The C-score: a Bayesian framework to sharply improve proteoform scoring in high-throughput top down proteomics.

ABSTRACT: The automated processing of data generated by top down proteomics would benefit from improved scoring for protein identification and characterization of highly related protein forms (proteoforms). Here we propose the "C-score" (short for Characterization Score), a Bayesian approach to the proteoform identification and characterization problem, implemented within a framework to allow the infusion of expert knowledge into generative models that take advantage of known properties of proteins and top down analytical systems (e.g., fragmentation propensities, "off-by-1 Da" discontinuous errors, and intelligent weighting for site-specific modifications). The performance of the scoring system based on the initial generative models was compared to the current probability-based scoring system used

SUBMITTER: LeDuc RD 

PROVIDER: S-EPMC4084843 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature