LRP2: a proteogenomics pipeline for long-read informed protein isoform analysis and discovery

Abstract

Summary: Most human genes produce multiple RNA isoforms, yet it remains unclear which isoforms are translated into stable, functional proteins. Long-read RNA sequencing resolves full-length transcript structures and, when paired with mass spectrometry, can provide empirical evidence of isoform translation. Despite this opportunity, comprehensive workflows integrating isoform discovery, open reading frame prediction, peptide identification, and protein inference remain limited, leaving users to handle these steps piecemeal. Here, we present LRP2, a modular, end-to-end long-read proteogenomics pipeline built in Nextflow. LRP2 scales transcript discovery to hundreds of samples via PacBio’s latest Isocall tool, removes technical artifacts with SQANTI QC, generates and classifies predicted proteomes via CPAT and SQANTI Protein, performs multi-group differential expression and usage analysis via edgeR, DRIMSeq, and a long-read adaptation of LeafCutter, and integrates protein-level evidence from DDA and DIA MS data through FragPipe. For cross-dataset comparison of novel isoforms, LRP2 employs deterministic splice junction, coordinate-based isoform identifiers. Used as an integrated pipeline, LRP2 enables the detection of novel peptides and improves protein isoform inference to confirm protein isoform translation.

Availability and implementation: LRP2 is freely available as a modular Nextflow pipeline at https://github.com/sheynkman-lab/LRP2 (v2.0.0, archived at https://doi.org/10.5281/zenodo.22795865). LRP2 supports Docker, Apptainer/Singularity, and Conda environments with GENCODE references.

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Cite

BibTeX RIS

BibTeX

@article{schertzer2026lrp2,
  title = {{LRP2: a proteogenomics pipeline for long-read informed protein isoform analysis and discovery}},
  author = {Megan D Schertzer and Julia T Lewandowski and Emily F Watts and Will Rosenow and Madison M Mehlferber and Erin D Jeffery and Scott I Adamson and Jocelyne Bruand and Elizabeth Tseng and Yaseswini Neelamraju and Francine E Garrett-Bakelman and Egor Dolzhenko and David A Knowles and Gloria Sheynkman},
  journal = {Bioinformatics},
  year = {2026},
  month = oct,
  doi = {10.1093/bioinformatics/btag790},
  url = {https://doi.org/10.1093/bioinformatics/btag790},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - LRP2: a proteogenomics pipeline for long-read informed protein isoform analysis and discovery
AU  - Megan D Schertzer
AU  - Julia T Lewandowski
AU  - Emily F Watts
AU  - Will Rosenow
AU  - Madison M Mehlferber
AU  - Erin D Jeffery
AU  - Scott I Adamson
AU  - Jocelyne Bruand
AU  - Elizabeth Tseng
AU  - Yaseswini Neelamraju
AU  - Francine E Garrett-Bakelman
AU  - Egor Dolzhenko
AU  - David A Knowles
AU  - Gloria Sheynkman
PY  - 2026
DA  - 2026/10/22
JO  - Bioinformatics
DO  - 10.1093/bioinformatics/btag790
UR  - https://doi.org/10.1093/bioinformatics/btag790
AB  - Summary: Most human genes produce multiple RNA isoforms, yet it remains unclear which isoforms are translated into stable, functional proteins. Long-read RNA sequencing resolves full-length transcript structures and, when paired with mass spectrometry, can provide empirical evidence of isoform translation. Despite this opportunity, comprehensive workflows integrating isoform discovery, open reading frame prediction, peptide identification, and protein inference remain limited, leaving users to handle these steps piecemeal. Here, we present LRP2, a modular, end-to-end long-read proteogenomics pipeline built in Nextflow. LRP2 scales transcript discovery to hundreds of samples via PacBio’s latest Isocall tool, removes technical artifacts with SQANTI QC, generates and classifies predicted proteomes via CPAT and SQANTI Protein, performs multi-group differential expression and usage analysis via edgeR, DRIMSeq, and a long-read adaptation of LeafCutter, and integrates protein-level evidence from DDA and DIA MS data through FragPipe. For cross-dataset comparison of novel isoforms, LRP2 employs deterministic splice junction, coordinate-based isoform identifiers. Used as an integrated pipeline, LRP2 enables the detection of novel peptides and improves protein isoform inference to confirm protein isoform translation.  Availability and implementation: LRP2 is freely available as a modular Nextflow pipeline at https://github.com/sheynkman-lab/LRP2 (v2.0.0, archived at https://doi.org/10.5281/zenodo.22795865). LRP2 supports Docker, Apptainer/Singularity, and Conda environments with GENCODE references.
SN  - 1367-4803
SN  - 1367-4811
ER  -

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