Thirteen dubious ways to detect conserved structural <scp>RNAs</scp>

Gao, William; Yang, Ann Shawing; Rivas, Elena

doi:10.1002/iub.2694

Cited by 13 publications

(9 citation statements)

References 103 publications

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“…These significantly covarying pairs that do not form any RNA helix are the result of another signal present in the alignment which results from the fact that COOLAIR class II.i isoform is antisense to the first exon of FLC. Variability within codon positions in alignments of protein-coding gene exons results in significant covariation above phylogenetic expectation that I have characterized elsewhere [8].…”

Section: Summarize the Results As Followsmentioning

confidence: 76%

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

Rivas

2023

Preprint

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Many biologically important RNAs fold into specific 3D structures conserved through evolution. Knowing when an RNA sequence includes a conserved RNA structure that could lead to new biology is not trivial and depends on clues left behind by conservation in the form of covariation and variation. For that purpose, the R-scape statistical test was created to identify from alignments of RNA sequences, the base pairs that significantly covary above phylogenetic expectation. R-scape treats base pairs as independent units. However, RNA base pairs do not occur in isolation. The Watson-Crick (WC) base pairs stack together forming helices that constitute the scaffold that facilitates the formation of the non-WC base pairs, and ultimately the complete 3D structure. The helix-forming WC base pairs carry most of the covariation signal in an RNA structure. Here, I introduce a new measure of statistically significant covariation at helix-level by aggregation of the covariation significance and covariation power calculated at base-pair-level resolution. Performance benchmarks show that helix-level aggregated covariation increases sensitivity in the detection of evolutionarily conserved RNA structure without sacrificing specificity. This additional helix-level sensitivity reveals an artifact that results from using covariation to build an alignment for a hypothetical structure and then testing the alignment for whether its covariation significantly supports the structure. Helix-level reanalysis of the evolutionary evidence for a selection of long non-coding RNAs (lncRNAs) reinforces the evidence against these lncRNAs having a conserved secondary structure. Availability: Helix aggregated E-values are integrated in the R-scape software package (version 2.0.0.p and higher). The R-scape web server eddylab.org/R-scape includes a link to download the source code. Supplementary information: Supplementary data and code are provided with this manuscript at rivaslab.org.

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Section: Summarize the Results As Followsmentioning

confidence: 76%

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

Rivas

2023

Preprint

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“…Pseudogenes pose another challenge, as they exhibit sequence and structure conservation but do not function as ncRNAs. The presence of pseudogenes in alignments of structural ncRNAs can dilute the covariation signal, resulting in an overall increase in power but a decrease in covariation at base-paired positions [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

GERONIMO: A tool for systematic retrieval of structural RNAs in a broad evolutionary context

Kilar,

Fajkus,

Fajkus

2022

GigaScience

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Background While web-based tools such as BLAST have made identifying conserved gene homologs appear easy, genes with variable sequences pose significant challenges. Functionally important noncoding RNAs (ncRNA) often show low sequence conservation due to genetic variations, including insertions and deletions. Rather than conserved sequences, these RNAs possess highly conserved structural features across a broad phylogenetic range. Such features can be identified using the covariance models approach, which combines sequence alignment with a secondary RNA structure consensus. However, running standard implementation of that approach (Infernal) requires advanced bioinformatics knowledge compared to user-friendly web services like BLAST. The issue is partially addressed by RNAcentral, which can be used to search for homologs across a broad range of ncRNA sequence collections from diverse organisms but not across the genome assemblies. Results Here, we present GERONIMO, which conducts evolutionary searches across hundreds of genomes in a fully automated way. It provides results extended with taxonomy context, as summary tables and visualizations, to facilitate analysis for user convenience. Additionally, GERONIMO supplements homologous sequences with genomic regions to analyze promoter motifs or gene collinearity, enhancing the validation of results. Conclusion GERONIMO, built using Snakemake, has undergone extensive testing on hundreds of genomes, establishing itself as a valuable tool in the identification of ncRNA homologs across diverse taxonomic groups. Consequently, GERONIMO facilitates the investigation of the evolutionary patterns of functionally significant ncRNA players, whose understanding has previously been limited to individual organisms and close relatives.

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“…Figure 4A compares our probe of ATOM-1 to two state-of-the-art 3D structure prediction methods: RhoFold [38], the deep learning method with best performance from CASP15 [43], and RoseTTAFold2NA [39]. Notably, both RhoFold and RoseTTAFold2NA make use of MSAs which are time-consuming to generate and are often unavailable for RNAs of interest [11]. Despite having no access to MSAs and being considerably smaller (∼15M parameters) and shallower (2 layers) than RhoFold (∼100M parameters in 12 layers) and RoseTTAFold2NA (∼68M parameters in 40 layers), our probe produces predictions with higher global accuracy as measured by root mean-squared deviation (RMSD) [44] to experimental structures (Figure 4A).…”

Section: Tertiary Structure Predictionmentioning

confidence: 99%

“…In fact, only 1% of entries in the Protein Data Bank (PDB) comprise RNA alone [9], despite the over 10-fold excess of genome intervals that produce RNA relative to proteins [10]. While evolutionary information encoded in multiple sequence alignments (MSAs) can provide critical insights on structure and function, these alignments are often shallow and uninformative for human targets and engineered sequences [11]. Consequently, state-of-the-art RNA structure and function prediction approaches fall short of the recent successes of highly accurate protein prediction methods [12].…”

Section: Introductionmentioning

confidence: 99%

ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data

Boyd,

Anderson,

Townshend

et al. 2023

Preprint

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RNA-based medicines and RNA-targeting drugs are emerging as promising new approaches for treating disease. Optimizing these therapeutics by naive experimental screening is a time-consuming and expensive process, while rational design requires an accurate understanding of the structure and function of RNA. To address this design challenge, we present ATOM-1, the first RNA foundation model trained on chemical mapping data, enabled by data collection strategies purposely developed for machine learning training. Using small probe neural networks on top of ATOM-1 embeddings, we demonstrate that this model has developed rich internal representations of RNA. Trained on limited amounts of additional data, these small networks achieve state-of-the-art accuracy on key RNA prediction tasks, suggesting that this approach can enable the design of therapies across the RNA landscape.

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Thirteen dubious ways to detect conserved structural RNAs

Cited by 13 publications

References 103 publications

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

GERONIMO: A tool for systematic retrieval of structural RNAs in a broad evolutionary context

ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping Data

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