moPepGen, a new open-source algorithm from teams at UCLA and the University of Toronto, gives proteogenomic studies a boost by detecting protein variants that earlier pipelines missed. Writing in Nature Biotechnology, the developers report that their graph-based workflow tracks many kinds of DNA and RNA change, including single-nucleotide variants, gene fusions, alternative splicing, circular RNAs, and RNA editing, and turns them into expected peptide sequences.
“We developed moPepGen to help researchers determine which genetic variants are truly expressed at the protein level, addressing a long-standing challenge in the proteogenomic community,” said Chenghao Zhu, postdoctoral scholar at UCLA and the study’s co-first author. "Our tool significantly improves the detection of hidden protein variations by using a graph-based approach to efficiently process all types of genetic changes. This provides a more comprehensive view of protein diversity and gives researchers a much more accurate picture of how mutations influence disease."
The group tested the tool on five prostate tumors, eight kidney tumors, and 376 cell lines. moPepGen found four times more distinct variant peptides than common workflows, including peptides formed by complex splicing events. Paul Boutros, a senior author, noted, “It bridges the gap between genetic data and real-world protein expression, unlocking new possibilities in precision medicine and beyond.”
The extra coverage matters to immuno-oncology. By flagging cancer-specific variant peptides, moPepGen identifies possible neoantigen candidates for personalized vaccines or engineered T cells. Because the software accepts standard genomics and proteomics inputs, it can fold into existing mass-spectrometry pipelines without extra hardware or fees.
The authors have released the code and documentation under a permissive license, inviting labs to test the approach on solid tumors, neurologic disorders, or any setting where variant peptides may influence disease biology.
Publication Details
Zhu, C., Liu, L.Y., Ha, A. et al. Identification of non-canonical peptides with moPepGen. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02701-0
“We developed moPepGen to help researchers determine which genetic variants are truly expressed at the protein level, addressing a long-standing challenge in the proteogenomic community,” said Chenghao Zhu, postdoctoral scholar at UCLA and the study’s co-first author. "Our tool significantly improves the detection of hidden protein variations by using a graph-based approach to efficiently process all types of genetic changes. This provides a more comprehensive view of protein diversity and gives researchers a much more accurate picture of how mutations influence disease."
The group tested the tool on five prostate tumors, eight kidney tumors, and 376 cell lines. moPepGen found four times more distinct variant peptides than common workflows, including peptides formed by complex splicing events. Paul Boutros, a senior author, noted, “It bridges the gap between genetic data and real-world protein expression, unlocking new possibilities in precision medicine and beyond.”
The extra coverage matters to immuno-oncology. By flagging cancer-specific variant peptides, moPepGen identifies possible neoantigen candidates for personalized vaccines or engineered T cells. Because the software accepts standard genomics and proteomics inputs, it can fold into existing mass-spectrometry pipelines without extra hardware or fees.
The authors have released the code and documentation under a permissive license, inviting labs to test the approach on solid tumors, neurologic disorders, or any setting where variant peptides may influence disease biology.
Publication Details
Zhu, C., Liu, L.Y., Ha, A. et al. Identification of non-canonical peptides with moPepGen. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02701-0