Proteogenomics requires deep and broad sampling of the proteome in order to generate sufficient numbers of unique peptides.
For each protein the number of unique peptides and the total peptide count, which includes multiple observations of the same peptide, are listed.
After removing duplicate peptides, i.e., the same peptide eluted from more than one cell line, the total number of unique peptides was 2740.
The identified proteins were then ranked according to the number of unique peptides detected in each case.
Specifically, we generated: Datasets of unique peptides MHCPEP-UPDS, MHCBN-UPDS, and IEDB-UPDS extracted from MHCPEP, MHCBN, and IEDB, respectively.
In each case, the estimated performance of the prediction methods evaluated on similarity-reduced datasets is substantially worse than that estimated using the datasets of unique peptides.
Bee samples were identified with the virus/bacterium/fungi proteome based on the number of unique peptides that remained after removal of degenerate peptides from the binary matrix.
The results are summarized in Table 1, which lists the number of unique peptides identified for each protein in the different experiments.
For the peptides that had replications, their normalized values were averaged to produce a list of unique peptides with their normalized values for each slide.
Quantification is performed exclusively through the use of unique peptides, thus eliminating peptides matching more than one protein or common to different isoforms.
Several studies [5], [15] [17], [19] have reported the performance of MHC-II binding peptide prediction methods using datasets of unique peptides.
