{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T07:44:43Z","timestamp":1791963883756,"version":"4.3.4"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fund of Northwest A&F University","award":["Z111021404"],"award-info":[{"award-number":["Z111021404"]}],"id":[{"id":"https:\/\/ror.org\/0051rme32","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Shaanxi Province of China","award":["A289021612"],"award-info":[{"award-number":["A289021612"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270608"],"award-info":[{"award-number":["32270608"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>By mapping ribosome-protected fragments genome-wide, ribosome profiling (Ribo-seq) has uncovered extensive translation beyond conventional coding sequences, revealing non-canonical ORFs (ncORFs) with roles in diverse biological processes. However, robust ncORF identification remains challenging when Ribo-seq datasets exhibit attenuated periodicity. Protocol-induced biases during library construction is an important contributor to this loss, yet most ORF callers do not explicitly model these distortions. Here, we present RiboBA, a bias-aware probabilistic framework for robust ORF identification from Ribo-seq data. RiboBA combines a generative module that jointly infers protocol-induced biases and codon-level ribosome occupancy with a supervised module that identifies translated ORFs and initiation sites from the bias-adjusted profiles. In simulations with known P-site positions, RiboBA improved exact nucleotide- and codon-level P-site assignment relative to a conventional fixed-offset approach. Across simulations and diverse empirical Ribo-seq datasets, together with orthogonal immunopeptidomics evidence, RiboBA accurately recovers protocol-induced parameters and improves ncORF identification, with pronounced advantages for RNase I libraries with attenuated periodicity, and for MNase and nuclease P1 datasets, while maintaining competitive runtimes. In a Drosophila case study, RiboBA identified candidate ncORFs with coding potential, including recurrent upstream translation in ThrRS and Mettl2, linking ncORF translation to threonine-related translational regulation.<\/jats:p>","DOI":"10.1093\/nar\/gkag969","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:24:48Z","timestamp":1790594688000},"source":"Crossref","is-referenced-by-count":0,"title":["RiboBA: a bias-aware probabilistic framework for robust ORF identification across diverse ribosome profiling protocols"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7761-9854","authenticated-orcid":false,"given":"Junyu","family":"Bai","sequence":"first","affiliation":[{"name":"College of Life Sciences, Northwest A&F University , Yangling, Shaanxi 712100 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6241-6317","authenticated-orcid":false,"given":"Ruolin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Northwest A&F University , Yangling, Shaanxi 712100 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,14]]},"reference":[{"key":"2026101402473577800_B1","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1126\/science.1168978","article-title":"Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling","volume":"324","author":"Ingolia","year":"2009","journal-title":"Science"},{"key":"2026101402473577800_B2","doi-asserted-by":"publisher","first-page":"1534","DOI":"10.1038\/nprot.2012.086","article-title":"The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of 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RiboBA_analysis. Software release. Zenodo. 2026."},{"key":"2026101402473577800_B52","unstructured":"Bai J, Yang R. RiboBA figure-ready data for main and supplementary figures. Dataset. Zenodo. 2026."},{"key":"2026101402473577800_B53","unstructured":"Bai J . RiboBA. Software release. 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