Robust ORF Deconvolution from Multiple Ribo-seq Datasets with Group LASSO Penalty
- 1University of Regensburg
Abstract
Ribo-seq has provided compelling evidence that there are many translated but unannotated ORFs. Despite a growing body of peptidomic support for cryptic ORFs, the computational discovery of weakly translated, short and mutually overlapping ORFs has remained a major bottleneck. Here we introduce PRICE 2, a method designed to resolve these challenging translation events by adapting concepts from isoform deconvolution. PRICE 2 employs a generative model combined with group LASSO regularized regression to jointly infer active ORFs across multiple sequencing runs. In a comprehensive benchmark, PRICE 2 outperformed thirteen state-of-the-art tools in detecting short and overlapping ORFs. Orthogonal validation via immunopeptidomics demonstrated that ORFs identified by PRICE 2 enable the discovery of [~]40% more cryptic MHC-I peptides than the best-performing competing tool at a fixed false discovery rate. By analyzing public ribo-seq data across 45 distinct cell and tissue types, we constructed ORF-stock, a comprehensive database of non-canonical ORFs for immunopeptidomic studies.
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BibTeX
@misc{maidhof2026robust,
title = {{Robust ORF Deconvolution from Multiple Ribo-seq Datasets with Group LASSO Penalty}},
author = {K. Maidhof and A. Schlosser and F. Erhard},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.755973},
url = {https://www.biorxiv.org/content/10.64898/2026.10.01.755973v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Robust ORF Deconvolution from Multiple Ribo-seq Datasets with Group LASSO Penalty
AU - K. Maidhof
AU - A. Schlosser
AU - F. Erhard
PY - 2026
DA - 2026/10/01
PB - bioRxiv
DO - 10.64898/2026.10.01.755973
UR - https://www.biorxiv.org/content/10.64898/2026.10.01.755973v1
AB - Ribo-seq has provided compelling evidence that there are many translated but unannotated ORFs. Despite a growing body of peptidomic support for cryptic ORFs, the computational discovery of weakly translated, short and mutually overlapping ORFs has remained a major bottleneck. Here we introduce PRICE 2, a method designed to resolve these challenging translation events by adapting concepts from isoform deconvolution. PRICE 2 employs a generative model combined with group LASSO regularized regression to jointly infer active ORFs across multiple sequencing runs. In a comprehensive benchmark, PRICE 2 outperformed thirteen state-of-the-art tools in detecting short and overlapping ORFs. Orthogonal validation via immunopeptidomics demonstrated that ORFs identified by PRICE 2 enable the discovery of [~]40% more cryptic MHC-I peptides than the best-performing competing tool at a fixed false discovery rate. By analyzing public ribo-seq data across 45 distinct cell and tissue types, we constructed ORF-stock, a comprehensive database of non-canonical ORFs for immunopeptidomic studies.
ER -