MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search

Spectral library
search can enable more sensitive peptide identification
in tandem mass spectrometry experiments. However, its drawbacks are
the limited availability of high-quality libraries and the added difficulty
of creating decoy spectra for result validation. We describe MS Ana,
a new spectral library search engine that enables high sensitivity
peptide identification using either curated or predicted spectral
libraries as well as robust false discovery control through its own
decoy library generation algorithm. MS Ana identifies on average 36%
more spectrum matches and 4% more proteins than database search in
a benchmark test on single-shot human cell-line data. Further, we
demonstrate the quality of the result validation with tests on synthetic
peptide pools and show the importance of library selection through
a comparison of library search performance with different configurations
of publicly available human spectral libraries.