  There is increased interest in the identification and analysis of gene
fusions and chimeric RNA transcripts. While most recent efforts focused on the
analysis of genomic and transcriptomic data, identi-fication of novel peptides
corresponding to such events in mass spectrometry-based proteomic datasets
would provide complemen-tary, protein-level evidence. The process of
identifying fusion pro-teins from mass spectrometry data is inherently
difficult because such events are rare. It is also complicated due to large
amount of spectra collected and the explosion in the number of candidate
peptide sequences that need to be considered, which makes ex-haustive search
for all possible fusion partner proteins impractical. In this work, we present
a sequence tag based fusion protein identi-fication algorithm, FIT, that
combines the virtue of both de novo sequence tag retrieval and peptide-spectrum
matching for identifi-cation of fusion proteins. Results on simulated datasets
show high sensitivity and low false positive rates for fusion protein
identifica-tion by the FIT algorithm.
