Thesis Open Access

# Computational solutions for quality control of mass spectrometry-based proteomics

Bittremieux, Wout

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"description": "<p>Mass spectrometry is an advanced analytical technique that can be used to identify and quantify the protein content of complex biological samples. Unfortunately mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, to inspire confidence in the generated results a comprehensive and systematic approach to quality control is an essential requirement.</p>\n\n<p>In this dissertation we present several computational solutions for quality control of mass spectrometry-based proteomics. In order to successfully employ comprehensive quality control procedures to assess the validity of<br>\nthe experimental results three basic requirements need to be fulfilled: (i) descriptive quality control metrics that characterize the experimental performance should be defined; (ii) the basic technical infrastructure to unambiguously store and communicate quality control data has to be available; (iii) advanced analysis techniques are needed to derive actionable insights from the quality control data.</p>\n\n<p>First, we show how secondary metrics that are not related to the spectral data, such as instrument metrics and environment variables, provide a complementary view on the experimental quality. We present the user-friendly Instrument MONitoring DataBase (iMonDB) toolset to manage and visualize these secondary metrics. Second, we introduce the Human Proteome Organization (HUPO) \u2013 Proteomics Standards Initiative (PSI) Quality Control<br>\nworking group, whose aim it is to provide a unifying framework for quality control data. We show how the standard qcML file format for mass spectrometry quality control data can be used as the focal point of a strong community-driven ecosystem of quality control tools and methodologies. Third, we present an unsupervised outlier detection workflow to automatically discriminate low-quality mass spectrometry experiments from high-quality<br>\nmass spectrometry experiments. We show how this workflow can replicate expert knowledge in a data-driven fashion, enabling the substitution of time-consuming manual analyses by automated decision-making. Finally, we show how approximate nearest neighbor indexing can be used to speed up spectral library open modification searching by several orders of magnitude, leading to a record number of spectrum identifications in a minimal processing time.</p>\n\n<p>We conclude with an overview of potential future steps that can be taken to further improve computational quality control methods for mass spectrometry-based proteomics, as well as discussing some of the opportunities to apply advanced machine learning techniques in this field with related challenges.</p>",
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"affiliation": "University of Antwerp, Antwerp, Belgium",
"name": "Laukens, Kris"
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"affiliation": "University of Antwerp, Antwerp, Belgium",
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