Format

Send to

Choose Destination
J Proteome Res. 2018 Dec 7;17(12):4051-4060. doi: 10.1021/acs.jproteome.8b00485. Epub 2018 Oct 11.

Expanding the Use of Spectral Libraries in Proteomics.

Author information

1
Institute for Systems Biology , Seattle , Washington 98109 , United States.
2
European Molecular Biology Laboratory , European Bioinformatics Institute , Wellcome Trust Genome Campus , Hinxton , Cambridge CB10 1SD , United Kingdom.
3
University of California , San Francisco , California 94158 , United States.
4
Proteomics and Bioanalytics , Technical University of Munich , Freising 85354 , Germany.
5
Sciex , Concord , Ontario L4K4 V8 , Canada.
6
Department of Computer Science, Center for Bioinformatics , University of Tübingen , Sand 14 , 72076 , Tübingen , Germany.
7
Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health , KTH - Royal Institute of Technology , Stockholm 114 28 , Sweden.
8
Thermo Fisher Scientific Bremen , Hanna-Kunath Straße 11 , 28199 Bremen , Germany.
9
Bioinformatics , Friedrich-Schiller-University Jena , 07743 Jena , Germany.
10
Luxembourg Centre for Systems Biomedicine , University of Luxembourg , 6 avenue du Swing , L-4367 Belvaux , Luxembourg.
11
Bioinformatics Research Group , University of Applied Sciences Upper Austria , Hagenberg 4232 , Austria.
12
Bavarian Biomolecular Mass Spectrometry Center , Technical University of Munich , Freising 85354 , Germany.
13
VIB-UGent Center for Medical Biotechnology , VIB , B-9000 Ghent , Belgium.
14
Helmholtz-Centre for Environmental Research - UFZ , 04318 Leipzig , Germany.
15
Waters Corporation , Wilmslow SK9 4AX , United Kingdom.
16
Department of Molecular Medicine , The Scripps Research Institute , La Jolla , California 92037 , United States.
17
Thermo Fisher Scientific , 355 River Oaks Parkway San Jose , California 95134 , United States.
18
Department of Population Health Sciences and Bristol Veterinary School, Faculty of Health Sciences , University of Bristol , Bristol BS9 1BN , U.K.
19
Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology , Medical University of Vienna , Währinger Gürtel 18-20 , Vienna 1090 , Austria.
20
The International Agency for Research on Cancer , 150 Cours Albert Thomas , 69372 Lyon Cedex 08 , France.
21
Department of Stress and Developmental Biology , Leibniz Institute of Plant Biochemistry , 06120 Halle , Germany.
22
German Centre for Integrative Biodiversity Research (iDiv) , Halle-Jena-Leipzig , 04103 Leipzig , Germany.
23
Clinical Chemistry Service , Centre Hospitalier Universitaire Vaudois , 1011 Lausanne , Switzerland.
24
Department of Chemical and Biological Engineering , The Hong Kong University of Science and Technology , Clear Water Bay 999077 , Hong Kong.
25
Center for Computational Mass Spectrometry, Department of Computer Science and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California , San Diego 92093-0404 , United States.
26
The Donnelly Centre , University of Toronto , 160 College Street , Toronto , Ontario M5S 3E1 , Canada.

Abstract

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

KEYWORDS:

Dagstuhl Seminar; Proteomics Standards Initiative; formats; mass spectrometry; meeting report; spectral libraries; standards

Supplemental Content

Full text links

Icon for American Chemical Society Icon for PubMed Central
Loading ...
Support Center