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<p>Proteogenomics is limited by the challenge of modeling the complexities of gene expression. We create moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization.</p>

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Identification of non-canonical peptides with moPepGen

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Chenghao Zhu,
  • Lydia Y. Liu,
  • Annie Ha,
  • Takafumi N. Yamaguchi,
  • Helen Zhu,
  • Rupert Hugh-White,
  • Julie Livingstone,
  • Yash Patel,
  • Thomas Kislinger,
  • Paul C. Boutros

摘要

Proteogenomics is limited by the challenge of modeling the complexities of gene expression. We create moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization.