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Communicating Mass Spectrometry Quality Information in mzQC with Python, R, and Java
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Cite this: J. Am. Soc. Mass Spectrom. 2024, 35, 8, 1875–1882
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https://doi.org/10.1021/jasms.4c00174
Published June 25, 2024

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Abstract

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Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intralaboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organization’s Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionalities, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries in a workflow for extracting, analyzing, and visualizing QC metrics from different sources. Additionally, we show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

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Published as part of Journal of the American Society for Mass Spectrometry virtual special issue “Asilomar: Computational Mass Spectrometry”.

Introduction

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Mass spectrometry (MS) is a powerful analytical technique for analyzing molecules in complex biological samples. However, MS data are inherently prone to variability and bias. Even between related MS experiments, subtle variations in sample preparation, instrument performance, and data processing can lead to hidden data inconsistency. (1) To inspire confidence in the results of an MS experiment and ensure consistency and comparability across different measurements, implementing appropriate quality assurance (QA) and quality control (QC) strategies is essential. QC is essential for generating high-quality MS data that can support meaningful and reproducible scientific discoveries. This is especially relevant in light of the reproducibility crisis in science. (2) By improving the quality assessment and reproducibility of MS experiments, QC ensures the credibility and confidence of the resulting scientific findings.
Historically, coordinated efforts for QC in MS were first advocated for in proteomics with the Amsterdam Principles, (3) in 2008, closely followed by proposals for potential metrics by Kinsinger et al. (4) and the NIST MSQC metrics. (5) Since then, several dedicated software packages have emerged for the QC and QA of various mass spectrometry applications. (6−12) Developed to serve a wide range of use cases and workflows, these tools employ a heterogeneous set of QC approaches and metrics. Additionally, several consortia and community initiatives, such as the Metabolomics Quality Assurance & Quality Control Consortium (13) and the Lipidomics Standard Initiative, (14,15) are actively developing QC best practices in their respective fields by establishing community-driven guidelines.
Despite these important efforts, so far no unified approaches toward QC in biological MS have been established. One of the limitations is the lack of a standard file format to store and communicate QC metrics, which are numerical or graphical indicators that describe the quality of MS data at different levels, such as sample quality, instrument performance, completeness of the measurements, and data consistency. (16) Currently, QC metrics are often stored in different formats and locations, such as instrument log files, proprietary software outputs, spreadsheets, and human-readable QC reports. This makes it difficult to access, compare, and share QC information over time, across different instruments, sample preparation techniques, and laboratories.
To address this issue, the Quality Control working group (17) of the Human Proteome Organization’s Proteomics Standards Initiative (HUPO-PSI) (18) has recently established the standard mzQC file format (https://github.com/HUPO-PSI/mzQC) to report and exchange data quality-related information for MS experiments and the associated analysis results. mzQC is based on the widespread JavaScript Object Notation (JSON) format to provide a lightweight yet versatile file format that can be easily implemented in software to produce or consume mzQC files, and its goal is to support diverse workflows in proteomics, metabolomics, and other MS applications. It is important to note that mzQC aims to provide a standardized framework for storing and exchanging QC metrics in MS data analysis in a transparent manner, rather than to directly judge the quality of the data it describes.
QC metrics in an mzQC file are grouped in “runQuality” or “setQuality” elements, depending on whether the metrics pertain to a single or multiple MS runs, respectively. Each runQuality or setQuality element contains a “metadata” section that provides information to track the provenance of the QC metrics, such as the originating MS run(s) and the software tool(s) used to calculate the metrics. QC metric values are stored in “qualityMetric” elements and can consist of single values, tuples, or tabular data. Additionally, each QC metric is defined by a corresponding term in the PSI-MS controlled vocabulary (19) for semantic annotation of the data and to ensure an unambiguous definition of each QC metric. Further technical details of the mzQC format and the official PSI specification document (version 1.0, released in February 2024) are available at https://github.com/HUPO-PSI/mzQC.
To ensure the adoption of the mzQC format, supporting software tools are needed. There is a vibrant open-source community of bioinformaticians developing software to analyze MS data in various programming languages, among which some of the most popular are Python, (20−23) which is widely used for data analysis and machine learning; R, (24,25) a language designed for statistical computing and graphics; and Java, (26,27) a multiplatform programming language that is suitable for large-scale applications.
In this manuscript, we present open-source software libraries to read, write, and validate QC data in the mzQC format in the three programming languages mentioned above: Python, R, and Java. We describe the design and implementation of these libraries, which follow a common data model and provide shared functionality to operate on mzQC files. We demonstrate the use of these software libraries for extracting, analyzing, and visualizing QC metrics from different sources. We also show how these libraries can be integrated with existing software tools and workflows for performing QC of MS data, with mzQC acting as the glue between various workflow steps. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub/).

Methods

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mzQC Software Libraries

The mzQC software libraries are implemented in three popular programming languages (Table 1): pymzqc in Python, rmzqc in R, and jmzqc in Java. Each library builds on the mzQC schema definition, which formally defines the syntax of mzQC documents using a JSON schema, and provides a high-level abstraction of data quality-related information in mzQC files. Rather than a single application programming interface (API) that all libraries share, they each follow the conventions and best practices of their respective programming languages.
Table 1. Overview of the High-Level Functionality Provided by the mzQC Software Libraries
FunctionalitySoftware libraryAPI
Read (deserialize): consume an mzQC file (optionally from a JSON string, a local file, or a remote file) and return a data object representing the file contentspymzqcMZQCFile.JsonSerialisable.FromJson(..)
 rmzqcrmzqc::readMZQC(..)
  MzQC$fromData(..)
 jmzqcConverter.of(..)
Write (serialize): export an mzQC data object to a JSON file or JSON stringpymzqcMZQCFile.JsonSerialisable.ToJson(..)
 rmzqcrmzqc::writeMZQC(..)
  jsonlite::toJSON(..)
 jmzqcConverter.toJsonString(..)
  Converter.toJsonFile(..)
Syntactic validation: verify that an mzQC file conforms to the mzQC schema specificationpymzqcSyntaxCheck().validate(..)
 rmzqcrmzqc::validateFromFile(..)
  rmzqc::validateFromString(..)
  rmzqc::validateFromObj(..)
 jmzqcConverter.validate(..)
Semantic validation: verify that an mzQC file conforms to the mzQC semantic content constraintspymzqcSemanticCheck().validate(..)
The primary functionality provided by all three software libraries is the serialization and deserialization of mzQC files, which facilitates the reading and writing of QC information, respectively. This enables users to create mzQC files containing newly computed QC information, read existing mzQC files with previously computed QC metrics, and manipulate the QC information for further data processing and analysis. The software libraries automatically perform native value type matching where possible, such as converting tabular data to data.frame objects in R or Pandas DataFrame objects in Python.
It is important to note that the mzQC software libraries do not calculate QC metrics directly. Instead, they facilitate the import and export of metric values obtained using external software from/to mzQC files. As such, they are agnostic to input file formats for MS-related data, such as mzML, (28) mzTab, (29) or custom formats, and operate at the level of QC metrics instead. The mzQC libraries provide functionality to conveniently create mzQC-related data structures, such as a “runQuality” or “setQuality”, and assemble these into a complete mzQC report in the respective programming language.
In addition to (de)serialization, the software libraries provide user-friendly functionality to validate mzQC files. Syntactic validation checks if the structure of mzQC data conforms to the defined syntax rules, ensuring that the data are structured correctly and contain all necessary pieces of information. Semantic validation, on the other hand, involves verifying that the data make sense in their specific context, ensuring that they meaningfully and logically represent MS-related QC concepts and information. All three libraries support syntactic validation, which is based on the mzQC JSON schema. The pymzqc library also supports semantic validation, which interprets the content of mzQC files to ensure the correctness of the QC information, including verification that all QC metrics are represented in an accessible controlled vocabulary or ontology and that the data value types match the definition in the controlled vocabularies. Additionally, a web application to validate mzQC files, powered by pymzqc, is available at https://hupo-psi.github.io/mzQC/validator/.

Code availability

All mzQC supporting software libraries are freely available on GitHub as open source (Table 2), collected in the MS-Quality-Hub organization (https://github.com/MS-Quality-Hub). Additionally, all software libraries can be easily installed using their respective language-preferred toolchains (Table 2). All software libraries follow development best practices, including extensive code documentation, detailed installation instructions, and automated testing using continuous integration.
Table 2. Availability of the Software Libraries in Their Respective Software Package and Source Code Repositories
Software libraryURL
pymzqcPyPI: https://pypi.org/project/pymzqc/
 GitHub: https://github.com/MS-Quality-Hub/pymzqc
rmzqcCRAN: https://cran.r-project.org/web/packages/rmzqc/index.html
 GitHub: https://github.com/MS-Quality-Hub/rmzqc
jzmqcMaven Central: https://central.sonatype.com/artifact/org.lifs-tools/jmzqc
 GitHub: https://github.com/MS-Quality-Hub/jmzqc
All code used in this manuscript to demonstrate the library’s capabilities can be found as open source in a dedicated GitHub repository under the MS-Quality-Hub organization at https://github.com/MS-Quality-hub/mzqclib-manuscript.

Data

We have reanalyzed MS data from a proteomics study of anaerobic respiration in E. coli grown in sulforaphane, obtained via ProteomeXchange (30) with data set identifier PXD040621. (31) In this study, bacterial cultures were grown in the presence of either sulforaphane (10 μM) or 0.034% dimethyl sulfoxide (DMSO) as a control. The study comprised four biological replicates of bacterial growth in both conditions, acquired using a 120 min liquid chromatography gradient measured on an Orbitrap Q-Exactive using data-dependent acquisition.
The data were reanalyzed by converting the raw files to mzML (28) using ThermoRawFileParser (biocontainer version 1.4.0) (32) and sequence database searching using Tide (Crux toolkit version 4.2). (33) We performed target–decoy searching against the UniProtKB (34) E. coli K12 reference proteome (UP000000625, downloaded on October 20, 2023) and configured the search for tryptic peptides with up to three missed cleavages, variable oxidation of methionine, and a precursor mass tolerance of 50 ppm. Spectrum identifications were filtered at a 1% protein-level false discovery rate with crema (version 0.0.10). (35)

Results

To illustrate the functionality and interoperability of the mzQC software libraries, we first used data analysis scripts in different programming languages to compute various QC metric values. Next, the respective mzQC software libraries were used to produce separate mzQC files containing these QC metrics, after which the individual mzQC files were combined into a final QC report. While this demonstration deliberately splits QC metric calculation and mzQC file generation across three programming languages to demonstrate the interoperability of the software libraries, a similar result could be achieved using the respective mzQC software library within a single programming language of preference.
Our example workflow consists of several steps (Figure 1). First, the raw files were converted to mzML and processed using sequence database searching. Next, various QC metrics (Supplementary Table 1) were computed using dedicated scripts in Java, Python, and R and exported to mzQC files using the mzQC software library for the corresponding programming language: (i) jmzqc: As a compiled language, Java is excellently suited to process large amounts of data. Consequently, we used jmzqc combined with jmzml (36) and MSDK (37) to efficiently read the mzML peak files and compute basic QC metrics from the MS data. We calculated the number of chromatograms, the m/z range of the acquired spectra, the retention time range of the acquired spectra, the total ion chromatogram, and the base peak intensities. (ii) rmzqc: Based on R’s emphasis on statistical processing, we used rmzqc to collect statistics of the ion injection parameters at the level of MS and MS/MS spectra. (iii) pymzqc: We used pymzqc in combination with Pyteomics (21) and Pandas (38) to read the peak and identification data; count the number of MS/MS spectra, the number of identified MS/MS spectra, the number of identified peptides, and the number of identified proteins; compute the distribution of the precursor mass deviation of the identifications; evaluate the number of missed cleavages; and find the retention time range during which spectra could be successfully annotated.

Figure 1

Figure 1. mzQC processing workflow. Each software library is separately used to process different QC metrics, which are ultimately combined into a single QC report. Note that the mzQC software libraries do not calculate QC metric values themselves, but rather this functionality is provided by external scripts (“Java app”, “R script”, “Python script”) that subsequently use the respective mzQC library to produce the corresponding mzQC reports.

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This process results in the creation of three mzQC files, each generated by one of the three programming languages. While these can work as independent quality reports, containing a limited set of QC metrics, they can also be combined into a single mzQC report that contains the full information. Therefore, pymzqc was used to merge the data into a final mzQC file. A Jupyter notebook (39) was used for subsequent interactive data analysis and to produce a report that summarizes all QC metrics. Metrics across all MS runs were visualized using a clustered heatmap, with metric values percentile rank scaled (Figure 2).

Figure 2

Figure 2. Heatmap with dendrogram displaying QC metrics across eight MS runs (DMSO controls colored in green, sulforaphane samples colored in blue), clustered by MS runs on the horizontal axis and by QC metrics on the vertical axis. Colors in the heatmap represent percentile ranks calculated from the combined data set, with darker shades indicating lower percentile ranks and lighter shades indicating higher ranks. The QC metrics include the number of acquired MS/MS spectra, MS/MS identifications, peptide identifications, protein identifications, summed total ion current, and number of missed cleavages, among others. The metrics discussed in the text are highlighted in green. See the Jupyter Notebook on our GitHub repository (https://github.com/MS-Quality-Hub/mzqclib-manuscript/) for data analysis and code to generate the plot.

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As a brief example, we performed a visual inspection of the QC metrics to illustrate how QC data can be used to explore the implications of MS data quality (Figure 2). Note that this description is not provided by mzQC directly but is based on the authors' interpretation of the heatmap. When examining the heatmap and its clustering of QC metrics across the eight MS runs, we observe a distinct separation between two specific runs from the rest: the Ecoli_DMSO_rep1_EG-1 control run and the Ecoli_Suf_rep1_EG-5 sulforaphane run. This divergence is driven by a comparatively lower number of MS/MS spectra acquired, peptides and proteins identified, as well as related QC metrics. Interestingly, these two runs exhibit above average total ion currents and the rate of MS/MS spectra that could be identified, suggesting that the lower peptide and protein identification rate is due to a decrease in the number of MS/MS spectra acquired, rather than due to a reduction in the quality of the MS/MS spectra. Additionally, the two outlier runs have a higher proportion of peptides with no missed tryptic cleavages, which might impact the subsequent protein inference. Consequently, deriving biological interpretations from the full experiment may require robust statistical models that are resistant to outliers.
The large contrast in the number of acquired MS/MS spectra and identifications indicates that some caution might need to be exercised when interpreting the results for these two runs to study the effect of sulforaphane on E. coli growth. The heatmap generated by the mzQC pipeline suggests the need for a deeper investigation into the cause of these discrepancies. This will necessitate a broader QC analysis incorporating long-term instrument performance monitoring, including repeated analysis of consistent QC samples. This would provide a more informed basis for interpreting these outlier results within the context of the study.

Conclusion

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The introduction of the mzQC standard file format for quality control in biological mass spectrometry has numerous potential benefits, including increased reproducibility, improved interoperability, and enhanced data sharing among researchers. However, adopting new file formats can be challenging without suitable software libraries to facilitate their integration into bioinformatics software. The development of open software libraries such as pymzqc, rmzqc, and jmzqc is therefore essential for the successful adoption of mzQC. These libraries provide a consistent interface for accessing mzQC files, allowing bioinformatics software developers to easily incorporate mzQC into their tools and workflows. Third-party support for mzQC is already emerging (12) and will be further strengthened by the presented software libraries.
On the basis of a worked use case, we have demonstrated that only a small amount of code is needed to construct an mzQC object in memory, populate it with calculated metric values, and export the data to an mzQC file on disk. Likewise, the interactive notebooks showcase the libraries for conveniently reading data from mzQC for further processing and reporting. This illustrates how the high-level abstractions provided by the mzQC libraries presented here facilitate interactions with mzQC files in different programming languages.
The mzQC software libraries offer several key benefits, including the ability to validate mzQC files, extract information from them, and convert to and from other formats. Additionally, the complexity of the mzQC software libraries is limited, building on native JSON support in the various programming languages. These capabilities are crucial for the development of new QC tools and workflows that can help to improve the reliability and reproducibility of mass spectrometry experiments. Especially as data analysis pipelines become increasingly complex, with separate processing steps implemented in different programming languages, this multilanguage support will be highly beneficial. This could even take the form of polyglot programming, where operations in multiple programming languages are combined in a single analysis notebook. As such, we anticipate that our software libraries will foster a vibrant ecosystem of general and bespoke bioinformatics tools for QC of MS experiments, which will be able to seamlessly interoperate through a common mzQC interface.
In light of these benefits, we invite software developers to start using the mzQC software libraries. Additionally, we welcome any contributions to the mzQC software libraries and the mzQC format, for example by developing complementary libraries in alternative programming languages, such as C++, C#, Rust, or JavaScript. By doing so, the adoption of mzQC in the mass spectrometry and bioinformatics communities will be further facilitated, ultimately leading to better-quality data and more reliable scientific discoveries. To ensure the continued evolution and application of mzQC, we are dedicated to enhancing its ecosystem, including broadening its integration into diverse bioinformatics tools and developing extensive use cases for QC across various biological mass spectrometry applications.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jasms.4c00174.

  • Supplementary Table 1: List of QC metrics considered during the analysis and their accession numbers in the PSI-MS controlled vocabulary (PDF)

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Author Information

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  • Corresponding Authors
  • Authors
    • Nils Hoffmann - Institute for Bio- and Geosciences (IBG-5), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany;  Orcidhttps://orcid.org/0000-0002-6540-6875
    • David Jimenez-Morales - Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, United States
    • Tim Van Den Bossche - Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium;  VIB-UGent Center for Medical Biotechnology, VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium;  Orcidhttps://orcid.org/0000-0002-5916-2587
    • Juan Antonio Vizcaíno - European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge CB10 1SD, United Kingdom;  Orcidhttps://orcid.org/0000-0002-3905-4335
    • David L. Tabb - European Research Institute for the Biology of Ageing, University Medical Center Groningen, Groningen 9713 AV, The Netherlands;  Orcidhttps://orcid.org/0000-0001-7223-578X
    • Wout Bittremieux - Department of Computer Science, University of Antwerp, Antwerpen 2020, Belgium;  Orcidhttps://orcid.org/0000-0002-3105-1359
  • Author Contributions

    C.B. and N.H. contributed equally.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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M.W. would like to acknowledge funding from the H2020 EPIC-XS grant [Grant number 823839], BBSRC ‘Proteomics DIA’ [BB/P024599/1], and from The Wellcome Trust [208391/Z/17/Z]. Additionally, J.A.V. would like to acknowledge EMBL core funding. This work was supported in part by the de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) and ELIXIR-DE (Forschungszentrum Jülich and W-de.NBI-001, W-de.NBI-004, W-de.NBI-008, W-de.NBI-010, W-de.NBI-013, W-de.NBI-014, W-de.NBI-016, and W-de.NBI-022). T.V.D.B. acknowledges funding from the Research Foundation Flanders (FWO) [1286824N]. W.B. acknowledges support by the University of Antwerp Research Fund.

References

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This article references 39 other publications.

  1. 1
    Bittremieux, W.; Tabb, D. L.; Impens, F.; Staes, A.; Timmerman, E.; Martens, L.; Laukens, K. Quality Control in Mass Spectrometry-Based Proteomics. Mass Spectrom. Rev. 2018, 37 (5), 697– 711,  DOI: 10.1002/mas.21544
    Google Scholar
    1
    Quality control in mass spectrometry-based proteomics
    Bittremieux, Wout; Tabb, David L.; Impens, Francis; Staes, An; Timmerman, Evy; Martens, Lennart; Laukens, Kris
    Mass Spectrometry Reviews (2018), 37 (5), 697-711CODEN: MSRVD3; ISSN:0277-7037. (John Wiley & Sons, Inc.)
    A review. Mass spectrometry is a highly complex anal. technique and mass spectrometry-based proteomics expts. can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry expt. consists of multiple different phases including the sample prepn., liq. chromatog., mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry expt. occurring in all of these steps, and we discuss how to monitor and remedy the neg. influences on the exptl. results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the exptl. workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.
  2. 2
    Baker, M. 1,500 Scientists Lift the Lid on Reproducibility. Nature 2016, 533 (7604), 452– 454,  DOI: 10.1038/533452a
    Google Scholar
    2
    1,500 scientists lift the lid on reproducibility
    Baker, Monya
    Nature (London, United Kingdom) (2016), 533 (7604), 452-454CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    Survey sheds light on the 'crisis' rocking research.
  3. 3
    Rodriguez, H.; Snyder, M.; Uhlén, M.; Andrews, P.; Beavis, R.; Borchers, C.; Chalkley, R. J.; Cho, S. Y.; Cottingham, K.; Dunn, M.; Dylag, T.; Edgar, R.; Hare, P.; Heck, A. J. R.; Hirsch, R. F.; Kennedy, K.; Kolar, P.; Kraus, H.-J.; Mallick, P.; Nesvizhskii, A.; Ping, P.; Pontén, F.; Yang, L.; Yates, J. R.; Stein, S. E.; Hermjakob, H.; Kinsinger, C. R.; Apweiler, R. Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: The Amsterdam Principles. J. Proteome Res. 2009, 8 (7), 3689– 3692,  DOI: 10.1021/pr900023z
    Google Scholar
    3
    Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: The Amsterdam Principles
    Rodriguez, Henry; Snyder, Mike; Uhlen, Mathias; Andrews, Phil; Beavis, Ronald; Borchers, Christoph; Chalkley, Robert J.; Cho, Sang Yun; Cottingham, Katie; Dunn, Michael; Dylag, Tomasz; Edgar, Ron; Hare, Peter; Heck, Albert J. R.; Hirsch, Roland F.; Kennedy, Karen; Kolar, Patrik; Kraus, Hans-Joachim; Mallick, Parag; Nesvizhskii, Alexey; Ping, Peipei; Ponten, Fredrik; Yang, Liming; Yates, John R.; Stein, Stephen E.; Hermjakob, Henning; Kinsinger, Christopher R.; Apweiler, Rolf
    Journal of Proteome Research (2009), 8 (7), 3689-3692CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    Policies supporting the rapid and open sharing of genomic data have directly fueled the accelerated pace of discovery in large-scale genomics research. The proteomics community is starting to implement analogous policies and infrastructure for making large-scale proteomics data widely available on a precompetitive basis. On August 14, 2008, the National Cancer Institute (NCI) convened the "International Summit on Proteomics Data Release and Sharing Policy" in Amsterdam, The Netherlands, to identify and address potential roadblocks to rapid and open access to data. The six principles agreed upon by key stakeholders at the summit addressed issues surrounding (1) timing, (2) comprehensiveness, (3) format, (4) deposition to repositories, (5) quality metrics, and (6) responsibility for proteomics data release. This summit report explores various approaches to develop a framework of data release and sharing principles that will most effectively fulfill the needs of the funding agencies and the research community.
  4. 4
    Kinsinger, C. R.; Apffel, J.; Baker, M.; Bian, X.; Borchers, C. H.; Bradshaw, R.; Brusniak, M.-Y.; Chan, D. W.; Deutsch, E. W.; Domon, B.; Gorman, J.; Grimm, R.; Hancock, W.; Hermjakob, H.; Horn, D.; Hunter, C.; Kolar, P.; Kraus, H.-J.; Langen, H.; Linding, R.; Moritz, R. L.; Omenn, G. S.; Orlando, R.; Pandey, A.; Ping, P.; Rahbar, A.; Rivers, R.; Seymour, S. L.; Simpson, R. J.; Slotta, D.; Smith, R. D.; Stein, S. E.; Tabb, D. L.; Tagle, D.; Yates, J. R. I.; Rodriguez, H. Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles). Mol. Cell. Proteomics 2011, 10 (12), O111.015446,  DOI: 10.1074/mcp.O111.015446
    Google Scholar
    There is no corresponding record for this reference.
  5. 5
    Rudnick, P. A.; Clauser, K. R.; Kilpatrick, L. E.; Tchekhovskoi, D. V.; Neta, P.; Blonder, N.; Billheimer, D. D.; Blackman, R. K.; Bunk, D. M.; Cardasis, H. L.; Ham, A.-J. L.; Jaffe, J. D.; Kinsinger, C. R.; Mesri, M.; Neubert, T. A.; Schilling, B.; Tabb, D. L.; Tegeler, T. J.; Vega-Montoto, L.; Variyath, A. M.; Wang, M.; Wang, P.; Whiteaker, J. R.; Zimmerman, L. J.; Carr, S. A.; Fisher, S. J.; Gibson, B. W.; Paulovich, A. G.; Regnier, F. E.; Rodriguez, H.; Spiegelman, C.; Tempst, P.; Liebler, D. C.; Stein, S. E. Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses. Mol. Cell. Proteomics 2010, 9 (2), 225– 241,  DOI: 10.1074/mcp.M900223-MCP200
    Google Scholar
    5
    Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses
    Rudnick, Paul A.; Clauser, Karl R.; Kilpatrick, Lisa E.; Tchekhovskoi, Dmitrii V.; Neta, Pedatsur; Blonder, Niksa; Billheimer, Dean D.; Blackman, Ronald K.; Bunk, David M.; Cardasis, Helene L.; Ham, Amy-Joan L.; Jaffe, Jacob D.; Kinsinger, Christopher R.; Mesri, Mehdi; Neubert, Thomas A.; Schilling, Birgit; Tabb, David L.; Tegeler, Tony J.; Vega-Montoto, Lorenzo; Variyath, Asokan Mulayath; Wang, Mu; Wang, Pei; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Carr, Steven A.; Fisher, Susan J.; Gibson, Bradford W.; Paulovich, Amanda G.; Regnier, Fred E.; Rodriguez, Henry; Spiegelman, Cliff; Tempst, Paul; Liebler, Daniel C.; Stein, Stephen E.
    Molecular and Cellular Proteomics (2010), 9 (2), 225-241CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
    A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quant. assessment of system performance and evaluation of tech. variability. Here we describe 46 system performance metrics for monitoring chromatog. performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlab. studies conducted under a common std. operating procedure identified outlier data and provided clues to specific causes. Moreover, interlab. variation reflected by the metrics indicates which system components vary the most between labs. Application of these metrics enables rational, quant. quality assessment for proteomics and other LC-MS/MS anal. applications.
  6. 6
    Ma, Z.-Q.; Polzin, K. O.; Dasari, S.; Chambers, M. C.; Schilling, B.; Gibson, B. W.; Tran, B. Q.; Vega-Montoto, L.; Liebler, D. C.; Tabb, D. L. QuaMeter: Multivendor Performance Metrics for LC-MS/MS Proteomics Instrumentation. Anal. Chem. 2012, 84 (14), 5845– 5850,  DOI: 10.1021/ac300629p
    Google Scholar
    6
    QuaMeter: Multivendor Performance Metrics for LC-MS/MS Proteomics Instrumentation
    Ma, Ze-Qiang; Polzin, Kenneth O.; Dasari, Surendra; Chambers, Matthew C.; Schilling, Birgit; Gibson, Bradford W.; Tran, Bao Q.; Vega-Montoto, Lorenzo; Liebler, Daniel C.; Tabb, David L.
    Analytical Chemistry (Washington, DC, United States) (2012), 84 (14), 5845-5850CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
    LC-MS/MS-based proteomics studies rely on stable anal. system performance that can be evaluated by objective criteria. The National Institute of Stds. and Technol. (NIST) introduced the MSQC software to compute diverse metrics from exptl. LC-MS/MS data, enabling quality anal. and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufd. by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.
  7. 7
    Pichler, P.; Mazanek, M.; Dusberger, F.; Weilnböck, L.; Huber, C. G.; Stingl, C.; Luider, T. M.; Straube, W. L.; Köcher, T.; Mechtler, K. SIMPATIQCO: A Server-Based Software Suite Which Facilitates Monitoring the Time Course of LC-MS Performance Metrics on Orbitrap Instruments. J. Proteome Res. 2012, 11 (11), 5540– 5547,  DOI: 10.1021/pr300163u
    Google Scholar
    7
    SIMPATIQCO: A Server-Based Software Suite Which Facilitates Monitoring the Time Course of LC-MS Performance Metrics on Orbitrap Instruments
    Pichler, Peter; Mazanek, Michael; Dusberger, Frederico; Weilnboeck, Lisa; Huber, Christian G.; Stingl, Christoph; Luider, Theo M.; Straube, Werner L.; Koecher, Thomas; Mechtler, Karl
    Journal of Proteome Research (2012), 11 (11), 5540-5547CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    While the performance of liq. chromatog. (LC) and mass spectrometry (MS) instrumentation continues to increase, applications such as analyses of complete or near-complete proteomes and quant. studies require const. and optimal system performance. For this reason, research labs. and core facilities alike are recommended to implement quality control (QC) measures as part of their routine workflows. Many labs. perform sporadic quality control checks. However, successive and systematic longitudinal monitoring of system performance would be facilitated by dedicated automatic or semiautomatic software solns. that aid an effortless anal. and display of QC metrics over time. We present the software package SIMPATIQCO (SIMPle AuTomatIc Quality COntrol) designed for evaluation of data from LTQ Orbitrap, Q-Exactive, LTQ FT, and LTQ instruments. A centralized SIMPATIQCO server can process QC data from multiple instruments. The software calcs. QC metrics supervising every step of data acquisition from LC and electrospray to MS. For each QC metric the software learns the range indicating adequate system performance from the uploaded data using robust statistics. Results are stored in a database and can be displayed in a comfortable manner from any computer in the lab. via a web browser. QC data can be monitored for individual LC runs as well as plotted over time. SIMPATIQCO thus assists the longitudinal monitoring of important QC metrics such as peptide elution times, peak widths, intensities, total ion current (TIC) as well as sensitivity, and overall LC-MS system performance; in this way the software also helps identify potential problems. The SIMPATIQCO software package is available free of charge.
  8. 8
    Bielow, C.; Mastrobuoni, G.; Kempa, S. Proteomics Quality Control: Quality Control Software for MaxQuant Results. J. Proteome Res. 2016, 15 (3), 777– 787,  DOI: 10.1021/acs.jproteome.5b00780
    Google Scholar
    8
    Proteomics Quality Control: Quality Control Software for MaxQuant Results
    Bielow, Chris; Mastrobuoni, Guido; Kempa, Stefan
    Journal of Proteome Research (2016), 15 (3), 777-787CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    Mass spectrometry-based proteomics coupled to liq. chromatog. has matured into an automatized, high-throughput technol., producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality anal. (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream anal. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report contg. a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of exptl. designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant's Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC.
  9. 9
    Chiva, C.; Olivella, R.; Borràs, E.; Espadas, G.; Pastor, O.; Solé, A.; Sabidó, E. QCloud: A Cloud-Based Quality Control System for Mass Spectrometry-Based Proteomics Laboratories. PLOS ONE 2018, 13 (1), e0189209  DOI: 10.1371/journal.pone.0189209
    Google Scholar
    9
    QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories
    Chiva, Cristina; Olivella, Roger; Borras, Eva; Espadas, Guadalupe; Pastor, Olga; Sole, Amanda; Sabido, Eduard
    PLoS One (2018), 13 (1), e0189209/1-e0189209/14CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
    The increasing no. of biomedical and translational applications in mass spectrometrybased proteomics poses new anal. challenges and raises the need for automated quality control systems. Despite previous efforts to set std. file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics labs., which limits the acquisition of high-quality results, inter-lab. comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics labs. in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community. QCloud system is an open source project, publicly available under a Creative Commons License Attribution- ShareAlike 4.0.
  10. 10
    Broadhurst, D.; Goodacre, R.; Reinke, S. N.; Kuligowski, J.; Wilson, I. D.; Lewis, M. R.; Dunn, W. B. Guidelines and Considerations for the Use of System Suitability and Quality Control Samples in Mass Spectrometry Assays Applied in Untargeted Clinical Metabolomic Studies. Metabolomics 2018, 14 (6), 72,  DOI: 10.1007/s11306-018-1367-3
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    Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies
    Broadhurst David; Reinke Stacey N; Goodacre Royston; Reinke Stacey N; Kuligowski Julia; Wilson Ian D; Lewis Matthew R; Dunn Warwick B; Dunn Warwick B; Dunn Warwick B
    Metabolomics : Official journal of the Metabolomic Society (2018), 14 (6), 72 ISSN:1573-3882.
    BACKGROUND: Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition. AIM OF REVIEW: This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported. KEY SCIENTIFIC CONCEPTS OF REVIEW: System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.
  11. 11
    Stratton, K. G.; Webb-Robertson, B.-J. M.; McCue, L. A.; Stanfill, B.; Claborne, D.; Godinez, I.; Johansen, T.; Thompson, A. M.; Burnum-Johnson, K. E.; Waters, K. M.; Bramer, L. M. pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data. J. Proteome Res. 2019, 18 (3), 1418– 1425,  DOI: 10.1021/acs.jproteome.8b00760
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    11
    pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data
    Stratton, Kelly G.; Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann; Stanfill, Bryan; Claborne, Daniel; Godinez, Iobani; Johansen, Thomas; Thompson, Allison M.; Burnum-Johnson, Kristin E.; Waters, Katrina M.; Bramer, Lisa M.
    Journal of Proteome Research (2019), 18 (3), 1418-1425CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    Prior to statistical anal. of mass spectrometry (MS) data, quality control (QC) of the identified biomol. peak intensities is imperative for reducing process-based sources of variation and extreme biol. outliers. Without this step, statistical results can be biased. Addnl., liq. chromatog.-MS proteomics data present inherent challenges due to large amts. of missing data that require special consideration during statistical anal. While a no. of R packages exist to address these challenges individually, there is no single R package that addresses all of them. The authors present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data anal. (EDA), visualization, and statistical anal. robust to missing data. Example anal. using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quant. and qual. statistical test, 19 proteins whose statistical significance would have been missed by a quant. test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.
  12. 12
    Naake, T.; Rainer, J.; Huber, W. MsQuality: An Interoperable Open-Source Package for the Calculation of Standardized Quality Metrics of Mass Spectrometry Data. Bioinformatics 2023, 39 (10), btad618,  DOI: 10.1093/bioinformatics/btad618
    Google Scholar
    There is no corresponding record for this reference.
  13. 13
    Kirwan, J. A.; Gika, H.; Beger, R. D.; Bearden, D.; Dunn, W. B.; Goodacre, R.; Theodoridis, G.; Witting, M.; Yu, L.-R.; Wilson, I. D. the metabolomics Quality Assurance and Quality Control Consortium (mQACC). Quality Assurance and Quality Control Reporting in Untargeted Metabolic Phenotyping: mQACC Recommendations for Analytical Quality Management. Metabolomics 2022, 18 (9), 70,  DOI: 10.1007/s11306-022-01926-3
    Google Scholar
    13
    Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management
    Kirwan, Jennifer A.; Gika, Helen; Beger, Richard D.; Bearden, Dan; Dunn, Warwick B.; Goodacre, Royston; Theodoridis, Georgios; Witting, Michael; Yu, Li-Rong; Wilson, Ian D.; the metabolomics Quality Assurance and Quality Control Consortium
    Metabolomics (2022), 18 (9), 70CODEN: METAHQ; ISSN:1573-3890. (Springer)
    Abstr.: Background: Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanal. of the body of work. Objective: The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and NMR spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to anal. best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as online resources. Key reporting practices: Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, prepn. and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrixes and sample prepn., the use of test mixts. and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.
  14. 14
    Köfeler, H. C.; Ahrends, R.; Baker, E. S.; Ekroos, K.; Han, X.; Hoffmann, N.; Holčapek, M.; Wenk, M. R.; Liebisch, G. Recommendations for Good Practice in MS-Based Lipidomics. J. Lipid Res. 2021, 62, 100138,  DOI: 10.1016/j.jlr.2021.100138
    Google Scholar
    14
    Recommendations for good practice in MS-based lipidomics
    Kofeler Harald C; Ahrends Robert; Baker Erin S; Ekroos Kim; Han Xianlin; Hoffmann Nils; Holcapek Michal; Wenk Markus R; Liebisch Gerhard
    Journal of lipid research (2021), 62 (), 100138 ISSN:.
    In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.
  15. 15
    McDonald, J. G.; Ejsing, C. S.; Kopczynski, D.; Holčapek, M.; Aoki, J.; Arita, M.; Arita, M.; Baker, E. S.; Bertrand-Michel, J.; Bowden, J. A.; Brügger, B.; Ellis, S. R.; Fedorova, M.; Griffiths, W. J.; Han, X.; Hartler, J.; Hoffmann, N.; Koelmel, J. P.; Köfeler, H. C.; Mitchell, T. W.; O’Donnell, V. B.; Saigusa, D.; Schwudke, D.; Shevchenko, A.; Ulmer, C. Z.; Wenk, M. R.; Witting, M.; Wolrab, D.; Xia, Y.; Ahrends, R.; Liebisch, G.; Ekroos, K. Introducing the Lipidomics Minimal Reporting Checklist. Nat. Metab. 2022, 4 (9), 1086– 1088,  DOI: 10.1038/s42255-022-00628-3
    Google Scholar
    15
    Introducing the Lipidomics Minimal Reporting Checklist
    McDonald Jeffrey G; Ejsing Christer S; Ejsing Christer S; Kopczynski Dominik; Ahrends Robert; Holcapek Michal; Wolrab Denise; Aoki Junken; Aoki Junken; Arita Makoto; Arita Masanori; Baker Erin S; Bertrand-Michel Justine; Bowden John A; Brugger Britta; Ellis Shane R; Ellis Shane R; Mitchell Todd W; Fedorova Maria; Griffiths William J; Han Xianlin; Han Xianlin; Hartler Jurgen; Hartler Jurgen; Hoffmann Nils; Koelmel Jeremy P; Kofeler Harald C; O'Donnell Valerie B; Saigusa Daisuke; Schwudke Dominik; Schwudke Dominik; Schwudke Dominik; Shevchenko Andrej; Ulmer Candice Z; Wenk Markus R; Witting Michael; Xia Yu; Liebisch Gerhard; Ekroos Kim
    Nature metabolism (2022), 4 (9), 1086-1088 ISSN:.
    There is no expanded citation for this reference.
  16. 16
    Bittremieux, W.; Valkenborg, D.; Martens, L.; Laukens, K. Computational Quality Control Tools for Mass Spectrometry Proteomics. PROTEOMICS 2017, 17 (3–4), 1600159,  DOI: 10.1002/pmic.201600159
    Google Scholar
    There is no corresponding record for this reference.
  17. 17
    Bittremieux, W.; Walzer, M.; Tenzer, S.; Zhu, W.; Salek, R. M.; Eisenacher, M.; Tabb, D. L. The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry. Anal. Chem. 2017, 89 (8), 4474– 4479,  DOI: 10.1021/acs.analchem.6b04310
    Google Scholar
    17
    The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry
    Bittremieux, Wout; Walzer, Mathias; Tenzer, Stefan; Zhu, Weimin; Salek, Reza M.; Eisenacher, Martin; Tabb, David L.
    Analytical Chemistry (Washington, DC, United States) (2017), 89 (8), 4474-4479CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
    To have confidence in results acquired during biol. mass spectrometry expts., a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Stds. Initiative established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic tech. necessities required to support extensive quality control for any type of mass spectrometry-based workflow. The authors want to emphasize that this is an open community effort, and the authors seek participation from all scientists with an interest in this field.
  18. 18
    Deutsch, E. W.; Vizcaíno, J. A.; Jones, A. R.; Binz, P.-A.; Lam, H.; Klein, J.; Bittremieux, W.; Perez-Riverol, Y.; Tabb, D. L.; Walzer, M.; Ricard-Blum, S.; Hermjakob, H.; Neumann, S.; Mak, T. D.; Kawano, S.; Mendoza, L.; Van Den Bossche, T.; Gabriels, R.; Bandeira, N.; Carver, J.; Pullman, B.; Sun, Z.; Hoffmann, N.; Shofstahl, J.; Zhu, Y.; Licata, L.; Quaglia, F.; Tosatto, S. C. E.; Orchard, S. E. Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work. J. Proteome Res. 2023, 22 (2), 287– 301,  DOI: 10.1021/acs.jproteome.2c00637
    Google Scholar
    18
    Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work
    Deutsch, Eric W.; Vizcaino, Juan Antonio; Jones, Andrew R.; Binz, Pierre-Alain; Lam, Henry; Klein, Joshua; Bittremieux, Wout; Perez-Riverol, Yasset; Tabb, David L.; Walzer, Mathias; Ricard-Blum, Sylvie; Hermjakob, Henning; Neumann, Steffen; Mak, Tytus D.; Kawano, Shin; Mendoza, Luis; Van Den Bossche, Tim; Gabriels, Ralf; Bandeira, Nuno; Carver, Jeremy; Pullman, Benjamin; Sun, Zhi; Hoffmann, Nils; Shofstahl, Jim; Zhu, Yunping; Licata, Luana; Quaglia, Federica; Tosatto, Silvio C. E.; Orchard, Sandra E.
    Journal of Proteome Research (2023), 22 (2), 287-301CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    A review. The Human Proteome Organization (HUPO) Proteomics Stds. 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 stds. We briefly describe the current state of the many existing PSI stds., 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 stds. 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.
  19. 19
    Mayer, G.; Montecchi-Palazzi, L.; Ovelleiro, D.; Jones, A. R.; Binz, P.-A.; Deutsch, E. W.; Chambers, M.; Kallhardt, M.; Levander, F.; Shofstahl, J.; Orchard, S.; Vizcaino, J. A.; Hermjakob, H.; Stephan, C.; Meyer, H. E.; Eisenacher, M. The HUPO Proteomics Standards Initiative- Mass Spectrometry Controlled Vocabulary. Database 2013, 2013, bat009,  DOI: 10.1093/database/bat009
    Google Scholar
    There is no corresponding record for this reference.
  20. 20
    Röst, H. L.; Schmitt, U.; Aebersold, R.; Malmström, L. pyOpenMS: A Python-Based Interface to the OpenMS Mass-Spectrometry Algorithm Library. PROTEOMICS 2014, 14 (1), 74– 77,  DOI: 10.1002/pmic.201300246
    Google Scholar
    20
    pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library
    Rost Hannes L; Schmitt Uwe; Aebersold Ruedi; Malmstrom Lars
    Proteomics (2014), 14 (1), 74-7 ISSN:.
    pyOpenMS is an open-source, Python-based interface to the C++ OpenMS library, providing facile access to a feature-rich, open-source algorithm library for MS-based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping, and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open-source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3-clause BSD licence).
  21. 21
    Levitsky, L. I.; Klein, J. A.; Ivanov, M. V.; Gorshkov, M. Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework. J. Proteome Res. 2019, 18 (2), 709– 714,  DOI: 10.1021/acs.jproteome.8b00717
    Google Scholar
    21
    Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework
    Levitsky, Lev I.; Klein, Joshua A.; Ivanov, Mark V.; Gorshkov, Mikhail V.
    Journal of Proteome Research (2019), 18 (2), 709-714CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    A review. Many of the novel ideas that drive today's proteomic technologies are focused essentially on exptl. or data-processing workflows. The latter are implemented and published in a no. of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic labs., 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.
  22. 22
    Huber, F.; Verhoeven, S.; Meijer, C.; Spreeuw, H.; Castilla, E.; Geng, C.; van der Hooft, J.; Rogers, S.; Belloum, A.; Diblen, F.; Spaaks, J. Matchms - Processing and Similarity Evaluation of Mass Spectrometry Data. J. Open Source Softw. 2020, 5 (52), 2411,  DOI: 10.21105/joss.02411
    Google Scholar
    There is no corresponding record for this reference.
  23. 23
    Bittremieux, W.; Levitsky, L.; Pilz, M.; Sachsenberg, T.; Huber, F.; Wang, M.; Dorrestein, P. C. Unified and Standardized Mass Spectrometry Data Processing in Python Using Spectrum_utils. J. Proteome Res. 2023, 22 (2), 625– 631,  DOI: 10.1021/acs.jproteome.2c00632
    Google Scholar
    There is no corresponding record for this reference.
  24. 24
    Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal. Chem. 2006, 78 (3), 779– 787,  DOI: 10.1021/ac051437y
    Google Scholar
    24
    XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification
    Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, Gary
    Analytical Chemistry (2006), 78 (3), 779-787CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
    Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity detn., and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biol. origin. Here we introduce an LC/MS-based data anal. approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal stds., the method dynamically identifies hundreds of endogenous metabolites for use as stds., calcg. a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
  25. 25
    Gatto, L.; Gibb, S.; Rainer, J. MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data. J. Proteome Res. 2021, 20 (1), 1063– 1069,  DOI: 10.1021/acs.jproteome.0c00313
    Google Scholar
    25
    MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data
    Gatto, Laurent; Gibb, Sebastian; Rainer, Johannes
    Journal of Proteome Research (2021), 20 (1), 1063-1069CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    We present version 2 of the MSnbase R/Bioconductor package. MSnbase provides infrastructure for the manipulation, processing, and visualization of mass spectrometry data. We focus on the new on-disk infrastructure, that allows the handling of large raw mass spectrometry expts. on commodity hardware and illustrate how the package is used for elegant data processing, method development, and visualization.
  26. 26
    Barsnes, H.; Vaudel, M.; Colaert, N.; Helsens, K.; Sickmann, A.; Berven, F. S.; Martens, L. Compomics-Utilities: An Open-Source Java Library for Computational Proteomics. BMC Bioinformatics 2011, 12 (1), 70,  DOI: 10.1186/1471-2105-12-70
    Google Scholar
    There is no corresponding record for this reference.
  27. 27
    Schmid, R.; Heuckeroth, S.; Korf, A.; Smirnov, A.; Myers, O.; Dyrlund, T. S.; Bushuiev, R.; Murray, K. J.; Hoffmann, N.; Lu, M.; Sarvepalli, A.; Zhang, Z.; Fleischauer, M.; Dührkop, K.; Wesner, M.; Hoogstra, S. J.; Rudt, E.; Mokshyna, O.; Brungs, C.; Ponomarov, K.; Mutabdžija, L.; Damiani, T.; Pudney, C. J.; Earll, M.; Helmer, P. O.; Fallon, T. R.; Schulze, T.; Rivas-Ubach, A.; Bilbao, A.; Richter, H.; Nothias, L.-F.; Wang, M.; Orešič, M.; Weng, J.-K.; Böcker, S.; Jeibmann, A.; Hayen, H.; Karst, U.; Dorrestein, P. C.; Petras, D.; Du, X.; Pluskal, T. Integrative Analysis of Multimodal Mass Spectrometry Data in MZmine 3. Nat. Biotechnol. 2023, 41 (4), 447– 449,  DOI: 10.1038/s41587-023-01690-2
    Google Scholar
    27
    Integrative analysis of multimodal mass spectrometry data in MZmine 3
    Schmid, Robin; Heuckeroth, Steffen; Korf, Ansgar; Smirnov, Aleksandr; Myers, Owen; Dyrlund, Thomas S.; Bushuiev, Roman; Murray, Kevin J.; Hoffmann, Nils; Lu, Miaoshan; Sarvepalli, Abinesh; Zhang, Zheng; Fleischauer, Markus; Duhrkop, Kai; Wesner, Mark; Hoogstra, Shawn J.; Rudt, Edward; Mokshyna, Olena; Brungs, Corinna; Ponomarov, Kirill; Mutabdzija, Lana; Damiani, Tito; Pudney, Chris J.; Earll, Mark; Helmer, Patrick O.; Fallon, Timothy R.; Schulze, Tobias; Rivas-Ubach, Albert; Bilbao, Aivett; Richter, Henning; Nothias, Louis-Felix; Wang, Mingxun; Oresic, Matej; Weng, Jing-Ke; Bocker, Sebastian; Jeibmann, Astrid; Hayen, Heiko; Karst, Uwe; Dorrestein, Pieter C.; Petras, Daniel; Du, Xiuxia; Pluskal, Tomas
    Nature Biotechnology (2023), 41 (4), 447-449CODEN: NABIF9; ISSN:1087-0156. (Nature Portfolio)
    There is no expanded citation for this reference.
  28. 28
    Martens, L.; Chambers, M.; Sturm, M.; Kessner, D.; Levander, F.; Shofstahl, J.; Tang, W. H.; Römpp, A.; Neumann, S.; Pizarro, A. D.; Montecchi-Palazzi, L.; Tasman, N.; Coleman, M.; Reisinger, F.; Souda, P.; Hermjakob, H.; Binz, P.-A.; Deutsch, E. W. mzML─a Community Standard for Mass Spectrometry Data. Mol. Cell. Proteomics 2011, 10 (1), R110.000133,  DOI: 10.1074/mcp.R110.000133
    Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Griss, J.; Jones, A. R.; Sachsenberg, T.; Walzer, M.; Gatto, L.; Hartler, J.; Thallinger, G. G.; Salek, R. M.; Steinbeck, C.; Neuhauser, N.; Cox, J.; Neumann, S.; Fan, J.; Reisinger, F.; Xu, Q.-W.; del Toro, N.; Perez-Riverol, Y.; Ghali, F.; Bandeira, N.; Xenarios, I.; Kohlbacher, O.; Vizcaíno, J. A.; Hermjakob, H. The mzTab Data Exchange Format: Communicating Mass-Spectrometry-Based Proteomics and Metabolomics Experimental Results to a Wider Audience. Mol. Cell. Proteomics 2014, 13 (10), 2765– 2775,  DOI: 10.1074/mcp.O113.036681
    Google Scholar
    29
    The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience
    Griss, Johannes; Jones, Andrew R.; Sachsenberg, Timo; Walzer, Mathias; Gatto, Laurent; Hartler, Jurgen; Thallinger, Gerhard G.; Salek, Reza M.; Steinbeck, Christoph; Neuhauser, Nadin; Cox, Jurgen; Neumann, Steffen; Fan, Jun; Reisinger, Florian; Xu, Qing-Wei; del Toro, Noemi; Perez-Riverol, Yasset; Ghali, Fawaz; Bandeira, Nuno; Xenarios, Ioannis; Kohlbacher, Oliver; Vizcaino, Juan Antonio; Hermjakob, Henning
    Molecular & Cellular Proteomics (2014), 13 (10), 2765-2775CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
    We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. MzTab is intended as a lightwt. supplement to the existing std. XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. MzTab files can contain protein, peptide, and small mol. identifications together with exptl. metadata and basic quant. information. The format is not intended to store the complete exptl. evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the exptl. design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biol. community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive addnl. documentation can be found online.
  30. 30
    Deutsch, E. W.; Bandeira, N.; Sharma, V.; Perez-Riverol, Y.; Carver, J. J.; Kundu, D. J.; García-Seisdedos, D.; Jarnuczak, A. F.; Hewapathirana, S.; Pullman, B. S.; Wertz, J.; Sun, Z.; Kawano, S.; Okuda, S.; Watanabe, Y.; Hermjakob, H.; MacLean, B.; MacCoss, M. J.; Zhu, Y.; Ishihama, Y.; Vizcaíno, J. A. The ProteomeXchange Consortium in 2020: Enabling “big Data” Approaches in Proteomics. Nucleic Acids Res. 2019, 48 (D1), D1145– D1152,  DOI: 10.1093/nar/gkz984
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Marshall, S. A.; Young, R. B.; Lewis, J. M.; Rutten, E. L.; Gould, J.; Barlow, C. K.; Giogha, C.; Marcelino, V. R.; Fields, N.; Schittenhelm, R. B.; Hartland, E. L.; Scott, N. E.; Forster, S. C.; Gulliver, E. L. The Broccoli-Derived Antioxidant Sulforaphane Changes the Growth of Gastrointestinal Microbiota, Allowing for the Production of Anti-Inflammatory Metabolites. J. Funct. Foods 2023, 107, 105645,  DOI: 10.1016/j.jff.2023.105645
    Google Scholar
    There is no corresponding record for this reference.
  32. 32
    Hulstaert, N.; Shofstahl, J.; Sachsenberg, T.; Walzer, M.; Barsnes, H.; Martens, L.; Perez-Riverol, Y. ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. J. Proteome Res. 2020, 19 (1), 537– 542,  DOI: 10.1021/acs.jproteome.9b00328
    Google Scholar
    32
    ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion
    Hulstaert, Niels; Shofstahl, Jim; Sachsenberg, Timo; Walzer, Mathias; Barsnes, Harald; Martens, Lennart; Perez-Riverol, Yasset
    Journal of Proteome Research (2020), 19 (1), 537-542CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the no. of samples analyzed per expt. as well as by the growing amt. of data obtained in each anal. run. In order to process these large amts. of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI std. file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addn., we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quant. results.
  33. 33
    Park, C. Y.; Klammer, A. A.; Käll, L.; MacCoss, M. J.; Noble, W. S. Rapid and Accurate Peptide Identification from Tandem Mass Spectra. J. Proteome Res. 2008, 7 (7), 3022– 3027,  DOI: 10.1021/pr800127y
    Google Scholar
    33
    Rapid and Accurate Peptide Identification from Tandem Mass Spectra
    Park, Christopher Y.; Klammer, Aaron A.; Kall, Lukas; MacCoss, Michael J.; Noble, William S.
    Journal of Proteome Research (2008), 7 (7), 3022-3027CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
    Mass spectrometry, the core technol. in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biol. sample. Currently, the primary bottleneck in this type of expt. is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program SEQUEST. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate ests. Crux also implements two recently described postprocessing methods: a p value calcn. based upon fitting a Weibull distribution to the obsd. scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.
  34. 34
    The UniProt Consortium UniProt: A Hub for Protein Information. Nucleic Acids Res. 2015, 43 (D1), D204– D212,  DOI: 10.1093/nar/gku989 .
    Google Scholar
    There is no corresponding record for this reference.
  35. 35
    Lin, A.; See, D.; Fondrie, W. E.; Keich, U.; Noble, W. S. Target-Decoy False Discovery Rate Estimation Using Crema. PROTEOMICS 2024, 24 (8), 2300084,  DOI: 10.1002/pmic.202300084
    Google Scholar
    There is no corresponding record for this reference.
  36. 36
    Côté, R. G.; Reisinger, F.; Martens, L. jmzML, an Open-Source Java API for mzML, the PSI Standard for MS Data. PROTEOMICS 2010, 10 (7), 1332– 1335,  DOI: 10.1002/pmic.200900719
    Google Scholar
    There is no corresponding record for this reference.
  37. 37
    Pluskal, T.; Hoffmann, N.; Du, X.; Weng, J.-K. Mass Spectrometry Development Kit (MSDK): A Java Library for Mass Spectrometry Data Processing. In New Developments in Mass Spectrometry; Winkler, R., Ed.; Royal Society of Chemistry: Cambridge, 2020; pp 399– 405,  DOI: 10.1039/9781788019880-00399 .
    Google Scholar
    There is no corresponding record for this reference.
  38. 38
    McKinney, W. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference; van der Walt, S.; Millman, J., Eds.; Austin, Texas, USA, 2010; pp 51– 56,  DOI: 10.25080/Majora-92bf1922-00a .
    Google Scholar
    There is no corresponding record for this reference.
  39. 39
    Thomas, K.; Benjamin, R.-K.; Fernando, P.; Brian, G.; Matthias, B.; Jonathan, F.; Kyle, K.; Jessica, H.; Jason, G.; Sylvain, C.; Paul, I.; Damián, A.; Safia, A.; Carol, W. Jupyter Development Team. Jupyter Notebooks - A Publishing Format for Reproducible Computational Workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas; IOS Press, 2016; pp 87– 90.
    Google Scholar
    There is no corresponding record for this reference.

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  • Abstract

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    Figure 1

    Figure 1. mzQC processing workflow. Each software library is separately used to process different QC metrics, which are ultimately combined into a single QC report. Note that the mzQC software libraries do not calculate QC metric values themselves, but rather this functionality is provided by external scripts (“Java app”, “R script”, “Python script”) that subsequently use the respective mzQC library to produce the corresponding mzQC reports.

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    Figure 2

    Figure 2. Heatmap with dendrogram displaying QC metrics across eight MS runs (DMSO controls colored in green, sulforaphane samples colored in blue), clustered by MS runs on the horizontal axis and by QC metrics on the vertical axis. Colors in the heatmap represent percentile ranks calculated from the combined data set, with darker shades indicating lower percentile ranks and lighter shades indicating higher ranks. The QC metrics include the number of acquired MS/MS spectra, MS/MS identifications, peptide identifications, protein identifications, summed total ion current, and number of missed cleavages, among others. The metrics discussed in the text are highlighted in green. See the Jupyter Notebook on our GitHub repository (https://github.com/MS-Quality-Hub/mzqclib-manuscript/) for data analysis and code to generate the plot.

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  • References

    This article references 39 other publications.

    1. 1
      Bittremieux, W.; Tabb, D. L.; Impens, F.; Staes, A.; Timmerman, E.; Martens, L.; Laukens, K. Quality Control in Mass Spectrometry-Based Proteomics. Mass Spectrom. Rev. 2018, 37 (5), 697– 711,  DOI: 10.1002/mas.21544
      1
      Quality control in mass spectrometry-based proteomics
      Bittremieux, Wout; Tabb, David L.; Impens, Francis; Staes, An; Timmerman, Evy; Martens, Lennart; Laukens, Kris
      Mass Spectrometry Reviews (2018), 37 (5), 697-711CODEN: MSRVD3; ISSN:0277-7037. (John Wiley & Sons, Inc.)
      A review. Mass spectrometry is a highly complex anal. technique and mass spectrometry-based proteomics expts. can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry expt. consists of multiple different phases including the sample prepn., liq. chromatog., mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry expt. occurring in all of these steps, and we discuss how to monitor and remedy the neg. influences on the exptl. results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the exptl. workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.
    2. 2
      Baker, M. 1,500 Scientists Lift the Lid on Reproducibility. Nature 2016, 533 (7604), 452– 454,  DOI: 10.1038/533452a
      2
      1,500 scientists lift the lid on reproducibility
      Baker, Monya
      Nature (London, United Kingdom) (2016), 533 (7604), 452-454CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Survey sheds light on the 'crisis' rocking research.
    3. 3
      Rodriguez, H.; Snyder, M.; Uhlén, M.; Andrews, P.; Beavis, R.; Borchers, C.; Chalkley, R. J.; Cho, S. Y.; Cottingham, K.; Dunn, M.; Dylag, T.; Edgar, R.; Hare, P.; Heck, A. J. R.; Hirsch, R. F.; Kennedy, K.; Kolar, P.; Kraus, H.-J.; Mallick, P.; Nesvizhskii, A.; Ping, P.; Pontén, F.; Yang, L.; Yates, J. R.; Stein, S. E.; Hermjakob, H.; Kinsinger, C. R.; Apweiler, R. Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: The Amsterdam Principles. J. Proteome Res. 2009, 8 (7), 3689– 3692,  DOI: 10.1021/pr900023z
      3
      Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: The Amsterdam Principles
      Rodriguez, Henry; Snyder, Mike; Uhlen, Mathias; Andrews, Phil; Beavis, Ronald; Borchers, Christoph; Chalkley, Robert J.; Cho, Sang Yun; Cottingham, Katie; Dunn, Michael; Dylag, Tomasz; Edgar, Ron; Hare, Peter; Heck, Albert J. R.; Hirsch, Roland F.; Kennedy, Karen; Kolar, Patrik; Kraus, Hans-Joachim; Mallick, Parag; Nesvizhskii, Alexey; Ping, Peipei; Ponten, Fredrik; Yang, Liming; Yates, John R.; Stein, Stephen E.; Hermjakob, Henning; Kinsinger, Christopher R.; Apweiler, Rolf
      Journal of Proteome Research (2009), 8 (7), 3689-3692CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      Policies supporting the rapid and open sharing of genomic data have directly fueled the accelerated pace of discovery in large-scale genomics research. The proteomics community is starting to implement analogous policies and infrastructure for making large-scale proteomics data widely available on a precompetitive basis. On August 14, 2008, the National Cancer Institute (NCI) convened the "International Summit on Proteomics Data Release and Sharing Policy" in Amsterdam, The Netherlands, to identify and address potential roadblocks to rapid and open access to data. The six principles agreed upon by key stakeholders at the summit addressed issues surrounding (1) timing, (2) comprehensiveness, (3) format, (4) deposition to repositories, (5) quality metrics, and (6) responsibility for proteomics data release. This summit report explores various approaches to develop a framework of data release and sharing principles that will most effectively fulfill the needs of the funding agencies and the research community.
    4. 4
      Kinsinger, C. R.; Apffel, J.; Baker, M.; Bian, X.; Borchers, C. H.; Bradshaw, R.; Brusniak, M.-Y.; Chan, D. W.; Deutsch, E. W.; Domon, B.; Gorman, J.; Grimm, R.; Hancock, W.; Hermjakob, H.; Horn, D.; Hunter, C.; Kolar, P.; Kraus, H.-J.; Langen, H.; Linding, R.; Moritz, R. L.; Omenn, G. S.; Orlando, R.; Pandey, A.; Ping, P.; Rahbar, A.; Rivers, R.; Seymour, S. L.; Simpson, R. J.; Slotta, D.; Smith, R. D.; Stein, S. E.; Tabb, D. L.; Tagle, D.; Yates, J. R. I.; Rodriguez, H. Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles). Mol. Cell. Proteomics 2011, 10 (12), O111.015446,  DOI: 10.1074/mcp.O111.015446
      There is no corresponding record for this reference.
    5. 5
      Rudnick, P. A.; Clauser, K. R.; Kilpatrick, L. E.; Tchekhovskoi, D. V.; Neta, P.; Blonder, N.; Billheimer, D. D.; Blackman, R. K.; Bunk, D. M.; Cardasis, H. L.; Ham, A.-J. L.; Jaffe, J. D.; Kinsinger, C. R.; Mesri, M.; Neubert, T. A.; Schilling, B.; Tabb, D. L.; Tegeler, T. J.; Vega-Montoto, L.; Variyath, A. M.; Wang, M.; Wang, P.; Whiteaker, J. R.; Zimmerman, L. J.; Carr, S. A.; Fisher, S. J.; Gibson, B. W.; Paulovich, A. G.; Regnier, F. E.; Rodriguez, H.; Spiegelman, C.; Tempst, P.; Liebler, D. C.; Stein, S. E. Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses. Mol. Cell. Proteomics 2010, 9 (2), 225– 241,  DOI: 10.1074/mcp.M900223-MCP200
      5
      Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses
      Rudnick, Paul A.; Clauser, Karl R.; Kilpatrick, Lisa E.; Tchekhovskoi, Dmitrii V.; Neta, Pedatsur; Blonder, Niksa; Billheimer, Dean D.; Blackman, Ronald K.; Bunk, David M.; Cardasis, Helene L.; Ham, Amy-Joan L.; Jaffe, Jacob D.; Kinsinger, Christopher R.; Mesri, Mehdi; Neubert, Thomas A.; Schilling, Birgit; Tabb, David L.; Tegeler, Tony J.; Vega-Montoto, Lorenzo; Variyath, Asokan Mulayath; Wang, Mu; Wang, Pei; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Carr, Steven A.; Fisher, Susan J.; Gibson, Bradford W.; Paulovich, Amanda G.; Regnier, Fred E.; Rodriguez, Henry; Spiegelman, Cliff; Tempst, Paul; Liebler, Daniel C.; Stein, Stephen E.
      Molecular and Cellular Proteomics (2010), 9 (2), 225-241CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
      A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quant. assessment of system performance and evaluation of tech. variability. Here we describe 46 system performance metrics for monitoring chromatog. performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlab. studies conducted under a common std. operating procedure identified outlier data and provided clues to specific causes. Moreover, interlab. variation reflected by the metrics indicates which system components vary the most between labs. Application of these metrics enables rational, quant. quality assessment for proteomics and other LC-MS/MS anal. applications.
    6. 6
      Ma, Z.-Q.; Polzin, K. O.; Dasari, S.; Chambers, M. C.; Schilling, B.; Gibson, B. W.; Tran, B. Q.; Vega-Montoto, L.; Liebler, D. C.; Tabb, D. L. QuaMeter: Multivendor Performance Metrics for LC-MS/MS Proteomics Instrumentation. Anal. Chem. 2012, 84 (14), 5845– 5850,  DOI: 10.1021/ac300629p
      6
      QuaMeter: Multivendor Performance Metrics for LC-MS/MS Proteomics Instrumentation
      Ma, Ze-Qiang; Polzin, Kenneth O.; Dasari, Surendra; Chambers, Matthew C.; Schilling, Birgit; Gibson, Bradford W.; Tran, Bao Q.; Vega-Montoto, Lorenzo; Liebler, Daniel C.; Tabb, David L.
      Analytical Chemistry (Washington, DC, United States) (2012), 84 (14), 5845-5850CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
      LC-MS/MS-based proteomics studies rely on stable anal. system performance that can be evaluated by objective criteria. The National Institute of Stds. and Technol. (NIST) introduced the MSQC software to compute diverse metrics from exptl. LC-MS/MS data, enabling quality anal. and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufd. by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.
    7. 7
      Pichler, P.; Mazanek, M.; Dusberger, F.; Weilnböck, L.; Huber, C. G.; Stingl, C.; Luider, T. M.; Straube, W. L.; Köcher, T.; Mechtler, K. SIMPATIQCO: A Server-Based Software Suite Which Facilitates Monitoring the Time Course of LC-MS Performance Metrics on Orbitrap Instruments. J. Proteome Res. 2012, 11 (11), 5540– 5547,  DOI: 10.1021/pr300163u
      7
      SIMPATIQCO: A Server-Based Software Suite Which Facilitates Monitoring the Time Course of LC-MS Performance Metrics on Orbitrap Instruments
      Pichler, Peter; Mazanek, Michael; Dusberger, Frederico; Weilnboeck, Lisa; Huber, Christian G.; Stingl, Christoph; Luider, Theo M.; Straube, Werner L.; Koecher, Thomas; Mechtler, Karl
      Journal of Proteome Research (2012), 11 (11), 5540-5547CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      While the performance of liq. chromatog. (LC) and mass spectrometry (MS) instrumentation continues to increase, applications such as analyses of complete or near-complete proteomes and quant. studies require const. and optimal system performance. For this reason, research labs. and core facilities alike are recommended to implement quality control (QC) measures as part of their routine workflows. Many labs. perform sporadic quality control checks. However, successive and systematic longitudinal monitoring of system performance would be facilitated by dedicated automatic or semiautomatic software solns. that aid an effortless anal. and display of QC metrics over time. We present the software package SIMPATIQCO (SIMPle AuTomatIc Quality COntrol) designed for evaluation of data from LTQ Orbitrap, Q-Exactive, LTQ FT, and LTQ instruments. A centralized SIMPATIQCO server can process QC data from multiple instruments. The software calcs. QC metrics supervising every step of data acquisition from LC and electrospray to MS. For each QC metric the software learns the range indicating adequate system performance from the uploaded data using robust statistics. Results are stored in a database and can be displayed in a comfortable manner from any computer in the lab. via a web browser. QC data can be monitored for individual LC runs as well as plotted over time. SIMPATIQCO thus assists the longitudinal monitoring of important QC metrics such as peptide elution times, peak widths, intensities, total ion current (TIC) as well as sensitivity, and overall LC-MS system performance; in this way the software also helps identify potential problems. The SIMPATIQCO software package is available free of charge.
    8. 8
      Bielow, C.; Mastrobuoni, G.; Kempa, S. Proteomics Quality Control: Quality Control Software for MaxQuant Results. J. Proteome Res. 2016, 15 (3), 777– 787,  DOI: 10.1021/acs.jproteome.5b00780
      8
      Proteomics Quality Control: Quality Control Software for MaxQuant Results
      Bielow, Chris; Mastrobuoni, Guido; Kempa, Stefan
      Journal of Proteome Research (2016), 15 (3), 777-787CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      Mass spectrometry-based proteomics coupled to liq. chromatog. has matured into an automatized, high-throughput technol., producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality anal. (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream anal. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report contg. a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of exptl. designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant's Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC.
    9. 9
      Chiva, C.; Olivella, R.; Borràs, E.; Espadas, G.; Pastor, O.; Solé, A.; Sabidó, E. QCloud: A Cloud-Based Quality Control System for Mass Spectrometry-Based Proteomics Laboratories. PLOS ONE 2018, 13 (1), e0189209  DOI: 10.1371/journal.pone.0189209
      9
      QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories
      Chiva, Cristina; Olivella, Roger; Borras, Eva; Espadas, Guadalupe; Pastor, Olga; Sole, Amanda; Sabido, Eduard
      PLoS One (2018), 13 (1), e0189209/1-e0189209/14CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)
      The increasing no. of biomedical and translational applications in mass spectrometrybased proteomics poses new anal. challenges and raises the need for automated quality control systems. Despite previous efforts to set std. file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics labs., which limits the acquisition of high-quality results, inter-lab. comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics labs. in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community. QCloud system is an open source project, publicly available under a Creative Commons License Attribution- ShareAlike 4.0.
    10. 10
      Broadhurst, D.; Goodacre, R.; Reinke, S. N.; Kuligowski, J.; Wilson, I. D.; Lewis, M. R.; Dunn, W. B. Guidelines and Considerations for the Use of System Suitability and Quality Control Samples in Mass Spectrometry Assays Applied in Untargeted Clinical Metabolomic Studies. Metabolomics 2018, 14 (6), 72,  DOI: 10.1007/s11306-018-1367-3
      10
      Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies
      Broadhurst David; Reinke Stacey N; Goodacre Royston; Reinke Stacey N; Kuligowski Julia; Wilson Ian D; Lewis Matthew R; Dunn Warwick B; Dunn Warwick B; Dunn Warwick B
      Metabolomics : Official journal of the Metabolomic Society (2018), 14 (6), 72 ISSN:1573-3882.
      BACKGROUND: Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition. AIM OF REVIEW: This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported. KEY SCIENTIFIC CONCEPTS OF REVIEW: System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.
    11. 11
      Stratton, K. G.; Webb-Robertson, B.-J. M.; McCue, L. A.; Stanfill, B.; Claborne, D.; Godinez, I.; Johansen, T.; Thompson, A. M.; Burnum-Johnson, K. E.; Waters, K. M.; Bramer, L. M. pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data. J. Proteome Res. 2019, 18 (3), 1418– 1425,  DOI: 10.1021/acs.jproteome.8b00760
      11
      pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data
      Stratton, Kelly G.; Webb-Robertson, Bobbie-Jo M.; McCue, Lee Ann; Stanfill, Bryan; Claborne, Daniel; Godinez, Iobani; Johansen, Thomas; Thompson, Allison M.; Burnum-Johnson, Kristin E.; Waters, Katrina M.; Bramer, Lisa M.
      Journal of Proteome Research (2019), 18 (3), 1418-1425CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      Prior to statistical anal. of mass spectrometry (MS) data, quality control (QC) of the identified biomol. peak intensities is imperative for reducing process-based sources of variation and extreme biol. outliers. Without this step, statistical results can be biased. Addnl., liq. chromatog.-MS proteomics data present inherent challenges due to large amts. of missing data that require special consideration during statistical anal. While a no. of R packages exist to address these challenges individually, there is no single R package that addresses all of them. The authors present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data anal. (EDA), visualization, and statistical anal. robust to missing data. Example anal. using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quant. and qual. statistical test, 19 proteins whose statistical significance would have been missed by a quant. test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.
    12. 12
      Naake, T.; Rainer, J.; Huber, W. MsQuality: An Interoperable Open-Source Package for the Calculation of Standardized Quality Metrics of Mass Spectrometry Data. Bioinformatics 2023, 39 (10), btad618,  DOI: 10.1093/bioinformatics/btad618
      There is no corresponding record for this reference.
    13. 13
      Kirwan, J. A.; Gika, H.; Beger, R. D.; Bearden, D.; Dunn, W. B.; Goodacre, R.; Theodoridis, G.; Witting, M.; Yu, L.-R.; Wilson, I. D. the metabolomics Quality Assurance and Quality Control Consortium (mQACC). Quality Assurance and Quality Control Reporting in Untargeted Metabolic Phenotyping: mQACC Recommendations for Analytical Quality Management. Metabolomics 2022, 18 (9), 70,  DOI: 10.1007/s11306-022-01926-3
      13
      Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management
      Kirwan, Jennifer A.; Gika, Helen; Beger, Richard D.; Bearden, Dan; Dunn, Warwick B.; Goodacre, Royston; Theodoridis, Georgios; Witting, Michael; Yu, Li-Rong; Wilson, Ian D.; the metabolomics Quality Assurance and Quality Control Consortium
      Metabolomics (2022), 18 (9), 70CODEN: METAHQ; ISSN:1573-3890. (Springer)
      Abstr.: Background: Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanal. of the body of work. Objective: The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and NMR spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to anal. best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as online resources. Key reporting practices: Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, prepn. and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrixes and sample prepn., the use of test mixts. and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.
    14. 14
      Köfeler, H. C.; Ahrends, R.; Baker, E. S.; Ekroos, K.; Han, X.; Hoffmann, N.; Holčapek, M.; Wenk, M. R.; Liebisch, G. Recommendations for Good Practice in MS-Based Lipidomics. J. Lipid Res. 2021, 62, 100138,  DOI: 10.1016/j.jlr.2021.100138
      14
      Recommendations for good practice in MS-based lipidomics
      Kofeler Harald C; Ahrends Robert; Baker Erin S; Ekroos Kim; Han Xianlin; Hoffmann Nils; Holcapek Michal; Wenk Markus R; Liebisch Gerhard
      Journal of lipid research (2021), 62 (), 100138 ISSN:.
      In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field.
    15. 15
      McDonald, J. G.; Ejsing, C. S.; Kopczynski, D.; Holčapek, M.; Aoki, J.; Arita, M.; Arita, M.; Baker, E. S.; Bertrand-Michel, J.; Bowden, J. A.; Brügger, B.; Ellis, S. R.; Fedorova, M.; Griffiths, W. J.; Han, X.; Hartler, J.; Hoffmann, N.; Koelmel, J. P.; Köfeler, H. C.; Mitchell, T. W.; O’Donnell, V. B.; Saigusa, D.; Schwudke, D.; Shevchenko, A.; Ulmer, C. Z.; Wenk, M. R.; Witting, M.; Wolrab, D.; Xia, Y.; Ahrends, R.; Liebisch, G.; Ekroos, K. Introducing the Lipidomics Minimal Reporting Checklist. Nat. Metab. 2022, 4 (9), 1086– 1088,  DOI: 10.1038/s42255-022-00628-3
      15
      Introducing the Lipidomics Minimal Reporting Checklist
      McDonald Jeffrey G; Ejsing Christer S; Ejsing Christer S; Kopczynski Dominik; Ahrends Robert; Holcapek Michal; Wolrab Denise; Aoki Junken; Aoki Junken; Arita Makoto; Arita Masanori; Baker Erin S; Bertrand-Michel Justine; Bowden John A; Brugger Britta; Ellis Shane R; Ellis Shane R; Mitchell Todd W; Fedorova Maria; Griffiths William J; Han Xianlin; Han Xianlin; Hartler Jurgen; Hartler Jurgen; Hoffmann Nils; Koelmel Jeremy P; Kofeler Harald C; O'Donnell Valerie B; Saigusa Daisuke; Schwudke Dominik; Schwudke Dominik; Schwudke Dominik; Shevchenko Andrej; Ulmer Candice Z; Wenk Markus R; Witting Michael; Xia Yu; Liebisch Gerhard; Ekroos Kim
      Nature metabolism (2022), 4 (9), 1086-1088 ISSN:.
      There is no expanded citation for this reference.
    16. 16
      Bittremieux, W.; Valkenborg, D.; Martens, L.; Laukens, K. Computational Quality Control Tools for Mass Spectrometry Proteomics. PROTEOMICS 2017, 17 (3–4), 1600159,  DOI: 10.1002/pmic.201600159
      There is no corresponding record for this reference.
    17. 17
      Bittremieux, W.; Walzer, M.; Tenzer, S.; Zhu, W.; Salek, R. M.; Eisenacher, M.; Tabb, D. L. The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry. Anal. Chem. 2017, 89 (8), 4474– 4479,  DOI: 10.1021/acs.analchem.6b04310
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      The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry
      Bittremieux, Wout; Walzer, Mathias; Tenzer, Stefan; Zhu, Weimin; Salek, Reza M.; Eisenacher, Martin; Tabb, David L.
      Analytical Chemistry (Washington, DC, United States) (2017), 89 (8), 4474-4479CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
      To have confidence in results acquired during biol. mass spectrometry expts., a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Stds. Initiative established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic tech. necessities required to support extensive quality control for any type of mass spectrometry-based workflow. The authors want to emphasize that this is an open community effort, and the authors seek participation from all scientists with an interest in this field.
    18. 18
      Deutsch, E. W.; Vizcaíno, J. A.; Jones, A. R.; Binz, P.-A.; Lam, H.; Klein, J.; Bittremieux, W.; Perez-Riverol, Y.; Tabb, D. L.; Walzer, M.; Ricard-Blum, S.; Hermjakob, H.; Neumann, S.; Mak, T. D.; Kawano, S.; Mendoza, L.; Van Den Bossche, T.; Gabriels, R.; Bandeira, N.; Carver, J.; Pullman, B.; Sun, Z.; Hoffmann, N.; Shofstahl, J.; Zhu, Y.; Licata, L.; Quaglia, F.; Tosatto, S. C. E.; Orchard, S. E. Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work. J. Proteome Res. 2023, 22 (2), 287– 301,  DOI: 10.1021/acs.jproteome.2c00637
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      Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work
      Deutsch, Eric W.; Vizcaino, Juan Antonio; Jones, Andrew R.; Binz, Pierre-Alain; Lam, Henry; Klein, Joshua; Bittremieux, Wout; Perez-Riverol, Yasset; Tabb, David L.; Walzer, Mathias; Ricard-Blum, Sylvie; Hermjakob, Henning; Neumann, Steffen; Mak, Tytus D.; Kawano, Shin; Mendoza, Luis; Van Den Bossche, Tim; Gabriels, Ralf; Bandeira, Nuno; Carver, Jeremy; Pullman, Benjamin; Sun, Zhi; Hoffmann, Nils; Shofstahl, Jim; Zhu, Yunping; Licata, Luana; Quaglia, Federica; Tosatto, Silvio C. E.; Orchard, Sandra E.
      Journal of Proteome Research (2023), 22 (2), 287-301CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      A review. The Human Proteome Organization (HUPO) Proteomics Stds. 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 stds. We briefly describe the current state of the many existing PSI stds., 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 stds. 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.
    19. 19
      Mayer, G.; Montecchi-Palazzi, L.; Ovelleiro, D.; Jones, A. R.; Binz, P.-A.; Deutsch, E. W.; Chambers, M.; Kallhardt, M.; Levander, F.; Shofstahl, J.; Orchard, S.; Vizcaino, J. A.; Hermjakob, H.; Stephan, C.; Meyer, H. E.; Eisenacher, M. The HUPO Proteomics Standards Initiative- Mass Spectrometry Controlled Vocabulary. Database 2013, 2013, bat009,  DOI: 10.1093/database/bat009
      There is no corresponding record for this reference.
    20. 20
      Röst, H. L.; Schmitt, U.; Aebersold, R.; Malmström, L. pyOpenMS: A Python-Based Interface to the OpenMS Mass-Spectrometry Algorithm Library. PROTEOMICS 2014, 14 (1), 74– 77,  DOI: 10.1002/pmic.201300246
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      pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library
      Rost Hannes L; Schmitt Uwe; Aebersold Ruedi; Malmstrom Lars
      Proteomics (2014), 14 (1), 74-7 ISSN:.
      pyOpenMS is an open-source, Python-based interface to the C++ OpenMS library, providing facile access to a feature-rich, open-source algorithm library for MS-based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping, and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open-source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3-clause BSD licence).
    21. 21
      Levitsky, L. I.; Klein, J. A.; Ivanov, M. V.; Gorshkov, M. Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework. J. Proteome Res. 2019, 18 (2), 709– 714,  DOI: 10.1021/acs.jproteome.8b00717
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      Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework
      Levitsky, Lev I.; Klein, Joshua A.; Ivanov, Mark V.; Gorshkov, Mikhail V.
      Journal of Proteome Research (2019), 18 (2), 709-714CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      A review. Many of the novel ideas that drive today's proteomic technologies are focused essentially on exptl. or data-processing workflows. The latter are implemented and published in a no. of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic labs., 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.
    22. 22
      Huber, F.; Verhoeven, S.; Meijer, C.; Spreeuw, H.; Castilla, E.; Geng, C.; van der Hooft, J.; Rogers, S.; Belloum, A.; Diblen, F.; Spaaks, J. Matchms - Processing and Similarity Evaluation of Mass Spectrometry Data. J. Open Source Softw. 2020, 5 (52), 2411,  DOI: 10.21105/joss.02411
      There is no corresponding record for this reference.
    23. 23
      Bittremieux, W.; Levitsky, L.; Pilz, M.; Sachsenberg, T.; Huber, F.; Wang, M.; Dorrestein, P. C. Unified and Standardized Mass Spectrometry Data Processing in Python Using Spectrum_utils. J. Proteome Res. 2023, 22 (2), 625– 631,  DOI: 10.1021/acs.jproteome.2c00632
      There is no corresponding record for this reference.
    24. 24
      Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Anal. Chem. 2006, 78 (3), 779– 787,  DOI: 10.1021/ac051437y
      24
      XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification
      Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace; Abagyan, Ruben; Siuzdak, Gary
      Analytical Chemistry (2006), 78 (3), 779-787CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
      Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity detn., and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biol. origin. Here we introduce an LC/MS-based data anal. approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal stds., the method dynamically identifies hundreds of endogenous metabolites for use as stds., calcg. a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
    25. 25
      Gatto, L.; Gibb, S.; Rainer, J. MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data. J. Proteome Res. 2021, 20 (1), 1063– 1069,  DOI: 10.1021/acs.jproteome.0c00313
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      MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data
      Gatto, Laurent; Gibb, Sebastian; Rainer, Johannes
      Journal of Proteome Research (2021), 20 (1), 1063-1069CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      We present version 2 of the MSnbase R/Bioconductor package. MSnbase provides infrastructure for the manipulation, processing, and visualization of mass spectrometry data. We focus on the new on-disk infrastructure, that allows the handling of large raw mass spectrometry expts. on commodity hardware and illustrate how the package is used for elegant data processing, method development, and visualization.
    26. 26
      Barsnes, H.; Vaudel, M.; Colaert, N.; Helsens, K.; Sickmann, A.; Berven, F. S.; Martens, L. Compomics-Utilities: An Open-Source Java Library for Computational Proteomics. BMC Bioinformatics 2011, 12 (1), 70,  DOI: 10.1186/1471-2105-12-70
      There is no corresponding record for this reference.
    27. 27
      Schmid, R.; Heuckeroth, S.; Korf, A.; Smirnov, A.; Myers, O.; Dyrlund, T. S.; Bushuiev, R.; Murray, K. J.; Hoffmann, N.; Lu, M.; Sarvepalli, A.; Zhang, Z.; Fleischauer, M.; Dührkop, K.; Wesner, M.; Hoogstra, S. J.; Rudt, E.; Mokshyna, O.; Brungs, C.; Ponomarov, K.; Mutabdžija, L.; Damiani, T.; Pudney, C. J.; Earll, M.; Helmer, P. O.; Fallon, T. R.; Schulze, T.; Rivas-Ubach, A.; Bilbao, A.; Richter, H.; Nothias, L.-F.; Wang, M.; Orešič, M.; Weng, J.-K.; Böcker, S.; Jeibmann, A.; Hayen, H.; Karst, U.; Dorrestein, P. C.; Petras, D.; Du, X.; Pluskal, T. Integrative Analysis of Multimodal Mass Spectrometry Data in MZmine 3. Nat. Biotechnol. 2023, 41 (4), 447– 449,  DOI: 10.1038/s41587-023-01690-2
      27
      Integrative analysis of multimodal mass spectrometry data in MZmine 3
      Schmid, Robin; Heuckeroth, Steffen; Korf, Ansgar; Smirnov, Aleksandr; Myers, Owen; Dyrlund, Thomas S.; Bushuiev, Roman; Murray, Kevin J.; Hoffmann, Nils; Lu, Miaoshan; Sarvepalli, Abinesh; Zhang, Zheng; Fleischauer, Markus; Duhrkop, Kai; Wesner, Mark; Hoogstra, Shawn J.; Rudt, Edward; Mokshyna, Olena; Brungs, Corinna; Ponomarov, Kirill; Mutabdzija, Lana; Damiani, Tito; Pudney, Chris J.; Earll, Mark; Helmer, Patrick O.; Fallon, Timothy R.; Schulze, Tobias; Rivas-Ubach, Albert; Bilbao, Aivett; Richter, Henning; Nothias, Louis-Felix; Wang, Mingxun; Oresic, Matej; Weng, Jing-Ke; Bocker, Sebastian; Jeibmann, Astrid; Hayen, Heiko; Karst, Uwe; Dorrestein, Pieter C.; Petras, Daniel; Du, Xiuxia; Pluskal, Tomas
      Nature Biotechnology (2023), 41 (4), 447-449CODEN: NABIF9; ISSN:1087-0156. (Nature Portfolio)
      There is no expanded citation for this reference.
    28. 28
      Martens, L.; Chambers, M.; Sturm, M.; Kessner, D.; Levander, F.; Shofstahl, J.; Tang, W. H.; Römpp, A.; Neumann, S.; Pizarro, A. D.; Montecchi-Palazzi, L.; Tasman, N.; Coleman, M.; Reisinger, F.; Souda, P.; Hermjakob, H.; Binz, P.-A.; Deutsch, E. W. mzML─a Community Standard for Mass Spectrometry Data. Mol. Cell. Proteomics 2011, 10 (1), R110.000133,  DOI: 10.1074/mcp.R110.000133
      There is no corresponding record for this reference.
    29. 29
      Griss, J.; Jones, A. R.; Sachsenberg, T.; Walzer, M.; Gatto, L.; Hartler, J.; Thallinger, G. G.; Salek, R. M.; Steinbeck, C.; Neuhauser, N.; Cox, J.; Neumann, S.; Fan, J.; Reisinger, F.; Xu, Q.-W.; del Toro, N.; Perez-Riverol, Y.; Ghali, F.; Bandeira, N.; Xenarios, I.; Kohlbacher, O.; Vizcaíno, J. A.; Hermjakob, H. The mzTab Data Exchange Format: Communicating Mass-Spectrometry-Based Proteomics and Metabolomics Experimental Results to a Wider Audience. Mol. Cell. Proteomics 2014, 13 (10), 2765– 2775,  DOI: 10.1074/mcp.O113.036681
      29
      The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience
      Griss, Johannes; Jones, Andrew R.; Sachsenberg, Timo; Walzer, Mathias; Gatto, Laurent; Hartler, Jurgen; Thallinger, Gerhard G.; Salek, Reza M.; Steinbeck, Christoph; Neuhauser, Nadin; Cox, Jurgen; Neumann, Steffen; Fan, Jun; Reisinger, Florian; Xu, Qing-Wei; del Toro, Noemi; Perez-Riverol, Yasset; Ghali, Fawaz; Bandeira, Nuno; Xenarios, Ioannis; Kohlbacher, Oliver; Vizcaino, Juan Antonio; Hermjakob, Henning
      Molecular & Cellular Proteomics (2014), 13 (10), 2765-2775CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
      We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. MzTab is intended as a lightwt. supplement to the existing std. XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. MzTab files can contain protein, peptide, and small mol. identifications together with exptl. metadata and basic quant. information. The format is not intended to store the complete exptl. evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the exptl. design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biol. community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive addnl. documentation can be found online.
    30. 30
      Deutsch, E. W.; Bandeira, N.; Sharma, V.; Perez-Riverol, Y.; Carver, J. J.; Kundu, D. J.; García-Seisdedos, D.; Jarnuczak, A. F.; Hewapathirana, S.; Pullman, B. S.; Wertz, J.; Sun, Z.; Kawano, S.; Okuda, S.; Watanabe, Y.; Hermjakob, H.; MacLean, B.; MacCoss, M. J.; Zhu, Y.; Ishihama, Y.; Vizcaíno, J. A. The ProteomeXchange Consortium in 2020: Enabling “big Data” Approaches in Proteomics. Nucleic Acids Res. 2019, 48 (D1), D1145– D1152,  DOI: 10.1093/nar/gkz984
      There is no corresponding record for this reference.
    31. 31
      Marshall, S. A.; Young, R. B.; Lewis, J. M.; Rutten, E. L.; Gould, J.; Barlow, C. K.; Giogha, C.; Marcelino, V. R.; Fields, N.; Schittenhelm, R. B.; Hartland, E. L.; Scott, N. E.; Forster, S. C.; Gulliver, E. L. The Broccoli-Derived Antioxidant Sulforaphane Changes the Growth of Gastrointestinal Microbiota, Allowing for the Production of Anti-Inflammatory Metabolites. J. Funct. Foods 2023, 107, 105645,  DOI: 10.1016/j.jff.2023.105645
      There is no corresponding record for this reference.
    32. 32
      Hulstaert, N.; Shofstahl, J.; Sachsenberg, T.; Walzer, M.; Barsnes, H.; Martens, L.; Perez-Riverol, Y. ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. J. Proteome Res. 2020, 19 (1), 537– 542,  DOI: 10.1021/acs.jproteome.9b00328
      32
      ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion
      Hulstaert, Niels; Shofstahl, Jim; Sachsenberg, Timo; Walzer, Mathias; Barsnes, Harald; Martens, Lennart; Perez-Riverol, Yasset
      Journal of Proteome Research (2020), 19 (1), 537-542CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the no. of samples analyzed per expt. as well as by the growing amt. of data obtained in each anal. run. In order to process these large amts. of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI std. file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addn., we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quant. results.
    33. 33
      Park, C. Y.; Klammer, A. A.; Käll, L.; MacCoss, M. J.; Noble, W. S. Rapid and Accurate Peptide Identification from Tandem Mass Spectra. J. Proteome Res. 2008, 7 (7), 3022– 3027,  DOI: 10.1021/pr800127y
      33
      Rapid and Accurate Peptide Identification from Tandem Mass Spectra
      Park, Christopher Y.; Klammer, Aaron A.; Kall, Lukas; MacCoss, Michael J.; Noble, William S.
      Journal of Proteome Research (2008), 7 (7), 3022-3027CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
      Mass spectrometry, the core technol. in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biol. sample. Currently, the primary bottleneck in this type of expt. is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program SEQUEST. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate ests. Crux also implements two recently described postprocessing methods: a p value calcn. based upon fitting a Weibull distribution to the obsd. scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.
    34. 34
      The UniProt Consortium UniProt: A Hub for Protein Information. Nucleic Acids Res. 2015, 43 (D1), D204– D212,  DOI: 10.1093/nar/gku989 .
      There is no corresponding record for this reference.
    35. 35
      Lin, A.; See, D.; Fondrie, W. E.; Keich, U.; Noble, W. S. Target-Decoy False Discovery Rate Estimation Using Crema. PROTEOMICS 2024, 24 (8), 2300084,  DOI: 10.1002/pmic.202300084
      There is no corresponding record for this reference.
    36. 36
      Côté, R. G.; Reisinger, F.; Martens, L. jmzML, an Open-Source Java API for mzML, the PSI Standard for MS Data. PROTEOMICS 2010, 10 (7), 1332– 1335,  DOI: 10.1002/pmic.200900719
      There is no corresponding record for this reference.
    37. 37
      Pluskal, T.; Hoffmann, N.; Du, X.; Weng, J.-K. Mass Spectrometry Development Kit (MSDK): A Java Library for Mass Spectrometry Data Processing. In New Developments in Mass Spectrometry; Winkler, R., Ed.; Royal Society of Chemistry: Cambridge, 2020; pp 399– 405,  DOI: 10.1039/9781788019880-00399 .
      There is no corresponding record for this reference.
    38. 38
      McKinney, W. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference; van der Walt, S.; Millman, J., Eds.; Austin, Texas, USA, 2010; pp 51– 56,  DOI: 10.25080/Majora-92bf1922-00a .
      There is no corresponding record for this reference.
    39. 39
      Thomas, K.; Benjamin, R.-K.; Fernando, P.; Brian, G.; Matthias, B.; Jonathan, F.; Kyle, K.; Jessica, H.; Jason, G.; Sylvain, C.; Paul, I.; Damián, A.; Safia, A.; Carol, W. Jupyter Development Team. Jupyter Notebooks - A Publishing Format for Reproducible Computational Workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas; IOS Press, 2016; pp 87– 90.
      There is no corresponding record for this reference.

  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jasms.4c00174.

    • Supplementary Table 1: List of QC metrics considered during the analysis and their accession numbers in the PSI-MS controlled vocabulary (PDF)


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