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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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SVNeoPP: A Workflow for Structural-Variant-Derived Neoantigen Prediction and Prioritization Using Multi-Omics Data.

Wanyang An1,2, Xiaoxiu Tan2, Zhenhao Liu2

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Biology
|March 27, 2026
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Summary
This summary is machine-generated.

This study introduces SVNeoPP, a novel computational workflow for identifying tumor neoantigens from structural variants (SVs). SVNeoPP expands the search for cancer neoantigens, improving personalized vaccine and T-cell therapy development.

Keywords:
Snakemakemulti-omicsneoantigensprediction workflowstructural variants

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Area of Science:

  • * Oncology
  • * Bioinformatics
  • * Immunology

Background:

  • * Tumor neoantigens are crucial for personalized cancer vaccines and T-cell therapies.
  • * Current methods primarily focus on single nucleotide variants (SNVs) and small indels, yielding limited neoantigen candidates.
  • * Structural variants (SVs) are prevalent in tumors and can generate novel neoantigens, but their prediction is challenging due to technical hurdles.

Purpose of the Study:

  • * To develop an end-to-end computational workflow, SVNeoPP, for predicting and prioritizing neoantigens derived from structural variants (SVs).
  • * To address challenges in SV-derived neoantigen prediction, including breakpoint uncertainty and isoform-dependent coding inference.
  • * To integrate multi-dimensional evidence for robust neoantigen identification and facilitate downstream therapeutic applications.

Main Methods:

  • * Developed SVNeoPP, a Snakemake-based workflow integrating whole genome sequencing (WGS) and RNA-sequencing (RNA-seq) data.
  • * Implemented SV calling, annotation, and reconstruction of altered transcripts and coding sequences in an isoform-aware manner.
  • * Employed hierarchical filtering and prioritization based on antigen-processing features, HLA binding, transcript expression, proteomics, immunogenicity, and similarity to validated neoantigen databases.

Main Results:

  • * Demonstrated SVNeoPP's performance using a hepatocellular carcinoma (HCC) multi-omics dataset, generating a high-priority shortlist of candidate peptides.
  • * Showcased SVNeoPP's ability to significantly expand the search space for SV-derived neoantigens compared to existing methods.
  • * Observed more favorable antigen-processing and HLA binding feature distributions for SVNeoPP-identified candidates.

Conclusions:

  • * SVNeoPP provides a reusable, traceable, and interpretable framework for multi-dimensional evidence-driven SV-derived neoantigen analysis.
  • * This workflow complements SNV/small-indel pipelines, broadening the neoantigen repertoire.
  • * Generates ranked neoantigen candidates with interpretable evidence to aid downstream prioritization and clinical decision-making.