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Detecting actively translated open reading frames in ribosome profiling data


RNA-sequencing protocols can quantify gene expression regulation from transcription to protein synthesis. Ribosome profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. We have developed RiboTaper (available at, a rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq data. We used RiboTaper with deep Ribo-seq data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORFs from Ribo-seq data.

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Figure 1: The RiboTaper workflow.
Figure 2: De novo ORF reconstruction and examples of detected ORFs.
Figure 3: Comparative analysis of translated ORFs from coding and noncoding regions.
Figure 4: Conserved and nonconserved RiboTaper-identified ORFs define the cellular proteome.

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L.C. sincerely thanks R. Marangoni (University of Pisa) for inspiring and fruitful discussions and A. Munteanu (MDC) for help with the supplementary figures. L.C. is funded by the MDC PhD program. B.O. acknowledges funding through a Delbrück fellowship at the MDC. N.M. acknowledges funding from EU Marie Curie IIF. N.M. and U.O. were supported by US National Institutes of Health grant R01GM104962.

Author information

Authors and Affiliations



L.C. and U.O. developed the computational approach. L.C. implemented the RiboTaper method and analyzed the sequencing data, supervised by U.O. E.W., N.M. and A.H. performed the Ribo-seq experiments, supervised by M.L. B.O. carried out the evolutionary-conservation analysis and helped with the interpretation of the presented findings. H.Z., M.S. and L.C. analyzed the mass spectrometry data. L.C., N.M. and U.O. wrote the manuscript, with crucial input from all other authors.

Corresponding author

Correspondence to Uwe Ohler.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17 (PDF 6623 kb)

Supplementary Table 1

Statistics about alignment and pre-processing of the sequencing datasets used in this study. (XLSX 5 kb)

Supplementary Table 2

Complete list of identified ORFs in the different datasets used. (XLSX 31374 kb)

Supplementary Table 3

Filtered list of identified ORFs in the different datasets used. Non-coding ORFs overlapping CDS regions were removed. Additionally, ORFs were filtered out when >30% of the Ribo-seq coverage were supported by multi-mapping reads only. (XLSX 29505 kb)

Supplementary Table 4

Evidence table for peptides identified by the RiboTaper database only. (XLSX 3115 kb)

Supplementary Data

Archive containing bed files for the identified ORFs. (ZIP 8432 kb)

Supplementary Software

RiboTaper (version 1.0) software code. (ZIP 69 kb)

Source data

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Calviello, L., Mukherjee, N., Wyler, E. et al. Detecting actively translated open reading frames in ribosome profiling data. Nat Methods 13, 165–170 (2016).

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