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January 22nd, 2025
Version: 2
GoldenHaystack Lab
bioinformatics
biorxiv

Quantifying the ~75-95% of Peptides in DIA-MS Datasets that were not Previously Quantified

Saxena, G.Open in Google Scholar•Fu, Q.Open in Google Scholar•Binek, A.Open in Google Scholar•Van Eyk, J.Open in Google Scholar

We demonstrate an algorithm termed GoldenHaystack (GH) that, compared to the leading DIA-MS algorithm, (a) quantifies and identifies with better FDR accuracy the peptides found in FASTA search spaces (~5-25% of analytes in DIA-MS datasets), (b) quantifies the remaining ~75-95% of analytes that were previously unquantified, and (c) runs ~40-200x faster (or ~1-10x faster than the LC-MS). Specifically, without a FASTA or spectral library, GH can deconvolute and accurately quantify chimeric LC-MS spectra. The central idea that enables this claim is: for sufficiently sized projects (e.g., [≥] ~50 LC-MS files), pairs of peptides that co-elute in one subset of LC-MS files do not exactly co-elute in a different subset of files. GH thus analyzes a project holistically: it uses multi-partite matching to match fragment ions across all samples, separates and regroups the fragment ions into unique analyte signatures, reduces stochastic noise, and then quantifies those unique analyte signatures.

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