RiboBA: a bias-aware probabilistic framework for robust ORF identification across diverse ribosome profiling protocols.

RiboBA enhances ncORF identification in Ribo-seq data by accurately modeling protocol-induced biases, especially in datasets with attenuated periodicity.

• Why it matters: Accurate detection of non-canonical ORFs is hindered by protocol biases and loss of periodicity in Ribo-seq datasets, limiting understanding of translation beyond canonical coding regions.
• What they did: The authors developed RiboBA, a probabilistic framework combining bias inference and ORF identification, tested through simulations and empirical datasets, including diverse library types and a Drosophila case study.
• The result: RiboBA improves P-site assignment and ncORF detection, notably in RNase I, MNase, and P1 datasets, and reveals candidate ncORFs linked to translational regulation, enabling more robust translation analysis.