Self containment, a property of modular RNA structures, distinguishes microRNAs.
RNA molecules will tend to adopt a folded conformation through the pairing of bases on a single strand; the resulting so-called secondary structure is critical to the function of many types of RNA. The secondary structure of a particular substring of functional RNA may depend on its surrounding sequence. Yet, some RNAs such as microRNAs retain their specific structures during biogenesis, which involves extraction of the substructure from a larger structural context, while other functional RNAs may be composed of a fusion of independent substructures. Such observations raise the question of whether particular functional RNA substructures may be selected for invariance of secondary structure to their surrounding nucleotide context. We define the property of self containment to be the tendency for an RNA sequence to robustly adopt the same optimal secondary structure regardless of whether it exists in isolation or is a substring of a longer sequence of arbitrary nucleotide content. We measured degree of self containment using a scoring method we call the self-containment index and found that miRNA stem loops exhibit high self containment, consistent with the requirement for structural invariance imposed by the miRNA biogenesis pathway, while most other structured RNAs do not. Further analysis revealed a trend toward higher self containment among clustered and conserved miRNAs, suggesting that high self containment may be a characteristic of novel miRNAs acquiring new genomic contexts. We found that miRNAs display significantly enhanced self containment compared to other functional RNAs, but we also found a trend toward natural selection for self containment in most functional RNA classes. We suggest that self containment arises out of selection for robustness against perturbations, invariance during biogenesis, and modular composition of structural function. Analysis of self containment will be important for both annotation and design of functional RNAs. A Python implementation and Web interface to calculate the self-containment index are available at http://kim.bio.upenn.edu/software/.
Lee
Miler T
MT
Genomics and Computational Biology Program, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.
Kim
Junhyong
J
D000818:Animals; D001483:Base Sequence; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D030541:Databases, Genetic; D019143:Evolution, Molecular; D006801:Humans; D035683:MicroRNAs / Q000096:biosynthesis / Q000737:chemistry*; D009690:Nucleic Acid Conformation; D009693:Nucleic Acid Hybridization / Q000502:physiology; D013816:Thermodynamics
D035683:MicroRNAs