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Functional classification of long non-coding RNAs by k-mer content

Nature Genetics volume 50, pages 1474–1482 (2018)Cite this article

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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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Fig. 1: Overview and initial test of k-mer-based sequence comparison.
Fig. 2: LncRNAs of related function often have related k-mer contents.
Fig. 3: LncRNA localization and protein-binding correlate with k-mer content.
Fig. 4: K-mer content correlates with lncRNA repressive activity.
Fig. 5: Mapping of elements required for repression by Xist-2kb in TETRIS.

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Data availability

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).

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Author notes

  1. Susan O. Kim & Kaoru Inoue

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

  2. Matthew J. Smola

    Present address: Ribometrix, Durham, NC, USA

  3. Allison R. Baker

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

Authors and 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.

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Correspondence to J. Mauro Calabrese.

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Supplementary information

Supplementary Text and Figures

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

Reporting Summary

Supplementary Table 1

List of curated cis-regulatory lncRNAs in human and mouse

Supplementary Table 7

Human lncRNA community assignments and descriptions

Supplementary Table 8

Mouse lncRNA community assignments and descriptions

Supplementary Table 11

Human community k-mer profiles

Supplementary Table 12

Mouse community k-mer profiles

Supplementary Table 18

k-mer abundance in nuclear and cytosolic lncRNAs

Supplementary Table 19

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

Supplementary Table 20

Protein logistic regression (LR) precision and recall results

Supplementary Table 22

TETRIS-lncRNA fragment information

Supplementary Table 23

Oligonucleotide primers for the TETRIS assay

Supplementary Software

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

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Kirk, J.M., Kim, S.O., Inoue, K. et al. Functional classification of long non-coding RNAs by k-mer content. Nat Genet 50, 1474–1482 (2018). https://doi.org/10.1038/s41588-018-0207-8

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