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EP38456
Poster Title: CCS-Aware DIA Data Analysis in PaSER
Submitted on 09 Mar 2022
Author(s): Robin Park1; Qin Fu2; Tharan Srikumar1; Michael Krawitzky1; Chistopher Adams1; Dennis Trede1; Gary Kruppa1; Jennifer E. Van Eyk2; Rohan Thakur1
Affiliations: 1Bruker Daltonik GmbH; 2Cedars-Sinai Medical Center, Los Angeles, CA
Poster Views: 256
Submitted on 09 Mar 2022
Author(s): Robin Park1; Qin Fu2; Tharan Srikumar1; Michael Krawitzky1; Chistopher Adams1; Dennis Trede1; Gary Kruppa1; Jennifer E. Van Eyk2; Rohan Thakur1
Affiliations: 1Bruker Daltonik GmbH; 2Cedars-Sinai Medical Center, Los Angeles, CA
Poster Views: 256
Abstract: Since Venable et al. first introduced dataindependent acquisition (DIA) in 2004, DIA acquisition and data analysis tools have been continuously improved, making DIA a vital technology to identify and quantify thousands of proteins with high reproducibility and deep proteomics coverage. DIA data analysis, in general, relies on a spectral library constructed from data-dependent acquisition (DDA). Alternatively, the library-free approach searches DIA data directly against a fasta database. We combined a recently developed CCS-aware ProLuCID-4D search engine using ion mobility and a spectral library-based DIA approach to increase coverage.
To build spectral libraries, we ran ProLuCID 4D GPU search engine of PaSER on DDA data. DTASelect filtered the scored spectra by 1% protein-level FDR using discriminant analysis. We used DTASelect output files as input to EasyPQP to generate spectral libraries. We ran DIA-NN to analyze dia-PASEF data using a spectral library, while we ran ProLuCID 4D GPU search engine for library-free database search.
Summary: To build spectral libraries, we ran ProLuCID 4D GPU search engine of PaSER on DDA data. DTASelect filtered the scored spectra by 1% protein-level FDR using discriminant analysis. We used DTASelect output files as input to EasyPQP to generate spectral libraries. We ran DIA-NN to analyze dia-PASEF data using a spectral library, while we ran ProLuCID 4D GPU search engine for library-free database search.
To build spectral libraries, we ran ProLuCID 4D GPU search engine of PaSER on DDA data. DTASelect filtered the scored spectra by 1% protein-level FDR using discriminant analysis. We used DTASelect output files as input to EasyPQP to generate spectral libraries. We ran DIA-NN to analyze dia-PASEF data using a spectral library, while we ran ProLuCID 4D GPU search engine for library-free database search.
Summary: To build spectral libraries, we ran ProLuCID 4D GPU search engine of PaSER on DDA data. DTASelect filtered the scored spectra by 1% protein-level FDR using discriminant analysis. We used DTASelect output files as input to EasyPQP to generate spectral libraries. We ran DIA-NN to analyze dia-PASEF data using a spectral library, while we ran ProLuCID 4D GPU search engine for library-free database search.
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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