 2-DE experiments, including difference gel electrophoresis along with image analysis and statistical analysis across multiple gels. This part of proteomics analysis is not represented in current proposals for proteomics standards. We are working with the Proteomics Standards Initiative to develop a model encompassing biological sample origin, experimental protocols, a number of separation techniques and mass spectrometry. The standard format will facilitate the development of central repositories of data, enabling results to be verified or re-analysed, and the correlation of results produced by different research groups using a variety of laboratory techniques.     

Progress in Establishing Common Standards for Exchanging Proteomics Data: The Second Meeting of the HUPO Proteomics Standards Initiative

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Rapid progress has been made in the development of common standards for data exchange in the fields of both mass spectrometry and protein-protein interactions since the first PSI meeting [1]. Both hardware and software manufacturers have agreed to work to ensure that a proteomics-specific extension is created for the emerging ASTM mass spectrometry standard and the data model for a proteomics experiment has advanced significantly. The Protein-Protein Interactions (PPI) group expects to publish the Level 1 PSI data exchange format for protein-protein interactions by early summer this year, and discussion as to the additional content of Level 2 has been initiated.     

Quality assessment and interference detection in targeted mass spectrometry data using machine learning

Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions’ ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.     

Proteomics made more accessible

MS‐based proteomics is a bioinformatic‐intensive field. Additionally, the instruments and instrument‐related and analytic software are expensive. Some free Internet‐based proteomics tools have gained wide usage, but there have not been any single bioinformatic framework that in an easy and intuitive way guided the user through the whole process from analyses to submission. Together, these factors may have limited the expansion of proteomics analyses, and also the secondary use (reanalyses) of proteomic data. Vaudel et al. (Proteomics 2014, 14, 1001–1005) are now describing their Compomics framework that guides the user through all the main steps, from the database generation, via the analyses and validation, and through the submission process to PRIDE, a proteomic data bank. Vaudel et al. partly base the framework on tools that they have developed themselves, and partly they are integrating other freeware tools into the workflow. One of the most interesting aspects with the Compomics framework is the possibility of extending MS‐based proteomics outside the MS laboratory itself. With the Compomics framework, any laboratory can handle large amounts of proteomic data, thereby facilitating collaboration and in‐depth data analyses. The described software also opens the potential for any laboratory to reanalyze data deposited in PRIDE.     

ProteomeCommons.org IO Framework: reading and writing multiple proteomics data formats

MOTIVATION: Effective use of proteomics data, specifically mass spectrometry data, relies on the ability to read and write the many mass spectrometer file formats. Even with mass spectrometer vendor-specific libraries and vendor-neutral file formats, such as mzXML and mzData it can be difficult to extract raw data files in a form suitable for batch processing and basic research. Introduced here are the ProteomeCommons.org Input and Output Framework, abbreviated to IO Framework, which is designed to abstractly represent mass spectrometry data. This project is a public, open-source, free-to-use framework that supports most of the mass spectrometry data formats, including current formats, legacy formats and proprietary formats that require a vendor-specific library in order to operate. The IO Framework includes an on-line tool for non-programmers and a set of libraries that developers may use to convert between various proteomics file formats. AVAILABILITY: The current source-code and documentation for the ProteomeCommons.org IO Framework is freely available at http://www.proteomecommons.org/current/531/