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How to talk about protein-level false discovery rates in shotgun proteomics
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0002-5401-5553
KTH, School of Biotechnology (BIO), Gene Technology.
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0001-5689-9797
2016 (English)In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 16, no 18, p. 2461-2469Article in journal (Refereed) Published
Abstract [en]

A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion. The standard technique to control for errors in such lists is to enforce a preset threshold for the false discovery rate (FDR). Many consider protein-level FDRs a difficult and vague concept, as the measurement entities, spectra, are manifestations of peptides and not proteins. Here, we argue that this confusion is unnecessary and provide a framework on how to think about protein-level FDRs, starting from its basic principle: the null hypothesis. Specifically, we point out that two competing null hypotheses are used concurrently in today's protein inference methods, which has gone unnoticed by many. Using simulations of a shotgun proteomics experiment, we show how confusing one null hypothesis for the other can lead to serious discrepancies in the FDR. Furthermore, we demonstrate how the same simulations can be used to verify FDR estimates of protein inference methods. In particular, we show that, for a simple protein inference method, decoy models can be used to accurately estimate protein-level FDRs for both competing null hypotheses.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2016. Vol. 16, no 18, p. 2461-2469
Keywords [en]
Bioinformatics, Data processing and analysis, Mass spectrometry-LC-MS/MS, Protein inference, Simulation, Statistical analysis
National Category
Biophysics Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-196441DOI: 10.1002/pmic.201500431ISI: 000385813600005PubMedID: 27503675Scopus ID: 2-s2.0-84988369698OAI: oai:DiVA.org:kth-196441DiVA, id: diva2:1050537
Note

QC 20161129

Available from: 2016-11-29 Created: 2016-11-14 Last updated: 2018-10-01Bibliographically approved
In thesis
1. Statistical and machine learning methods to analyze large-scale mass spectrometry data
Open this publication in new window or tab >>Statistical and machine learning methods to analyze large-scale mass spectrometry data
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern biology is faced with vast amounts of data that contain valuable information yet to be extracted. Proteomics, the study of proteins, has repositories with thousands of mass spectrometry experiments. These data gold mines could further our knowledge of proteins as the main actors in cell processes and signaling. Here, we explore methods to extract more information from this data using statistical and machine learning methods.

First, we present advances for studies that aggregate hundreds of runs. We introduce MaRaCluster, which clusters mass spectra for large-scale datasets using statistical methods to assess similarity of spectra. It identified up to 40% more peptides than the state-of-the-art method, MS-Cluster. Further, we accommodated large-scale data analysis in Percolator, a popular post-processing tool for mass spectrometry data. This reduced the runtime for a draft human proteome study from a full day to 10 minutes.

Second, we clarify and promote the contentious topic of protein false discovery rates (FDRs). Often, studies report lists of proteins but fail to report protein FDRs. We provide a framework to systematically discuss protein FDRs and take away hesitance. We also added protein FDRs to Percolator, opting for the best-peptide approach which proved superior in a benchmark of scalable protein inference methods.

Third, we tackle the low sensitivity of protein quantification methods. Current methods lack proper control of error sources and propagation. To remedy this, we developed Triqler, which controls the protein quantification FDR through a Bayesian framework. We also introduce MaRaQuant, which proposes a quantification-first approach that applies clustering prior to identification. This reduced the number of spectra to be searched and allowed us to spot unidentified analytes of interest. Combining these tools outperformed the state-of-the-art method, MaxQuant/Perseus, and found enriched functional terms for datasets that had none before.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 64
Series
TRITA-CBH-FOU ; 2018:45
Keywords
mass spectrometry - LC-MS/MS, statistical analysis, data processing and analysis, protein inference, large-scale studies, simulation, protein quantification, clustering, machine learning, Bayesian statistics
National Category
Bioinformatics (Computational Biology)
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-235629 (URN)978-91-7729-967-7 (ISBN)
Public defence
2018-10-24, Atrium, Nobels väg 12B, Solna, 13:00 (English)
Opponent
Supervisors
Note

QC 20181001

Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-01Bibliographically approved

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The, MatthewTasnim, AyeshaKäll, Lukas

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