Browsing by Author "Marina, Kriek"

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    Quality control for data-dependent and data-independent mass-spectrometry-based proteomics
    (Stellenbosch : Stellenbosch University, 2020-12) Marina, Kriek; Tabb, David; Stoychev, Stoyan Hristov; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences: Molecular Biology and Human Genetics.
    ENGLISH ABSTRACT: Discovery proteomics is advancing at a rapid rate, and quality control of the technique must adapt accordingly. In 2012, a console application, QuaMeter, was created to produce quality control metrics for data-dependent proteomics based on metrics first designed by the USA National Institute for Standards and Technology (NIST). In 2014, the tool gained an identification-independent mode, which can generate 44 quality metrics still applicable only to data-dependent acquisition. However, the development of new data-independent acquisition methods in recent years introduces the need for a data-independent acquisition version of QuaMeter. The QuaMeter metrics must also still be analysed in a statistical framework such as R/Python to gain full value of the multivariate nature of the metrics. Biologists who are inexperienced at programming/ using a console might therefore find the use of such software limiting and there is a desire for a tool with a user interface with which to analyse the metrics. Here, I have created a console software for the analysis of data-independent acquisition results. The tool provides a platform for in-depth analysis of data quality. The tool is the first of its sort to allow the user to divide the retention time into segments and return quality metrics for each segment separately. This allows the researcher to gain extra insight into the chromatography steps, and as I illustrate here, the tool illuminates problems that would not have been visible if only one metric was provided for the entire file. In addition, the m/z axis is split into the data’s underlying isolation window structure and metrics calculated for each window separately to equip a researcher with additional information for method development. A set of metrics is also added which produce one value for the entire file for easy outlier detection among files. This project also involves the creation of a desktop application with user interface for running either of the two console applications. This tool can also perform some of the key downstream analysis regularly performed in quality control. Outlier detection is enabled via PCA, classification of longitudinal data as good or bad quality is performed with random forest analysis and individual metrics can also be visualized against their distributions. In addition, many quality control principles are explained and demonstrated in the context of the quality control metrics, such as experimental design, identifying sources of variability in an experiment and conventional quality control techniques such as outlier detection and classification of data quality are demonstrated.