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21 results on '"Brademan, Dain R."'

2. The One Hour Human Proteome

Author

Serrano, Lia R., Peters-Clarke, Trenton M., Arrey, Tabiwang N., Damoc, Eugen, Robinson, Margaret Lea, Lancaster, Noah M., Shishkova, Evgenia, Moss, Corinne, Pashkova, Anna, Sinitcyn, Pavel, Brademan, Dain R., Quarmby, Scott T., Peterson, Amelia C., Zeller, Martin, Hermanson, Daniel, Stewart, Hamish, Hock, Christian, Makarov, Alexander, Zabrouskov, Vlad, and Coon, Joshua J.

Published
2024
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3. Defining mitochondrial protein functions through deep multiomic profiling

Author

Rensvold, Jarred W., Shishkova, Evgenia, Sverchkov, Yuriy, Miller, Ian J., Cetinkaya, Arda, Pyle, Angela, Manicki, Mateusz, Brademan, Dain R., Alanay, Yasemin, Raiman, Julian, Jochem, Adam, Hutchins, Paul D., Peters, Sean R., Linke, Vanessa, Overmyer, Katherine A., Salome, Austin Z., Hebert, Alexander S., Vincent, Catherine E., Kwiecien, Nicholas W., Rush, Matthew J. P., Westphall, Michael S., Craven, Mark, Akarsu, Nurten A., Taylor, Robert W., Coon, Joshua J., and Pagliarini, David J.

Published
2022
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6. A large-scale genome–lipid association map guides lipid identification

Author

Linke, Vanessa, Overmyer, Katherine A., Miller, Ian J., Brademan, Dain R., Hutchins, Paul D., Trujillo, Edna A., Reddy, Thiru R., Russell, Jason D., Cushing, Emily M., Schueler, Kathryn L., Stapleton, Donald S., Rabaglia, Mary E., Keller, Mark P., Gatti, Daniel M., Keele, Gregory R., Pham, Duy, Broman, Karl W., Churchill, Gary A., Attie, Alan D., and Coon, Joshua J.

Published
2020
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8. Improved Structural Characterization of Glycerophospholipids and Sphingomyelins with Real-Time Library Searching

Author

Brademan, Dain R., primary, Overmyer, Katherine A., additional, He, Yuchen, additional, Barshop, William D., additional, Canterbury, Jesse D., additional, Bills, Brandon J., additional, Anderson, Benton J., additional, Hutchins, Paul D., additional, Sharma, Seema, additional, Zabrouskov, Vlad, additional, McAlister, Graeme C., additional, and Coon, Joshua J., additional

Published
2023
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9. LipiDex 2 Integrates MSnTree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics

Author

Anderson, Benton J., Brademan, Dain R., He, Yuchen, Overmyer, Katherine A., and Coon, Joshua J.

Abstract

As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography–mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. In silicospectral library generation now supports tandem mass spectral (MSn) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.

Published
2024
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13. Large-Scale Multi-omic Analysis of COVID-19 Severity

Author

Overmyer, Katherine A., primary, Shishkova, Evgenia, additional, Miller, Ian J., additional, Balnis, Joseph, additional, Bernstein, Matthew N., additional, Peters-Clarke, Trenton M., additional, Meyer, Jesse G., additional, Quan, Qiuwen, additional, Muehlbauer, Laura K., additional, Trujillo, Edna A., additional, He, Yuchen, additional, Chopra, Amit, additional, Chieng, Hau C., additional, Tiwari, Anupama, additional, Judson, Marc A., additional, Paulson, Brett, additional, Brademan, Dain R., additional, Zhu, Yunyun, additional, Serrano, Lia R., additional, Linke, Vanessa, additional, Drake, Lisa A., additional, Adam, Alejandro P., additional, Schwartz, Bradford S., additional, Singer, Harold A., additional, Swanson, Scott, additional, Mosher, Deane F., additional, Stewart, Ron, additional, Coon, Joshua J., additional, and Jaitovich, Ariel, additional

Published
2021
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14. Large-scale Multi-omic Analysis of COVID-19 Severity

Author

Overmyer, Katherine A., primary, Shishkova, Evgenia, additional, Miller, Ian J., additional, Balnis, Joseph, additional, Bernstein, Matthew N., additional, Peters-Clarke, Trenton M., additional, Meyer, Jesse G., additional, Quan, Qiuwen, additional, Muehlbauer, Laura K., additional, Trujillo, Edna A., additional, He, Yuchen, additional, Chopra, Amit, additional, Chieng, Hau C., additional, Tiwari, Anupama, additional, Judson, Marc A., additional, Paulson, Brett, additional, Brademan, Dain R., additional, Zhu, Yunyun, additional, Serrano, Lia R., additional, Linke, Vanessa, additional, Drake, Lisa A., additional, Adam, Alejandro P., additional, Schwartz, Bradford S., additional, Singer, Harold A., additional, Swanson, Scott, additional, Mosher, Deane F., additional, Stewart, Ron, additional, Coon, Joshua J., additional, and Jaitovich, Ariel, additional

Published
2020
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19. Comprehensive Single-Shot Proteomics with FAIMS on a Hybrid Orbitrap Mass Spectrometer

Author

Hebert, Alexander S., primary, Prasad, Satendra, additional, Belford, Michael W., additional, Bailey, Derek J., additional, McAlister, Graeme C., additional, Abbatiello, Susan E., additional, Huguet, Romain, additional, Wouters, Eloy R., additional, Dunyach, Jean-Jacques, additional, Brademan, Dain R., additional, Westphall, Michael S., additional, and Coon, Joshua J., additional

Published
2018
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20. LipiDex 2 Integrates MS n Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics.

Author

Anderson BJ, Brademan DR, He Y, Overmyer KA, and Coon JJ

Subjects
Lipids chemistry, Lipids analysis, Software, Chromatography, Liquid methods, Algorithms, Lipidomics methods, Quality Control, Tandem Mass Spectrometry
Abstract

As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography-mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. In silico spectral library generation now supports tandem mass spectral (MS n ) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.

Published
2024
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21. Large-scale Multi-omic Analysis of COVID-19 Severity.

Author

Overmyer KA, Shishkova E, Miller IJ, Balnis J, Bernstein MN, Peters-Clarke TM, Meyer JG, Quan Q, Muehlbauer LK, Trujillo EA, He Y, Chopra A, Chieng HC, Tiwari A, Judson MA, Paulson B, Brademan DR, Zhu Y, Serrano LR, Linke V, Drake LA, Adam AP, Schwartz BS, Singer HA, Swanson S, Mosher DF, Stewart R, Coon JJ, and Jaitovich A

Abstract

We performed RNA-Seq and high-resolution mass spectrometry on 128 blood samples from COVID-19 positive and negative patients with diverse disease severities. Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a comparative analysis with published data and a machine learning approach for prediction of COVID-19 severity.

Published
2020
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