Thesis Open Access

Computational solutions for quality control of mass spectrometry-based proteomics

Bittremieux, Wout


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    <subfield code="a">&lt;p&gt;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.&lt;/p&gt;

&lt;p&gt;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&lt;br&gt;
the 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.&lt;/p&gt;

&lt;p&gt;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) – Proteomics Standards Initiative (PSI) Quality Control&lt;br&gt;
working 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&lt;br&gt;
mass 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.&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;</subfield>
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