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Integrated identification and quantification error probabilities for shotgun proteomics
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.ORCID iD: 0000-0002-5401-5553
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.ORCID iD: 0000-0001-5689-9797
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differentially expressed proteins use intermediate filters in an attempt to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered datasets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical dataset we discovered 35 proteins at 5% FDR, with the original study discovering none at this threshold. Compellingly, these proteins showed enrichment for functional annotation terms. The model executes in minutes and is freely available at https://pypi.org/project/triqler/.

Keywords [en]
mass spectrometry - LC-MS/MS, statistical analysis, data processing and analysis, protein quantification, large-scale studies, Bayesian statistics
National Category
Bioinformatics (Computational Biology)
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-235625OAI: oai:DiVA.org:kth-235625DiVA, id: diva2:1252228
Note

QC 20181001

Available from: 2018-10-01 Created: 2018-10-01 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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Käll, Lukas

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