1
|
Luo X, Bittremieux W, Griss J, Deutsch EW, Sachsenberg T, Levitsky LI, Ivanov MV, Bubis JA, Gabriels R, Webel H, Sanchez A, Bai M, Käll L, Perez-Riverol Y. A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics. J Proteome Res 2022; 21:1566-1574. [PMID: 35549218 PMCID: PMC9171829 DOI: 10.1021/acs.jproteome.2c00069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Spectrum clustering
is a powerful strategy to minimize redundant
mass spectra by grouping them based on similarity, with the aim of
forming groups of mass spectra from the same repeatedly measured analytes.
Each such group of near-identical spectra can be represented by its
so-called consensus spectrum for downstream processing. Although several
algorithms for spectrum clustering have been adequately benchmarked
and tested, the influence of the consensus spectrum generation step
is rarely evaluated. Here, we present an implementation and benchmark
of common consensus spectrum algorithms, including spectrum averaging,
spectrum binning, the most similar spectrum, and the best-identified
spectrum. We have analyzed diverse public data sets using two different
clustering algorithms (spectra-cluster and MaRaCluster) to evaluate
how the consensus spectrum generation procedure influences downstream
peptide identification. The BEST and BIN methods were found the most
reliable methods for consensus spectrum generation, including for
data sets with post-translational modifications (PTM) such as phosphorylation.
All source code and data of the present study are freely available
on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.
Collapse
Affiliation(s)
- Xiyang Luo
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, 400065 Chongqing, China
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Johannes Griss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, U.K.,Department of Dermatology, Medical University of Vienna, 1090 Vienna, Austria
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Timo Sachsenberg
- Applied Bioinformatics, Department for Computer Science, University of Tuebingen, Sand 14, 72076 Tuebingen, Germany
| | - Lev I Levitsky
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow 142432, Russia
| | - Mark V Ivanov
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow 142432, Russia
| | - Julia A Bubis
- V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow 142432, Russia
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, B-9052 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, B-9000 Ghent, Belgium
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Aniel Sanchez
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 20502 Malmö, Sweden
| | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, 400065 Chongqing, China
| | - Lukas Käll
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology - KTH, Box 1031, 17121 Solna, Sweden
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, U.K
| |
Collapse
|
2
|
A proteomics sample metadata representation for multiomics integration and big data analysis. Nat Commun 2021; 12:5854. [PMID: 34615866 PMCID: PMC8494749 DOI: 10.1038/s41467-021-26111-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/16/2021] [Indexed: 11/08/2022] Open
Abstract
The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.
Collapse
|
3
|
Schmidt T, Samaras P, Dorfer V, Panse C, Kockmann T, Bichmann L, van Puyvelde B, Perez-Riverol Y, Deutsch EW, Kuster B, Wilhelm M. Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison. J Proteome Res 2021; 20:3388-3394. [PMID: 33970638 DOI: 10.1021/acs.jproteome.1c00096] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Here, we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can be either provided manually by the user (table format) or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high-quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other web sites (https://github.com/kusterlab/universal_spectrum_explorer).
Collapse
Affiliation(s)
- Tobias Schmidt
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany
| | - Patroklos Samaras
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, 4232 Hagenberg im Mühlkreis, Austria
| | - Christian Panse
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology in Zurich/University of Zurich, 8092 Zürich, Switzerland.,SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Tobias Kockmann
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology in Zurich/University of Zurich, 8092 Zürich, Switzerland
| | - Leon Bichmann
- Applied Bioinformatics, Department of Computer Science, University Tübingen, 72074 Tübingen, Germany
| | - Bart van Puyvelde
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, B9000, Ghent, Denmark
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States of America
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany.,Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich (TUM), Freising 85354, Germany
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany.,Computational Mass Spectrometry, Technical University of Munich, Freising 85354, Germany
| |
Collapse
|
4
|
Bittremieux W, Bouyssié D, Dorfer V, Locard-Paulet M, Perez-Riverol Y, Schwämmle V, Uszkoreit J, Van Den Bossche T. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS): an open community for bioinformatics training and research. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2021:e9087. [PMID: 33861485 DOI: 10.1002/rcm.9087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/13/2021] [Accepted: 03/18/2021] [Indexed: 06/12/2023]
Abstract
The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS; eubic-ms.org) was founded in 2014 to unite European computational mass spectrometry researchers and proteomics bioinformaticians working in academia and industry. EuBIC-MS maintains educational resources (proteomics-academy.org) and organises workshops at national and international conferences on proteomics and mass spectrometry. Furthermore, EuBIC-MS is actively involved in several community initiatives such as the Human Proteome Organization's Proteomics Standards Initiative (HUPO-PSI). Apart from these collaborations, EuBIC-MS has organised two Winter Schools and two Developers' Meetings that have contributed to the strengthening of the European mass spectrometry network and fostered international collaboration in this field, even beyond Europe. Moreover, EuBIC-MS is currently actively developing a community-driven standard dedicated to mass spectrometry data annotation (SDRF-Proteomics) that will facilitate data reuse and collaboration. This manuscript highlights what EuBIC-MS is, what it does, and what it already has achieved. A warm invitation is extended to new researchers at all career stages to join the EuBIC-MS community on its Slack channel (eubic.slack.com).
Collapse
Affiliation(s)
- Wout Bittremieux
- European Bioinformatics Community for Mass Spectrometry, Belgium
- University of California San Diego, La Jolla, CA, USA
- University of Antwerp, Antwerp, Belgium
| | - David Bouyssié
- European Bioinformatics Community for Mass Spectrometry, Belgium
- IPBS, University of Toulouse, CNRS, UPS, Toulouse, France
| | - Viktoria Dorfer
- European Bioinformatics Community for Mass Spectrometry, Belgium
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg, Austria
| | - Marie Locard-Paulet
- European Bioinformatics Community for Mass Spectrometry, Belgium
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark
| | - Yasset Perez-Riverol
- European Bioinformatics Community for Mass Spectrometry, Belgium
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Veit Schwämmle
- European Bioinformatics Community for Mass Spectrometry, Belgium
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Julian Uszkoreit
- European Bioinformatics Community for Mass Spectrometry, Belgium
- Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr University Bochum, Bochum, Germany
- Medical Faculty, Medizinisches Proteom-Center, Ruhr University Bochum, Bochum, Germany
| | - Tim Van Den Bossche
- European Bioinformatics Community for Mass Spectrometry, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| |
Collapse
|
5
|
Verschaffelt P, Van Den Bossche T, Gabriel W, Burdukiewicz M, Soggiu A, Martens L, Renard BY, Schiebenhoefer H, Mesuere B. MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets. J Proteome Res 2021; 20:2083-2088. [PMID: 33661648 DOI: 10.1021/acs.jproteome.0c00926] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.
Collapse
Affiliation(s)
- Pieter Verschaffelt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent 9000, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Wassim Gabriel
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising 85354, Germany
| | - Michał Burdukiewicz
- Laboratory of Mass Spectrometry, Institute of Biochemistry and Biophysics Polish Academy of Sciences, Warsaw 02-106, Poland
| | - Alessio Soggiu
- "One Health" Section, Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan 20122, Italy
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Bernhard Y Renard
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, Potsdam 14482, Germany.,Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Henning Schiebenhoefer
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, Potsdam 14482, Germany.,Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Bart Mesuere
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent 9000, Belgium.,VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium
| |
Collapse
|