Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Discovery of variant
peptides such as a single amino acid variant
(SAAV) in shotgun proteomics data is essential for personalized proteomics.
Both the resolution of shotgun proteomics methods and the search engines
have improved dramatically, allowing for confident identification
of SAAV peptides. However, it is not yet known if these methods are
truly successful in accurately identifying SAAV peptides without prior
genomic information in the search database. We studied this in unprecedented
detail by exploiting publicly available long-read RNA sequences and
shotgun proteomics data from the gold standard reference cell line
NA12878. Searching spectra from this cell line with the state-of-the-art
open modification search engine ionbot against carefully
curated search databases resulted in 96.7% false-positive SAAVs and
an 85% lower true positive rate than searching with peptide search
databases that incorporate prior genetic information. While adding
genetic variants to the search database remains indispensable for
correct peptide identification, inclusion of long-read RNA sequences
in the search database contributes only 0.3% new peptide identifications.
These findings reveal the differences in SAAV detection that result
from various approaches, providing guidance to researchers studying
SAAV peptides and developers of peptide spectrum identification tools.