PT  - JOURNAL ARTICLE
AU  - Eugene A. Kapp
AU  - Giuseppe Infusini
AU  - Yunshan Zhong
AU  - Laura F. Dagley
AU  - Terence P. Speed
AU  - Andrew I. Webb
TI  - MSCypher: an integrated database searching and machine learning workflow for multiplexed proteomics
AID  - 10.1101/397257
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 397257
4099  - http://biorxiv.org/content/early/2018/08/22/397257.short
4100  - http://biorxiv.org/content/early/2018/08/22/397257.full
AB  - 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.HighlightsOpen-source end-to-end label-free proteomics workflowIntegrated database searching and machine learningCustomisable and extensible workflow including de novo sequencingOptimised for multiplexed spectra, challenging proteomics datasets and peptidomics applications