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

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.