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.
Kenney, Grace E; Dassama, Laura M K; Pandelia, Maria-Eirini et al. (2018) The biosynthesis of methanobactin. Science 359:1411-1416 |
Swaroop, Alok; Oyer, Jon A; Will, Christine M et al. (2018) An activating mutation of the NSD2 histone methyltransferase drives oncogenic reprogramming in acute lymphocytic leukemia. Oncogene : |
Davis, Roderick G; Park, Hae-Min; Kim, Kyunggon et al. (2018) Top-Down Proteomics Enables Comparative Analysis of Brain Proteoforms Between Mouse Strains. Anal Chem 90:3802-3810 |
LeDuc, Richard D; Schwämmle, Veit; Shortreed, Michael R et al. (2018) ProForma: A Standard Proteoform Notation. J Proteome Res 17:1321-1325 |
Aebersold, Ruedi; Agar, Jeffrey N; Amster, I Jonathan et al. (2018) How many human proteoforms are there? Nat Chem Biol 14:206-214 |
Turcan, Sevin; Makarov, Vladimir; Taranda, Julian et al. (2018) Mutant-IDH1-dependent chromatin state reprogramming, reversibility, and persistence. Nat Genet 50:62-72 |
Lyon, Yana A; Riggs, Dylan; Fornelli, Luca et al. (2018) The Ups and Downs of Repeated Cleavage and Internal Fragment Production in Top-Down Proteomics. J Am Soc Mass Spectrom 29:150-157 |
Fornelli, Luca; Toby, Timothy K; Schachner, Luis F et al. (2018) Top-down proteomics: Where we are, where we are going? J Proteomics 175:3-4 |
Fisher, Oriana S; Kenney, Grace E; Ross, Matthew O et al. (2018) Characterization of a long overlooked copper protein from methane- and ammonia-oxidizing bacteria. Nat Commun 9:4276 |
Gruppuso, Philip A; Boylan, Joan M; Zabala, Valerie et al. (2018) Stability of histone post-translational modifications in samples derived from liver tissue and primary hepatic cells. PLoS One 13:e0203351 |
Showing the most recent 10 out of 53 publications