The analysis of proteomic data has changed greatly since the term ?proteomics? was coined over 17 years ago. Early experimentation often involved a mass spectrometrist huddled over their instrument, carefully interrogating an analyte of interest and manually interpreting the resulting spectra. Modern LC-MS analyses are much different; automated acquisition methods drive mass spectrometers in a data dependent mode, followed by high-throughput deconvolution and algorithm based searching. While current approaches are adequate for projects of limited scope, a fundamental leap is required to service the large-scale, clinical proteomics projects of the future. TR&D 3 describes the AUTOPILOT software framework, a suite of components that handles the complexity of top-down experiments and presents users with intuitive application interfaces. Each software component, broken into the aims below, either expands upon our existing high-throughput framework at the Proteomics Center of Excellence in novel ways or creates completely new functionality.
Specific Aim 1 : To Acquire Data Intelligently Specific Aim 2: To Integrate High-Throughput Deconvolution, Searching, and Advanced Scoring Specific Aim 3: To Quantitate Proteins Confidently Specific Aim 4: To Aid Data Analysis, Visualization, and Reporting When coupled with the advancements in separations (TR&D 1) and instrumentation (TR&D 2) in this proposal, the AUTOPILOT framework stands to revolutionize clinical proteomics. For the first time it will be possible to conduct clinical, top-down proteomics projects on a scale to robustly test its value in improving human health. AUTOPILOT empowers the National Resource for Translational and Developmental Proteomics to manage and execute the large-scale proteomics projects required by our DBPs and addresses many of the shortcomings of current high-throughput systems.
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