STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data

• Published in: Briefings in bioinformatics Vol. 25; no. 6
• Main Authors: Ran, Peng, Wang, Yunzhi, Li, Kai, He, Shiman, Tan, Subei, Lv, Jiacheng, Zhu, Jiajun, Tang, Shaoshuai, Feng, Jinwen, Qin, Zhaoyu, Li, Yan, Huang, Lin, Yin, Yanan, Zhu, Lingli, Yang, Wenjun, Ding, Chen
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 23.09.2024; Oxford Publishing Limited (England)
• Subjects: Algorithms; Benchmarking - methods; Benchmarks; Comparative analysis; Databases, Protein; Datasets; Humans; Mass spectrometry; Mass Spectrometry - methods; Mass Spectrometry - standards; Mass spectroscopy; Problem Solving Protocol; Proteins; Proteomics; Proteomics - methods; Proteomics - standards; Reproducibility of Results; Software; STAVER algorithm; non-biological noise; quantitative proteomics; bioinformatics; data-independent acquisition; proteomics analysis
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae553

Abstract Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER’s robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.