Abstract

The functions of most long non-coding RNAs (lncRNAs) are unknown. In contrast to proteins, lncRNAs with similar functions often lack linear sequence homology; thus, the identification of function in one lncRNA rarely informs the identification of function in others. We developed a sequence comparison method to deconstruct linear sequence relationships in lncRNAs and evaluate similarity based on the abundance of short motifs called k-mers. We found that lncRNAs of related function often had similar k-mer profiles despite lacking linear homology, and that k-mer profiles correlated with protein binding to lncRNAs and with their subcellular localization. Using a novel assay to quantify Xist-like regulatory potential, we directly demonstrated that evolutionarily unrelated lncRNAs can encode similar function through different spatial arrangements of related sequence motifs. K-mer-based classification is a powerful approach to detect recurrent relationships between sequence and function in lncRNAs.

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The datasets generated during and/or analyzed during the current study are available within the article and its supplementary information files.

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Acknowledgements

We thank UNC colleagues for discussions, and J. Cheng for help with TETRIS cloning. This work was supported by National Institutes of Health (NIH) Grants UL1TR002489, GM121806, and GM105785, Basil O’Connor Award no. 5100683 from the March of Dimes Foundation, and funds from the Eshelman Institute for Innovation, the Lineberger Comprehensive Cancer Center and the UNC Department of Pharmacology (J.M.C.), the James S. McDonnell Foundation 21st Century Science Initiative–Complex Systems Scholar Award Grant no. 220020315 (P.J.M.), and NIH MIRA award R35 GM122532 (K.M.W.). J.M.K. is an NSF Graduate Research Fellow (Grant DGE-1650116) and was supported in part by an NIH training grant in bioinformatics and computational biology (T32 GM067553). D.M.L. was supported in part by an NIH training grant in genetics and molecular biology (T32 GM007092). M.J.S. was an NSF Graduate Research Fellow (Grant DGE-1144081) and was supported in part by an NIH training grant in molecular and cellular biophysics (Grant T32 GM08570).

Author information

Author notes

    • Susan O. Kim
    •  & Kaoru Inoue

    Present address: National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA

    • Matthew J. Smola

    Present address: Ribometrix, Durham, NC, USA

    • Allison R. Baker

    Present address: Harvard Medical School, Ph.D. Program in Biological and Biomedical Sciences, Boston, MA, USA

Affiliations

  1. Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • Jessime M. Kirk
    • , Susan O. Kim
    • , Kaoru Inoue
    • , David M. Lee
    • , Megan D. Schertzer
    • , Joshua S. Wooten
    • , Allison R. Baker
    • , Daniel Sprague
    •  & J. Mauro Calabrese
  2. Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • Jessime M. Kirk
  3. Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • Matthew J. Smola
    •  & Kevin M. Weeks
  4. Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • David M. Lee
    • , Megan D. Schertzer
    •  & Joshua S. Wooten
  5. Curriculum in Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • Daniel Sprague
  6. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • David W. Collins
    • , Christopher R. Horning
    • , Shuo Wang
    •  & Qidi Chen
  7. Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • Peter J. Mucha

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Contributions

J.M.K., P.J.M., and J.M.C. conceived the study. J.M.K., D.S., and J.M.C. performed the computational analysis. S.O.K., K.I., D.M.L., M.D.S., J.S.W., A.R.B., K.M.W., and J.M.C. designed and performed the TETRIS assays. D.W.C., C.R.H., S.W., Q.C., and J.M.K. built the website. J.M.K. and J.M.C. wrote the paper.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to J. Mauro Calabrese.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–11 and Supplementary Tables 2–6, 9, 10, 13–17 and 21

  2. Reporting Summary

  3. Supplementary Table 1

    List of curated cis-regulatory lncRNAs in human and mouse

  4. Supplementary Table 7

    Human lncRNA community assignments and descriptions

  5. Supplementary Table 8

    Mouse lncRNA community assignments and descriptions

  6. Supplementary Table 11

    Human community k-mer profiles

  7. Supplementary Table 12

    Mouse community k-mer profiles

  8. Supplementary Table 18

    k-mer abundance in nuclear and cytosolic lncRNAs

  9. Supplementary Table 19

    Protein log-likelihood results comparing the predictive power of null versus full logistic regression models

  10. Supplementary Table 20

    Protein logistic regression (LR) precision and recall results

  11. Supplementary Table 22

    TETRIS-lncRNA fragment information

  12. Supplementary Table 23

    Oligonucleotide primers for the TETRIS assay

  13. Supplementary Software

    A library for counting small k-mer frequencies in nucleotide sequences

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DOI

https://doi.org/10.1038/s41588-018-0207-8