@Article{riboreport2022,
    author = {Gelhausen, Rick and M\"uller, Teresa and Svensson, Sarah L and Alkhnbashi, Omer S and Sharma, Cynthia M and Eggenhofer, Florian and Backofen, Rolf},
    title = {RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria},
    journal = {Briefings in Bioinformatics},
    year = {2022},
    month = {01},
    abstract = {Small proteins encoded by short open reading frames (ORFs) with 50 codons or fewer are emerging as an important class of cellular macromolecules in diverse organisms. However, they often evade detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation in genomic regions previously thought to be non-coding, driving the development of ORF detection tools using Ribo-seq data. However, only a handful of tools have been designed for bacteria, and these have not yet been systematically compared. Here, we aimed to identify tools that use Ribo-seq data to correctly determine the translational status of annotated bacterial ORFs and also discover novel translated regions with high sensitivity. To this end, we generated a large set of annotated ORFs from four diverse bacterial organisms, manually labeled for their translation status based on Ribo-seq data, which are available for future benchmarking studies. This set was used to investigate the predictive performance of seven Ribo-seq-based ORF detection tools (REPARATION\_blast, DeepRibo, Ribo-TISH, PRICE, smORFer, ribotricer and SPECtre), as well as IRSOM, which uses coding potential and RNA-seq coverage only. DeepRibo and REPARATION\_blast robustly predicted translated ORFs, including sORFs, with no significant difference for ORFs in close proximity to other genes versus stand-alone genes. However, no tool predicted a set of novel, experimentally verified sORFs with high sensitivity. Start codon predictions with smORFer show the value of initiation site profiling data to further improve the sensitivity of ORF prediction tools in bacteria. Overall, we find that bacterial tools perform well for sORF detection, although there is potential for improving their performance, applicability, usability and reproducibility.},
    issn = {1477-4054},
    doi = {10.1093/bib/bbab549},
    url = {https://doi.org/10.1093/bib/bbab549},
    note = {bbab549},
    eprint = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab549/42213041/bbab549.pdf},
    user = {gelhausr}
}