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

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.