Abstract
Large databases (>106 sequences) used in metaproteomic and proteogenomic studies present challenges in matching peptide sequences to MS/MS data using database-search programs. Most notably, strict filtering to avoid false-positive matches leads to more false negatives, thus constraining the number of peptide matches. To address this challenge, we developed a two-step method wherein matches derived from a primary search against a large database were used to create a smaller subset database. The second search was performed against a target-decoy version of this subset database merged with a host database. High confidence peptide sequence matches were then used to infer protein identities. Applying our two-step method for both metaproteomic and proteogenomic analysis resulted in twice the number of high confidence peptide sequence matches in each case, as compared to the conventional one-step method. The two-step method captured almost all of the same peptides matched by the one-step method, with a majority of the additional matches being false negatives from the one-step method. Furthermore, the two-step method improved results regardless of the database search program used. Our results show that our two-step method maximizes the peptide matching sensitivity for applications requiring large databases, especially valuable for proteogenomics and metaproteomics studies.
Original language | English (US) |
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Pages (from-to) | 1352-1357 |
Number of pages | 6 |
Journal | Proteomics |
Volume | 13 |
Issue number | 8 |
DOIs | |
State | Published - Apr 2013 |
Keywords
- Bioinformatics
- Mass spectrometry
- Metaproteomics
- Proteogenomics
- Sequence database search
- Two-step workflow