IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0020484.html
   My bibliography  Save this article

Mutational Patterns in RNA Secondary Structure Evolution Examined in Three RNA Families

Author

Listed:
  • Anuj Srivastava
  • Liming Cai
  • Jan Mrázek
  • Russell L Malmberg

Abstract

The goal of this work was to study mutational patterns in the evolution of RNA secondary structure. We analyzed bacterial tmRNA, RNaseP and eukaryotic telomerase RNA secondary structures, mapping structural variability onto phylogenetic trees constructed primarily from rRNA sequences. We found that secondary structures evolve both by whole stem insertion/deletion, and by mutations that create or disrupt stem base pairing. We analyzed the evolution of stem lengths and constructed substitution matrices describing the changes responsible for the variation in the RNA stem length. In addition, we used principal component analysis of the stem length data to determine the most variable stems in different families of RNA. This data provides new insights into the evolution of RNA secondary structures and patterns of variation in the lengths of double helical regions of RNA molecules. Our findings will facilitate design of improved mutational models for RNA structure evolution.

Suggested Citation

  • Anuj Srivastava & Liming Cai & Jan Mrázek & Russell L Malmberg, 2011. "Mutational Patterns in RNA Secondary Structure Evolution Examined in Three RNA Families," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-10, June.
    • Handle: RePEc:plo:pone00:0020484
    DOI: 10.1371/journal.pone.0020484
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0020484
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0020484&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0020484?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Elena Rivas & Sean R Eddy, 2008. "Probabilistic Phylogenetic Inference with Insertions and Deletions," PLOS Computational Biology, Public Library of Science, vol. 4(9), pages 1-21, September.
    2. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    3. Robert K Bradley & Ian Holmes, 2009. "Evolutionary Triplet Models of Structured RNA," PLOS Computational Biology, Public Library of Science, vol. 5(8), pages 1-20, August.
    4. Claude Lemieux & Christian Otis & Monique Turmel, 2000. "Ancestral chloroplast genome in Mesostigma viride reveals an early branch of green plant evolution," Nature, Nature, vol. 403(6770), pages 649-652, February.
    5. Paul D Williams & David D Pollock & Benjamin P Blackburne & Richard A Goldstein, 2006. "Assessing the Accuracy of Ancestral Protein Reconstruction Methods," PLOS Computational Biology, Public Library of Science, vol. 2(6), pages 1-8, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlos Carrasco-Farré, 2022. "The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-18, December.
    2. Felix Mbuga & Cristina Tortora, 2021. "Spectral Clustering of Mixed-Type Data," Stats, MDPI, vol. 5(1), pages 1-11, December.
    3. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    4. Michal Bernardelli & Zbigniew Korzeb & Pawel Niedziolka, 2021. "The banking sector as the absorber of the COVID-19 crisis’ economic consequences: perception of WSE investors," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 335-374, June.
    5. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    6. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Carlos Fernández-Hernández & Carmelo J. León & Jorge E. Araña & Flora Díaz-Pére, 2016. "Market segmentation, activities and environmental behaviour in rural tourism," Tourism Economics, , vol. 22(5), pages 1033-1054, October.
    8. Hafid Kadi & Mohammed Rebbah & Boudjelal Meftah & Olivier Lézoray, 2021. "A Data Representation Model for Personalized Medicine," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-25, October.
    9. Zhang, Tonglin & Lin, Ge, 2021. "Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    10. Andreas Lackner & Michael Müller & Magdalena Gamperl & Delyana Stoeva & Olivia Langmann & Henrieta Papuchova & Elisabeth Roitinger & Gerhard Dürnberger & Richard Imre & Karl Mechtler & Paulina A. Lato, 2023. "The Fgf/Erf/NCoR1/2 repressive axis controls trophoblast cell fate," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    11. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
    12. Beibei Yu & Zhonghui Wang & Haowei Mu & Li Sun & Fengning Hu, 2019. "Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    13. Liguo Fei & Jun Xia & Yuqiang Feng & Luning Liu, 2019. "A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.
    14. Bernd Scherer & Diogo Judice & Stephan Kessler, 2010. "Price reversals in global equity markets," Journal of Asset Management, Palgrave Macmillan, vol. 11(5), pages 332-345, December.
    15. Robert K Bradley & Adam Roberts & Michael Smoot & Sudeep Juvekar & Jaeyoung Do & Colin Dewey & Ian Holmes & Lior Pachter, 2009. "Fast Statistical Alignment," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-15, May.
    16. Ugofilippo Basellini & Carlo Giovanni Camarda, 2020. "Modelling COVID-19 mortality at the regional level in Italy," Working Papers axq0sudakgkzhr-blecv, French Institute for Demographic Studies.
    17. Andrew Webb, 1997. "Radial basis functions for exploratory data analysis: An iterative majorisation approach for Minkowski distances based on multidimensional scaling," Journal of Classification, Springer;The Classification Society, vol. 14(2), pages 249-267, September.
    18. Jianzhong Ma & Christopher I Amos, 2012. "Investigation of Inversion Polymorphisms in the Human Genome Using Principal Components Analysis," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-12, July.
    19. Annah Vimbai Bengesai & Evelyn Derera, 2021. "The Association Between Women Empowerment and Emotional Violence in Zimbabwe: A Cluster Analysis Approach," SAGE Open, , vol. 11(2), pages 21582440211, June.
    20. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0020484. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.