Work

Cross-Run Hybrid Features Improve the Identification of Data-Independent Acquisition Proteomics

Year: 2024

Type: article

Abstract: The analysis of data-independent acquisition (DIA) mass spectrometry data is crucial for comprehensive proteomics studies. However, traditional single-run methods often fall short in terms of identification depth and consistency. We present HFDiscrim, a specialized multirun DIA analysis tool aimed at enhancing the depth and consistency of reliable peptide identifications of DIA analysis tools. HFDiscrim was extensively benchmarked on multiple data sets, including the MCB data set, the ccRCC data set, and a three-species benchmark mixture. Compared to PyProphet, HFDiscrim identified 22.04% more precursors, 19.1% more peptides, and 13.2% more proteins while maintaining a controllable false discovery rate. Furthermore, HFDiscrim demonstrated higher identification rates and improved reproducibility across multiple runs. HFDiscrim is publicly available at https://github.com/yachliu/HFDiscrim.

Source: ACS Omega

DOI: 10.1021/acsomega.4c07398

Authors: Yachen Liu, Longfei Mei, Chenyu Liang, Chuan-Qi Zhong, Mengsha Tong, Rongshan Yu

Institution: Xiamen University

Language: English

Topic: Advanced Proteomics Techniques and Applications

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,935

Funders: National Natural Science Foundation of China, Xiamen University