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

Abstract

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.

BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens

“…However, these approaches suffer from their dedication as the identified MAPs could also derive from other transcripts absent from these databases. Accordingly, based on evidence showing that greater RNA expression confers a greater probability of MAP generation [ 7 , 13 ], we implemented a biotype annotation tool in BamQuery and showed that many presumed ncMAPs could be coded with greater probability by regions annotated with different biotypes. Notably, cryptic proteins are translated as efficiently as canonical proteins and generate MAPs fivefold more efficiently per translation event [ 5 ].…”

“…Indeed, these studies revealed that ~ 5–10% of MAPs derive from non-canonical (nc) regions of the genome, such as introns, non-coding RNAs (ncRNA), or endogenous retroelements (EREs), as well as from out-of-frame exonic translation [ 3 – 6 ]. Furthermore, a recent study showed that a significant fraction of MS peptide-spectrum matches assigned to canonical MAPs have better scores when attributed to ncMAPs, suggesting a greater contribution of the non-canonical regions to the immunopeptidome than previously estimated [ 7 ]. While most of the discovered ncMAPs are non-mutated [ 4 , 8 – 12 ], many of them are found exclusively in cancer cells and attract attention as (1) they can be immunogenic in vitro as well as in vivo; (2) they are more numerous in the immunopeptidome of malignant cells than mutated TAs, and (3) several non-coding TAs are widely shared between cancer patients whereas mutations mainly generate private antigens [ 13 , 14 ].…”

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

“… 166–168 This number could be underestimated as some MS spectra originally assigned to canonical proteins could indeed correspond to non-canonical ones. 169 In addition, the total number of human ncORFs is yet unclear. While conservative estimates consider around 7,000, others find certain evidence for the existence for several hundred thousand.…”

Second Special Issue on Methods for Omics Research: Proteome Research and Beyond

“…Guilloy et al point out the incompleteness of conventional protein sequence databases that only contain translations of canonical open reading frames. They offer a computational solution, OpenCustomDB (), to account for alternative open reading frames using RNA-Seq data specific to each patient sample. Finally, Li et al have developed and made publicly available GlycoSLASH (), a computational pipeline that goes beyond the capabilities of the commercial Byonic software.…”

“…Ryan et al describe the robust optimization of a clinical analytical pipeline to quantify over 1000 lipid species from plasma and serum using internal standards labeled with stable isotopes (SIL-IS) and targeted mass spectrometry . Finally, two of the computational pipelines described above for deep proteomics were applied to patient samples and shown to significantly improve our ability to detect genomic variants and glycosylated biomarkers . Such technical improvements on both the LC-MS side and the computational analysis of the resulting data sets have the potential to support personalized medicine on a large-scale.…”