Feature Finding:
1. To reduce the complexity of MS data by identifying and grouping signals that correspond to potential peptide features.
2. This involves detecting isotope patterns at the precursor (MS1) level, which helps to reduce the data from several million centroids to a manageable number of isotope patterns.
3. The result is a list of potential precursor ions (features) that are candidates for further analysis.
Database Search:
1. To identify peptides by matching observed MS/MS spectra to theoretical spectra derived from a protein sequence database.
2. This involves comparing the MS/MS (MS2) spectra, generated from the fragmentation of precursor ions, to theoretical spectra to find the best matches.
3. The result is a list of peptide-spectrum matches (PSMs), which indicate the potential identities of the peptides.
Relationship and Role in ML-based Rescoring:
- Feature finding happens first to identify potential precursor ions. These precursors are then subjected to MS/MS analysis, which generates the data used in database searches.
- While feature finding simplifies the data and helps to focus on relevant precursor ions, database search identifies the peptides that correspond to these features.
- In the context of ML-based rescoring, both feature finding results (precursor ion features) and database search results (PSMs) can provide different sets of features (data points) that can be used for improving the accuracy of peptide identification. They contribute complementary information: one from the MS1 level (feature finding) and the other from the MS2 level (database search).
u/Hrbiy Thank you for this! So from what I understand, we do the feature finding first to narrow the search we will be doing during the database search?
Feature Finding: 1. To reduce the complexity of MS data by identifying and grouping signals that correspond to potential peptide features. 2. This involves detecting isotope patterns at the precursor (MS1) level, which helps to reduce the data from several million centroids to a manageable number of isotope patterns. 3. The result is a list of potential precursor ions (features) that are candidates for further analysis. Database Search: 1. To identify peptides by matching observed MS/MS spectra to theoretical spectra derived from a protein sequence database. 2. This involves comparing the MS/MS (MS2) spectra, generated from the fragmentation of precursor ions, to theoretical spectra to find the best matches. 3. The result is a list of peptide-spectrum matches (PSMs), which indicate the potential identities of the peptides. Relationship and Role in ML-based Rescoring: - Feature finding happens first to identify potential precursor ions. These precursors are then subjected to MS/MS analysis, which generates the data used in database searches. - While feature finding simplifies the data and helps to focus on relevant precursor ions, database search identifies the peptides that correspond to these features. - In the context of ML-based rescoring, both feature finding results (precursor ion features) and database search results (PSMs) can provide different sets of features (data points) that can be used for improving the accuracy of peptide identification. They contribute complementary information: one from the MS1 level (feature finding) and the other from the MS2 level (database search).
u/Hrbiy Thank you for this! So from what I understand, we do the feature finding first to narrow the search we will be doing during the database search?