MSCypher: an integrated database searching and machine learning workflow for multiplexed proteomics.

Abstract

Improvements in shotgun proteomics approaches are hampered by increases in multiplexed (chimeric) spectra, as improvements in peak capacity, sensitivity or dynamic range all increase the number of co-eluting peptides. This results in diminishing returns using traditional search algorithms, as co-fragmented spectra are known to decrease identification rates. Here we describe MSCypher, a freely available software suite that enables an extensible workflow including a hybrid supervised machine learned strategy that dynamically adjusts to individual datasets. This results in improved identification rates and quantification of low-abundant peptides and proteins. In addition, the integration of peptide de novo sequencing and database searching enables an unbiased view of variants and high-intensity unassigned peptide spectral matches.\n\nHighlightsO_LIOpen-source end-to-end label-free proteomics workflow\nC_LIO_LIIntegrated database searching and machine learning\nC_LIO_LICustomisable and extensible workflow including de novo sequencing\nC_LIO_LIOptimised for multiplexed spectra, challenging proteomics datasets and peptidomics applications\nC_LI