Ten lessons with Carl Woese about RNA and comparative analysis

Gutell, Robin R.

doi:10.4161/rna.28718

Cited by 17 publications

(10 citation statements)

References 51 publications

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“…For instance, for the human telomerase RNA with 451 nts, the number of possible helixes with at least 3 base pairs is 1716. This “exceedingly large number of potential helices” (Gutell, 2014) is a reason why it is unlikely to predict an RNA structure correctly only from maximizing G:C, A:U, and G:U base pairs. In addition, it has been shown that small perturbation in the thermodynamic parameters, perturbations within the parameters' determination error, can result in important changes in the predicted structure (Layton & Bundschuh, 2005).…”

Section: All Rnas Even Random Ones Have Plausible Structuresmentioning

confidence: 99%

Evolutionary conservation of RNA sequence and structure

Rivas

2021

WIREs RNA

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An RNA structure prediction from a single-sequence RNA folding program is not evidence for an RNA whose structure is important for function. Random sequences have plausible and complex predicted structures not easily distinguishable from those of structural RNAs. How to tell when an RNA has a conserved structure is a question that requires looking at the evolutionary signature left by the conserved RNA. This question is important not just for long noncoding RNAs which usually lack an identified function, but also for RNA binding protein motifs which can be single stranded RNAs or structures.Here we review recent advances using sequence and structural analysis to determine when RNA structure is conserved or not. Although covariation measures assess structural RNA conservation, one must distinguish covariation due to RNA structure from covariation due to independent phylogenetic substitutions. We review a statistical test to measure false positives expected under the null hypothesis of phylogenetic covariation alone (specificity). We also review a complementary test that measures power, that is, expected covariation derived from sequence variation alone (sensitivity). Power in the absence of covariation signals the absence of a conserved RNA structure. We analyze artifacts that falsely identify conserved RNA structure such as the misuse of programs that do not assess significance, the use of inappropriate statistics confounded by signals other than covariation, or misalignments that induce spurious covariation. Among artifacts that obscure the signal of a conserved RNA structure, we discuss the inclusion of pseudogenes in alignments which increase power but destroy covariation.

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Section: All Rnas Even Random Ones Have Plausible Structuresmentioning

confidence: 99%

Evolutionary conservation of RNA sequence and structure

Rivas

2021

WIREs RNA

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“…As any two nucleotides (i.e., AUCG) can theoretically pair up (14), the native set of base pairs is regarded as the optimal among all possible sets. Such an optimal set is best inferred from the covariance patterns of homologous sequences which can be costly or impossible to obtain (10,15). In lieu of homologous sequences, traditional de novo computational approaches generally represent the secondary structure as a graph with nucleotides as nodes and base pairs as edges.…”

Section: Introductionmentioning

confidence: 99%

Decisive Roles of Sequence Distributions in the Generalizability ofde novoDeep Learning Models for RNA Secondary Structure Prediction

Qiu

2022

Preprint

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The availability of sizeable RNA structure databases and powerful deep learning (DL) frameworks has prompted recent developments of DL models for RNA secondary structure prediction. Taking RNA sequences as the only inputs, the class of de novo DL models has demonstrated far superior performances than traditional algorithms. However, key questions remain over the statistical underpinning of such DL models which make no use of co-evolutionary information or physical laws of RNA folding. Here we present a quantitative study of the capacity and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied sequence distributions of seen and unseen datasets. Excellent performances are observed from our models with as few as 16K parameters, affirming their remarkable learning capacity. Our DL models prove to generalize well over non-identical unseen sequences, but the model generalizability degrades rapidly as the sequence distributions between the seen and unseen become dissimilar. Examinations of RNA family-specific behaviors manifest not only disparate family-dependent model performances but substantial generalization gaps within the same RNA family. We further determine how model generalization decreases with the decrease of sequence similarity via pairwise sequence alignment, providing quantitative insights into the limitations of statistical learning. Model generalizability thus poses major hurdles for practical uses of current single-sequence-based DL models and we discuss avenues for future advances of such de novo DL models for RNA secondary structure prediction.

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“…Carl Woese first proposed that the systematic comparison of small subunit ribosomal RNA (SSU rRNA: 16 S rRNA for prokaryotes and 18 S rRNA for eukaryotes) sequences from different organisms would make it possible to infer the evolutionary relationships among organisms in the form of phylogenetic trees 3 – 5 . Recursive phylogenetic mapping of each newly discovered (micro-) organism onto an already constructed phylogenetic tree has progressively established a persuasive global tree of life 4 , 6 – 9 , and this pictorial concept has penetrated deeply into the field of biology as a consensus view on the way organisms have evolved 3 .…”

Section: Introductionmentioning

confidence: 99%

Comparative RNA function analysis reveals high functional similarity between distantly related bacterial 16 S rRNAs

Tsukuda

Kitahara

Miyazaki

2017

Sci Rep

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The 16 S rRNA sequence has long been used uncritically as a molecular clock to infer phylogenetic relationships among prokaryotes without fully elucidating the evolutionary changes that this molecule undergoes. In this study, we investigated the functional evolvability of 16 S rRNA, using comparative RNA function analyses between the 16 S rRNAs of Escherichia coli (Proteobacteria) and Acidobacteria (78% identity, 334 nucleotide differences) in the common genetic background of E. coli. While the growth phenotype of an E. coli mutant harboring the acidobacterial gene was disrupted significantly, it was restored almost completely following introduction of a 16 S rRNA sequence with a single base-pair variation in helix 44; the remaining 332 nucleotides were thus functionally similar to those of E. coli. Our results suggest that 16 S rRNAs share an inflexible cradle structure formed by ribosomal proteins and have evolved by accumulating species-specific yet functionally similar mutations. While this experimental evidence suggests the neutral evolvability of 16 S rRNA genes and hence satisfies the necessary requirements to use the sequence as a molecular clock, it also implies the promiscuous nature of the 16 S rRNA gene, i.e., the occurrence of horizontal gene transfer among bacteria.

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Ten lessons with Carl Woese about RNA and comparative analysis

Cited by 17 publications

References 51 publications

Evolutionary conservation of RNA sequence and structure

Evolutionary conservation of RNA sequence and structure

Decisive Roles of Sequence Distributions in the Generalizability ofde novoDeep Learning Models for RNA Secondary Structure Prediction

Comparative RNA function analysis reveals high functional similarity between distantly related bacterial 16 S rRNAs

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