TIDD: tool-independent and data-dependent machine learning for peptide identification.

ABSTRACT:

Background

In shotgun proteomics, database search engines have been developed to assign peptides to tandem mass (MS/MS) spectra and at the same time post-processing (or rescoring) approaches over the search results have been proposed to increase the number of confident peptide identifications. The most popular post-processing approaches such as Percolator and PeptideProphet have improved rates of peptide identifications by combining multiple scores from database search engines while applying machine learning techniques. Existing post-processing approaches, however, are limited when dealing with results from new search engines because their features for machine learning must be optimized specifically for each search engine.

Results

We propose a universal post-processing tool,

SUBMITTER: Li H 

PROVIDER: S-EPMC8969291 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature