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Improving the Comprehensiveness of Large-Scale Proteomics Experiments Using Advanced Computational Tools and Accurate Multiple Hypothesis Testing Statistics

机译:使用先进的计算工具和准确的多个假设检验统计数据来提高大规模蛋白质组学实验的综合性

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摘要

Mass spectrometry based technology for the analysis of complex protein mixtures has improved at an amazing rate. With each new instrument release, mass spectrometers have become more sensitive and have faster MS/MS data acquisition speeds. Furthermore, instruments are continuously improving the dynamic range, mass accuracy, and resolution of the resulting mass spectrometry data. All of these developments have increased the number of peptides that can be identified and quantified without extending the overall analysis time. While the technological hardware advances that are required to increase the number of peptide identifications by 50% with a constant analysis time is monumental, we have been able to demonstrate that increase in performance without increasing the analysis time at all. To accomplish this, we have made use of improved database searching algorithms, spectrum library searching, use of chromatographic retention time, powerful machine learning tools, accurate multiple hypothesis testing statistics, and many more. Strategies will be discussed on how to increase the comprehensiveness of any dataset using improved data analysis strategies.
机译:基于质谱的复杂蛋白质混合物分析技术得到了惊人的发展。随着每个新仪器的发布,质谱仪变得更加灵敏,并且具有更快的MS / MS数据采集速度。此外,仪器还在不断改善动态范围,质量准确性和所得质谱数据的分辨率。所有这些发展都增加了无需延长总分析时间即可鉴定和定量的肽的数量。虽然在恒定的分析时间内将肽鉴定数量增加50%所需的技术硬件进步是巨大的,但我们已经能够证明性能的提高完全不增加分析时间。为此,我们使用了改进的数据库搜索算法,光谱库搜索,色谱保留时间的使用,强大的机器学习工具,准确的多个假设检验统计数据等等。将讨论如何使用改进的数据分析策略来提高任何数据集的全面性的策略。

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