Kriek, MMonyai, KMagcwebeba, TUDu Plessis, NStoychev, SHTabb, David L2020-10-312020-10-312020-05Kriek, M., Monyai, K., Magcwebeba, T.U., et al. 2020. Interrogating fractionation and other sources of variability in shotgun proteomes using quality metrics. Proteomics: https://doi.org/10.1002/pmic.2019003821615-98531615-9861doi.org/10.1002/pmic.201900382https://onlinelibrary.wiley.com/doi/full/10.1002/pmic.201900382http://hdl.handle.net/10204/11654Copyright: 2020, Wiley. Due to copyright restrictions, the attached PDF file contains the abstract of the full-text item. For access to the full-text item, please consult the publisher's website.The increasing amount of publicly available proteomics data creates opportunities for data scientists to investigate quality metrics in novel ways. QuaMeter IDFree is used to generate quality metrics from 665 RAW files and 97 WIFF files representing publicly available “shotgun” mass spectrometry datasets. These experiments are selected to represent Mycobacterium tuberculosis lysates, mouse MDSCs, and exosomes derived from human cell lines. Machine learning techniques are demonstrated to detect outliers within experiments and it is shown that quality metrics may be used to distinguish sources of variability among these experiments. In particular, the findings demonstrate that according to nested ANOVA performed on an SDS-PAGE shotgun principal component analysis, runs of fractions from the same gel regions cluster together rather than technical replicates, close temporal proximity, or even biological samples. This indicates that the individual fraction may have had a higher impact on the quality metrics than other factors. In addition, sample type, instrument type, mass analyzer, fragmentation technique, and digestion enzyme are identified as sources of variability. From a quality control perspective, the importance of study design and in particular, the run order, is illustrated in seeking ways to limit the impact of technical variability.enExosomesFractionationMycobacterium tuberculosisMyeloid-derivedsuppressor cellsQuality ControlQuaMeterShotgunArticleKriek, M., Monyai, K., Magcwebeba, T., Du Plessis, N., Stoychev, S., & Tabb, D. L. (2020). Interrogating fractionation and other sources of variability in shotgun proteomes using quality metrics. http://hdl.handle.net/10204/11654Kriek, M, K Monyai, TU Magcwebeba, N Du Plessis, SH Stoychev, and David L Tabb "Interrogating fractionation and other sources of variability in shotgun proteomes using quality metrics." (2020) http://hdl.handle.net/10204/11654Kriek M, Monyai K, Magcwebeba T, Du Plessis N, Stoychev S, Tabb DL. Interrogating fractionation and other sources of variability in shotgun proteomes using quality metrics. 2020; http://hdl.handle.net/10204/11654.TY - Article AU - Kriek, M AU - Monyai, K AU - Magcwebeba, TU AU - Du Plessis, N AU - Stoychev, SH AU - Tabb, David L AB - The increasing amount of publicly available proteomics data creates opportunities for data scientists to investigate quality metrics in novel ways. QuaMeter IDFree is used to generate quality metrics from 665 RAW files and 97 WIFF files representing publicly available “shotgun” mass spectrometry datasets. These experiments are selected to represent Mycobacterium tuberculosis lysates, mouse MDSCs, and exosomes derived from human cell lines. Machine learning techniques are demonstrated to detect outliers within experiments and it is shown that quality metrics may be used to distinguish sources of variability among these experiments. In particular, the findings demonstrate that according to nested ANOVA performed on an SDS-PAGE shotgun principal component analysis, runs of fractions from the same gel regions cluster together rather than technical replicates, close temporal proximity, or even biological samples. This indicates that the individual fraction may have had a higher impact on the quality metrics than other factors. In addition, sample type, instrument type, mass analyzer, fragmentation technique, and digestion enzyme are identified as sources of variability. From a quality control perspective, the importance of study design and in particular, the run order, is illustrated in seeking ways to limit the impact of technical variability. DA - 2020-05 DB - ResearchSpace DP - CSIR KW - Exosomes KW - Fractionation KW - Mycobacterium tuberculosis KW - Myeloid-derivedsuppressor cells KW - Quality Control KW - QuaMeter KW - Shotgun LK - https://researchspace.csir.co.za PY - 2020 SM - 1615-9853 SM - 1615-9861 T1 - Interrogating fractionation and other sources of variability in shotgun proteomes using quality metrics TI - Interrogating fractionation and other sources of variability in shotgun proteomes using quality metrics UR - http://hdl.handle.net/10204/11654 ER -