Overcoming Species Boundaries in Peptide Identification with BICEPS

Currently, the reliable identification of peptides and proteins is only feasible when thoroughly annotated sequence databases are available. While sequencing capacities continue to grow, many organisms remain without reliable, fully annotated reference genomes required for proteomic analyses. Standard database search algorithms fail to identify peptides which are not exactly contained in a protein database. De novo searches are generally hindered by their restricted reliability and current error-tolerant search strategies are limited by global, heuristic tradeoffs between database and spectral information. We propose a Bayesian Information Criterion-driven Error-tolerant Peptide Search (BICEPS) and offer an open-source implementation based on this statistical criterion to

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