FIT: Tag based method for fusion proteins identification

TL;DR

The paper introduces FIT, a sequence tag-based algorithm that improves the identification of fusion proteins in mass spectrometry data by combining de novo sequencing and peptide-spectrum matching, achieving high sensitivity and low false positives.

Contribution

The novel FIT algorithm effectively detects fusion proteins in proteomic datasets using a combined approach of sequence tags and peptide-spectrum matching.

Findings

High sensitivity in simulated datasets

Low false positive rates

Effective fusion protein identification

Abstract

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.