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Understanding Non-Coding RNAs: Insights from Homology and Comparative Genomics

This overview explores how to infer the structure and function of non-coding RNAs without experimental methods. By leveraging covarying mutations, homologous sequences, and statistical significance, we can gain insights into RNA architecture. Key resources like Infernal software and the Rfam database facilitate analysis. Studies illustrate how nearby genetic elements can inform RNA function. Notable works by scientists, including those from the Weinberg and Breaker laboratory, highlight the importance of comparative sequence analysis in uncovering RNA's biological roles.

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Understanding Non-Coding RNAs: Insights from Homology and Comparative Genomics

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  1. What can you know about noncoding RNAs without doing any experiments? Zasha Weinberg Laboratory of Ron Breaker Yale University / HHMI

  2. Data: (Baker, et al., 2012) Drawing: (Weinberg & Breaker, 2011)

  3. Importance of homology

  4. Mammalian HDV ribozyme: poor covariation (Based on Salehi-Ashtiani, et al., 2006)

  5. Mammalian HDV ribozyme: poor covariation (Based on Salehi-Ashtiani, et al., 2006)

  6. Mammalian HDV ribozyme: poor covariation (Based on Salehi-Ashtiani, et al., 2006)

  7. You can know a structure given: • Covarying mutations • Few bad mutations • Homology • Correct alignment • Statistical significance

  8. Michel and Westhof (1990)“Modeling of the three-dimensional architecture of group I catalytic introns based on comparative sequence analysis” • Pace, Thomas, Woese (1999)“Probing RNA structure, function and history by comparative analysis” • Weinberg, et al., and Breaker (2010) “Comparative genomics reveals 104 candidate structured RNAs from bacteria, archaea, and their metagenomes.”

  9. Knowing the homologs • Infernal software search • Knowing: E-value < 10-5 • Thinking: E-value < 10-1 • BLAST search • Knowing: E-value < 10-15 (maybe) • Thinking: E-value < 10-5 • Pattern search, e.g., Rnamotif • No significance statistic given

  10. Nearby genetic elements can suggest function (Ames, et al., 2010)

  11. RNA-specific tools • Infernal http://infernal.janelia.org • Rfam Database http://rfam.sanger.ac.uk • RNA sequence alignment editors: • Any text editor • Boulder ALE http://www.microbio.me/boulderale/ • RALEE and Emacs http://personalpages.manchester.ac.uk/staff/sam.griffiths-jones/software/ralee/ • Drawing RNAs • R2R http://www.biomedcentral.com/1471-2105/12/3 • XRNA http://rna.ucsc.edu/rnacenter/xrna/ • VARNA http://varna.lri.fr/ • Prediction of alignments • WAR Server http://genome.ku.dk/resources/war/ • Others: CMfinder, LocaRNA

  12. Summary • Know structure by covariation • Know homologs by E-values • Know function by nearby genes Thanks: Breaker Lab members HHMI NIH Special thanks: mutation and selection

  13. Other examples of RNAs and their nearby genes (Lee, et al., 2010), (Weinberg, et al., 2009)

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