OpenCustomDB: Integration of Unannotated Open Reading Frames and Genetic Variants to Generate More Comprehensive Customized Protein Databases

Proteomic diversity
in biological samples can be characterized
by mass spectrometry (MS)-based proteomics using customized protein
databases generated from sets of transcripts previously detected by
RNA-seq. This diversity has only been increased by the recent discovery
that many translated alternative open reading frames rest unannotated
at unsuspected locations of mRNAs and ncRNAs. These novel protein
products, termed alternative proteins, have been left out of all previous
custom database generation tools. Consequently, genetic variations
that impact alternative open reading frames and variant peptides from
their translated proteins are not detectable with current computational
workflows. To fill this gap, we present OpenCustomDB, a bioinformatics
tool that uses sample-specific RNaseq data to identify genomic variants
in canonical and alternative open reading frames, allowing for more
than one coding region per transcript. In a test reanalysis of a cohort
of 16 patients with acute myeloid leukemia, 5666 peptides from alternative
proteins were detected, including 201 variant peptides. We also observed
that a significant fraction of peptide-spectrum matches previously
assigned to peptides from canonical proteins got better scores when
reassigned to peptides from alternative proteins. Custom protein libraries
that include sample-specific sequence variations of all possible open
reading frames are promising contributions to the development of proteomics
and precision medicine. The raw and processed proteomics data presented
in this study can be found in PRIDE repository with accession number
PXD029240.