The Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Abstract

Discovery of variant peptides such as single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA seq and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

Deep Time Paleoproteomics: Looking Forward

“…60 We argue that this is an all but impossible threshold to meet for bioinformatics analyses that rely on matching experimental data to a database of already known sequencesa process that biases results toward conserved peptides and can fail to identify novel sequences even when they are present. 61 Additionally, confident identification of peptides with even a single amino acid variant from the search database remains quite difficult, 61 even in modern tissues, 71 and would likely be more difficult to assess for taxa in deep time. The use of more advanced peptide spectral match (PSM) quality metrics than a basic 1% false discovery rate (FDR) (e.g., posterior error probability [PEP] or more strict FDR values) may be necessary for variant peptides or rare peptides to support their detection when species-specific peptides are not found.…”

“…As a result, new bioinformatic tools or add-ons to current tools will be necessary to maximize detection and confidence in identification of sequences that vary from search databases. 61 The difficulties in detecting and controlling false discovery of these variants 71 makes finding changes specific to extinct species very difficult, but the ability to do so is a crucial goal of paleoproteomics as a discipline. Thus, although such variations affect all proteomic disciplines that rely on database searching, the field of paleoproteomics must take a central role in overcoming these challenges.…”

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

“…For these incorrect interpretations one could expect bimodal distributions that contain a combination of right and wrongly assigned mass shifts, but the error distributions are mostly unimodal. The observation that presumed mutations are among the most problematic corresponds to the known non-trivial nature of reliably identifying such sequence changes 26,30 .…”

Identification of Protein Isoforms Using Reference Databases Built from Long and Short Read RNA-Sequencing

“…44−46 Cell-or tissue-specific databases built from long RNA-seq read data will be of particular use, helping to detect sample-specific peptides and thus protein isoforms. 47 Custom protein sequence databases built from RNA-seq data typically contain more sequences than traditional reference databases due to the translation of assembled and variant transcripts in more than one frame. 9 However, increasing the number of candidate protein sequences can affect the statistical validity of peptide matches.…”