ArticleNature communications2020
Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
80 citing papers in PubMed.
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Review
- PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- pVACtools v6: A comprehensive suite for neoantigen prediction, visualization, and therapy design.ArXiv · 2026Article
- iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.Analytical chemistry · 2026Article
- Article
- Prioritizing peptides for targeted mass spectrometry experiments using deep learning.bioRxiv : the preprint server for biology · 2026Article
- Identification of non-canonical peptides with moPepGen.Nature biotechnology · 2026Article
- Review
- iPepGen: a modular, immunopeptidogenomic analysis pipeline for discovery, verification, and prioritization of cancer peptide neoantigen candidates.Genome biology · 2026Article
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.Journal of proteome research · 2026Article
- Assessing the Performance of Mass Spectrometry Search Strategies in Identifying Translational Errors Using PDX Proteomics Data.Molecular & cellular proteomics : MCP · 2026Article
- Predominant mutated non-canonical tumor-specific antigens identified by proteogenomics demonstrate immunogenicity and tumor suppression in CRC.Cell genomics · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Computational neoantigen prediction for cancer immunotherapy.Genes and immunity · 2025Review
- Proteomics Data Imputation With a Deep Model That Learns From Many Datasets.Molecular & cellular proteomics : MCP · 2025Article
- Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.Nature communications · 2025Article
- Artificial Intelligence and the Evolving Landscape of Immunopeptidomics.Proteomics. Clinical applications · 2025Review
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.