ArticleBioinformatics (Oxford, England)2020
Neoantimon: a multifunctional R package for identification of tumor-specific neoantigens.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Proteogenomic detection of tumor-specific somatic mutant proteins in urinary extracellular vesicles for non-invasive monitoring of bladder cancer.Frontiers in oncology · 2026Article
- The difference in tumor immune microenvironment and IFN-γ response between solid and non-solid adenocarcinoma.Translational lung cancer research · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Comprehensive analysis of prognosis markers with molecular features derived from pan-cancer whole-genome sequences.Human genomics · 2025Article
- Unraveling the power of NAP-CNB's machine learning-enhanced tumor neoantigen prediction.eLife · 2025Article
- NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.Bioinformatics (Oxford, England) · 2024Article
- Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors.Genome biology · 2023Article
- Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis.Frontiers in immunology · 2023Article
- Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction.International journal of molecular sciences · 2022Article
- New evaluation of the tumor immune microenvironment of non-small cell lung cancer and its association with prognosis.Journal for immunotherapy of cancer · 2022Article
- Computational cancer neoantigen prediction: current status and recent advances.Immuno-oncology technology · 2021Review
- Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool.Scientific reports · 2021Article
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summaryIt is known that some mutant peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T cells on the surface of a tumor cell. These peptides are termed neoantigen, and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package termed Neoantimon that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions and structural variants. Beyond the existing applications, Neoantimon is capable of attaching and reflecting several additional information, e.g. wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information and combinations of mutations to filter out infeasible peptides as neoantigen. AVAILABILITY AND IMPLEMENTATION: The R package is available at http://github/hase62/Neoantimon.
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