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Investigating open reading frames in known and novel transcripts using ORFanage

Nature Computational Science volume 3, pages 700–708 (2023)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

ORFanage is a system designed to assign open reading frames (ORFs) to known and novel gene transcripts while maximizing similarity to annotated proteins. The primary intended use of ORFanage is the identification of ORFs in the assembled results of RNA-sequencing experiments, a capability that most transcriptome assembly methods do not have. Our experiments demonstrate how ORFanage can be used to find novel protein variants in RNA-seq datasets, and to improve the annotations of ORFs in tens of thousands of transcript models in the human annotation databases. Through its implementation of a highly accurate and efficient pseudo-alignment algorithm, ORFanage is substantially faster than other ORF annotation methods, enabling its application to very large datasets. When used to analyze transcriptome assemblies, ORFanage can aid in the separation of signal from transcriptional noise and the identification of likely functional transcript variants, ultimately advancing our understanding of biology and medicine.

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Fig. 1: Overview of irregularities in reference database ORF annotation.
Fig. 2: Diagram illustrating the algorithm implemented in ORFanage.
Fig. 3: Novel ORFs in the GTEx dataset inferred using ORFanage.

Data availability

No new sequencing data were created for this study. The sequencing data used in this study are available through the GTEx project (phs000424.v7.p2). GTEx data were first analyzed as part of the CHESS project and the details can be found in the corresponding resources and publications (http://ccb.jhu.edu/chess/). The datasets analyzed in this study are (1) GENCODE annotation build version 41 (https://www.gencodegenes.org/human/release_41.html); (2) RefSeq annotation build 110 (https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Homo_sapiens/110/); (3) MANE joint annotation build version 1.0 (https://ftp.ncbi.nlm.nih.gov/refseq/MANE/MANE_human/); (4) A. thaliana annotation (https://ftp.ncbi.nlm.nih.gov/genomes/refseq/plant/Arabidopsis_thaliana/all_assembly_versions/GCF_000001735.3_TAIR10/); and (5) C. elegans genome annotation (https://ftp.ncbi.nlm.nih.gov/genomes/refseq/invertebrate/Caenorhabditis_elegans/all_assembly_versions/GCF_000002985.6_WBcel235/). Source data are provided with this paper.

Code availability

All code required to reproduce the data generated within the study from public sources is provided at https://github.com/alevar/ORFanage_tests. The core method is implemented in C++ and based on the GFFutils45 and KSW264,65 libraries. The code and test data are available for download at https://github.com/alevar/ORFanage/releases/tag/1.0 (https://doi.org/10.5281/zenodo.8102912)66. Jupyter notebooks used to generate all results described in the manuscript are provided separately at https://github.com/alevar/ORFanage_tests (https://doi.org/10.5281/zenodo.8102918)67. All additional software methods used in this study and their versions and appropriate references are listed in Methods.

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Acknowledgements

This work was supported in part by the US National Institutes of Health under grants nos. R01 HG006677 (S.L.S.), R01 MH123567 (S.L.S.) and R35 GM130151 (S.L.S.) and by the US National Science Foundation under grant no. DBI-1759518 (M.P.). We would also like to thank C. Pockrandt for helpful discussions on phyloCSF++ implementation and usage.

Author information

Authors and Affiliations

  1. Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA

    Ales Varabyou, Beril Erdogdu, Steven L. Salzberg & Mihaela Pertea

  2. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Ales Varabyou, Steven L. Salzberg & Mihaela Pertea

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Beril Erdogdu, Steven L. Salzberg & Mihaela Pertea

  4. Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA

    Steven L. Salzberg

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Contributions

A.V. and B.E. conceived and developed the original idea. A.V. developed and implemented the final method and experiments. A.V., B.E., S.L.S. and M.P. conceptualized the study, methods and wrote the paper.

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Correspondence to Ales Varabyou or Mihaela Pertea.

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Nature Computational Science thanks Liugo Wang and the other anonymous reviewer for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Supplementary Figs. 1–4 and Tables 1 and 2.

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Source Data Fig. 1

Three files with data used to generate Fig. 1c,d,e,g,h. README.md is included to map individual files to panels within figure and to provide descriptions of the columns.

Source Data Fig. 3

Contains a file with data used to generate Fig. 3a. README.md is included with a detailed description of the contents.

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Varabyou, A., Erdogdu, B., Salzberg, S.L. et al. Investigating open reading frames in known and novel transcripts using ORFanage. Nat Comput Sci 3, 700–708 (2023). https://doi.org/10.1038/s43588-023-00496-1

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