RT Journal Article
SR Electronic
T1 Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 152744
DO 10.1101/152744
A1 Ryan Peckner
A1 Samuel A Myers
A1 Jarrett D Egertson
A1 Richard S Johnson
A1 Steven A Carr
A1 Michael J MacCoss
A1 Jacob D Jaffe
YR 2017
UL http://biorxiv.org/content/early/2017/06/20/152744.abstract
AB Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.