Development of novel peptides filtering algorithm based on peptide information

Abstract

Variant novel peptides in cancer samples can provide guidelines and evidence for cancer research. In order to obtain more valuable novel peptides, protein databases need to contain more non-canonical proteins. However, the dramatic increase of database brings high false positive rate of peptides, the identification of novel peptides faces great challenges. We filter peptide results with high false positive rates by combining peptide retention time and peptide fragment ion informations using the prediction tool DeepLC and the validation algorithm SpectrumAI. Both methods are currently integrated into the pypgatk Python package and provide multiple parameters to meet individual needs.