Change search
ReferencesLink to record
Permanent link

Direct link
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.ORCID iD: 0000-0001-5689-9797
(English)Manuscript (preprint) (Other academic)
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. Many researchers consider protein-level false discovery rates 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 false discovery rates, 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 false discovery rate. Furthermore, we demonstrate how the same simulations can be used to verify false discovery rate 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 false discovery rates for both competing null hypotheses.

Keyword [en]
mass spectrometry - LC-MS/MS, statistical analysis, data processing and analysis, protein inference, simulation
National Category
Bioinformatics and Systems Biology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-185116OAI: oai:DiVA.org:kth-185116DiVA: diva2:918719
Note

QC 20160412

Available from: 2016-04-11 Created: 2016-04-11 Last updated: 2016-04-12Bibliographically 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
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As in many other fields, biology is faced with enormous amounts ofdata that contains valuable information that is yet to be extracted. The field of proteomics, the study of proteins, has the luxury of having large repositories containing data from tandem mass-spectrometry experiments, readily accessible for everyone who is interested. At the same time, there is still a lot to discover about proteins as the main actors in cell processes and cell signaling.

In this thesis, we explore several methods to extract more information from the available data using methods from statistics and machine learning. In particular, we introduce MaRaCluster, a new method for clustering mass spectra on large-scale datasets. This method uses statistical methods to assess similarity between mass spectra, followed by the conservative complete-linkage clustering algorithm.The combination of these two resulted in up to 40% more peptide identifications on its consensus spectra compared to the state of the art method.

Second, we attempt to clarify and promote protein-level false discovery rates (FDRs). Frequently, studies fail to report protein-level FDRs even though the proteins are actually the entities of interest. We provided a framework in which to discuss protein-level FDRs in a systematic manner to open up the discussion and take away potential hesitance. We also benchmarked some scalable protein inference methods and included the best one in the Percolator package. Furthermore, we added functionality to the Percolator package to accommodate the analysis of studies in which many runs are aggregated. This reduced the run time for a recent study regarding a draft human proteome from almost a full day to just 10 minutes on a commodity computer, resulting in a list of proteins together with their corresponding protein-level FDRs.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. vi, 44 p.
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2016:3
Keyword
mass spectrometry - LC-MS/MS, statistical analysis, data processing and analysis, protein inference, large-scale studies, simulation
National Category
Bioinformatics and Systems Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-185149 (URN)978-91-7595-933-7 (ISBN)
Presentation
2016-05-03, Pascal, våning 6 i Gamma-huset, Science for Life Laboratory, Tomtebodavägen 23, Solna, 13:00 (English)
Opponent
Supervisors
Note

QC 20160412

Available from: 2016-04-12 Created: 2016-04-11 Last updated: 2016-04-12Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
The, MatthewKäll, Lukas
By organisation
Gene Technology
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 254 hits
ReferencesLink to record
Permanent link

Direct link