PeptidomicsDB: a unified resource of endogenous peptidomes for peptide discovery and functional exploration across species and biological contexts

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ID: 333612
2026
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Abstract
Abstract Endogenous peptides represent a rich source of bioactive molecules that regulate diverse physiological processes and offer opportunities for functional discovery. Mass spectrometry-based peptidomics enables their systematic characterization, yet public datasets remain fragmented and are often processed using heterogeneous tools and parameters, limiting peptide-level retrieval, cross-study exploration, and data reuse. We developed PeptidomicsDB (freely accessible at http://www.inbirg.com/ppd/), a unified resource based on the standardized reanalysis of 151 public peptidomics projects comprising 3992 biological samples from major branches, including global peptidomics, neuropeptidomics, and immunopeptidomics, across species and biological contexts. Using the DeepNovo Peptidome workflow implemented in PEAKS Studio 13.0, which integrates database searching, homology searching, and de novo sequencing, we identified >33 million peptide records representing over 4 million unique peptide sequences, of which >400 000 showed sequence similarity to bioactive peptides curated in public repositories. Analysis of 249 curated pairwise comparison datasets identified 78 910 differentially abundant peptide records, while 14 context-specific datasets yielded 148 175 peptide records enriched in individual tissues, diseases, or cell types. PeptidomicsDB links peptide, precursor-protein, sample, and dataset levels with experimental provenance, quantitative evidence, mass spectrometric data, and functional annotations. Together, these capabilities support peptide discovery and functional exploration.
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openalex_W7223376548 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fengyuan Yang, Yichen Guo, Peiqi Luo, Yucheng Wu, Yapeng Li, Zishun Zhang, Na Ge, Haihan Yuan, Desi Liu, Jianbo Pan
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1032
URL
https://doi.org/10.1093/nar/gkag1032
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