How to Identify Low-Abundance Modified Peptides with Proteomics Mass Spectrometry
Our White Paper explains the solid analytic methodology behind SorcererScore™. We believe SorcererScore uniquely enables deep proteomics by presenting peptide IDs close to their raw data from using simple-to-understand analytics. Its foundation is based on well-established components, namely the cross-correlation search engine (John Yates Lab, Scripps), target-decoy search (Steve Gygi Lab, Harvard), and a rigorous peptide-to-protein framework (Ruedi Aebersold Lab, ETH Zurich).
Unlike opaque software that report peptide IDs not readily verifiable, SorcererScore respects the integrity of the science by being transparent and hypothesis-driven, and by presenting data-driven evidence that can be drilled down to any level by scientists.
Unlike opaque software that report peptide IDs not readily verifiable, SorcererScore respects the integrity of the science by being transparent and hypothesis-driven, and by presenting data-driven evidence that can be drilled down to any level by scientists.
