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Data-Dependent Scoring Parameter Optimization in MS-GF plus Using Spectrum Quality Filter

Title
Data-Dependent Scoring Parameter Optimization in MS-GF plus Using Spectrum Quality Filter
Author
백은옥
Keywords
proteomics; search algorithm; parameter optimization; machine learning; peptide identification
Issue Date
2018-10
Publisher
AMER CHEMICAL SOC
Citation
JOURNAL OF PROTEOME RESEARCH, v. 17, no. 10, page. 3593-3598
Abstract
Most database search tools for proteomics have their own scoring parameter sets depending on experimental conditions such as fragmentation methods, instruments, digestion enzymes, and so on. These scoring parameter sets are usually predefined by tool developers and cannot be modified by users. The number of different experimental conditions grows as the technology develops, and the given set of scoring parameters could be suboptimal for tandem mass spectrometry data acquired using new sample preparation or fragmentation methods. Here we introduce a new approach to optimize scoring parameters in a data-dependent manner using a spectrum quality filter. The new approach conducts a preliminary search for the spectra selected by the spectrum quality filter. Search results from the preliminary search are used to generate data-dependent scoring parameters; then, the full search over the entire input spectra is conducted using the learned scoring parameters. We show that the new approach yields more and better peptide-spectrum matches than the conventional search using built-in scoring parameters when compared at the same 1% false discovery rate.
URI
https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00415https://repository.hanyang.ac.kr/handle/20.500.11754/120354
ISSN
1535-3893; 1535-3907
DOI
10.1021/acs.jproteome.8b00415
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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