ArticleBioinformatics (Oxford, England)2023
ScanNeo2: a comprehensive workflow for neoantigen detection and immunogenicity prediction from diverse genomic and transcriptomic alterations.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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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.
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Who cites it
9 citing papers in PubMed, 9 citations in OpenAlex.
- Analysis of isoform complexity in pan-transcriptome graphs with atroplex.bioRxiv : the preprint server for biology · 2026Article
- ImmunoPepper: extracting personalized peptides from complex splicing graphs.Bioinformatics (Oxford, England) · 2026Article
- Beyond counting: how single-cell long-read sequencing turns transcriptome complexity into precision targets.Frontiers in oncology · 2026Review
- Review
- Engineering Anti-Tumor Immunity: An Immunological Framework for mRNA Cancer Vaccines.Vaccines · 2025Review
- Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources.Genomics, proteomics & bioinformatics · 2025Review
- A comprehensive benchmark of tools for efficient genomic interval querying.Briefings in bioinformatics · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- A Comprehensive Benchmark of Tools for Efficient Genomic Interval Querying.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationNeoantigens, tumor-specific protein fragments, are invaluable in cancer immunotherapy due to their ability to serve as targets for the immune system. Computational prediction of these neoantigens from sequencing data often requires multiple algorithms and sophisticated workflows, which are currently restricted to specific types of variants, such as single-nucleotide variants or insertions/deletions. Nevertheless, other sources of neoantigens are often overlooked.
resultsWe introduce ScanNeo2 an improved and fully automated bioinformatics pipeline designed for high-throughput neoantigen prediction from raw sequencing data. Unlike its predecessor, ScanNeo2 integrates multiple sources of somatic variants, including canonical- and exitron-splicing, gene fusion events, and various somatic variants. Our benchmark results demonstrate that ScanNeo2 accurately identifies neoantigens, providing a comprehensive and more efficient solution for neoantigen prediction. AVAILABILITY AND IMPLEMENTATION: ScanNeo2 is freely available at https://github.com/ylab-hi/ScanNeo2/ and is accompanied by instruction and application data.
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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.