当前位置: X-MOL 学术Proteomics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Interrogating Fractionation and Other Sources of Variability in Shotgun Proteomes Using Quality Metrics.
Proteomics ( IF 3.4 ) Pub Date : 2020-05-16 , DOI: 10.1002/pmic.201900382
Marina Kriek 1, 2, 3, 4 , Koena Monyai 5 , Tandeka U Magcwebeba 2, 3, 4 , Nelita Du Plessis 2, 3, 4 , Stoyan H Stoychev 5 , David L Tabb 1, 2, 3, 4
Affiliation  

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.

中文翻译:

使用质量指标询问 Shotgun 蛋白质组中的分级和其他变异来源。

越来越多的公开蛋白质组学数据为数据科学家以新颖的方式研究质量指标创造了机会。QuaMeter IDFree 用于从 665 个 RAW 文件和 97 个 WIFF 文件中生成质量指标,这些文件代表公开可用的“霰弹枪”质谱数据集。选择这些实验来代表结核分枝杆菌裂解物、小鼠 MDSC 和源自人类细胞系的外泌体。机器学习技术被证明可以检测实验中的异常值,并且表明质量指标可用于区分这些实验中的变异来源。特别是,研究结果表明,根据对 SDS-PAGE 霰弹枪主成分分析进行的嵌套方差分析,来自相同凝胶区域的级分聚集在一起,而不是技术重复、时间接近甚至生物样本。这表明单个分数可能比其他因素对质量度量产生更大的影响。此外,样品类型、仪器类型、质量分析器、碎裂技术和消化酶被确定为变异来源。从质量控制的角度来看,
更新日期:2020-05-16
down
wechat
bug