ArticleACS omega2024
Cross-Run Hybrid Features Improve the Identification of Data-Independent Acquisition Proteomics.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
6 authors.
Funding
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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.
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Registered trials
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