Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins-Reference-Cited by-同舟云学术

Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins

Published:2019-03-12 Issue:4 Volume:18 Page:1477-1485
ISSN:1535-3893
Container-title:Journal of Proteome Research
language:en
Short-container-title:J. Proteome Res.

Author:

Griss Johannes¹²^ORCID,Stanek Florian³⁴,Hudecz Otto³⁴,Dürnberger Gerhard³⁴⁵,Perez-Riverol Yasset²,Vizcaíno Juan Antonio²,Mechtler Karl³⁴^ORCID

Affiliation:

1. Department of Dermatology, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria

2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, CB10 1SD Hinxton, Cambridge, United Kingdom

3. Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria

4. Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria

5. Gregor Mendel Institute of Molecular Plant Biology (GMI), Vienna Biocenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna, Austria

Funder

H2020 Marie Sklodowska-Curie Actions

Austrian Science Fund

Wellcome Trust

European Molecular Biology Laboratory

Publisher

American Chemical Society (ACS)

Subject

General Chemistry,Biochemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.8b00377

Reference23 articles.

1. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ

2. Future Prospects of Spectral Clustering Approaches in Proteomics

3. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

4. Spectral archives: extending spectral libraries to analyze both identified and unidentified spectra

5. MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics

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