A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

Aviran

2021

Preprint

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g., transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens, and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

Section: Simplified Reactivity Model Improves Accuracy Of Motif Detectionmentioning

confidence: 68%

Section: Simplified Reactivity Model Improves Accuracy Of Motif Detectionmentioning

confidence: 99%

Rapid Structure-Function Insights via Hairpin-Centric Analysis of Big RNA Structure Probing Datasets

Aviran

2021

Preprint

“…Given the rapidly evolving field of structure probing and disparate statistical properties of SP datasets ( 47 ), we also investigated whether the benefits from the DOM generalize to other data distributions. Different probes have different quality ( 47 , 68 ), different conditions yield different quality ( 47 ), and the quality of probes is constantly improving ( 69 ); therefore, adaptability of methods is crucial. Benchmark datasets like the Weeks set are not currently available for the plethora of probes used, so we resorted to simulations.…”

Section: Resultsmentioning

confidence: 99%

“…In datasets of poor quality, the most extreme reactivities likely provide the only opportunity for reliable inference on pairing state, so it’s possible that the relatively coarse discretization scheme reduces the information content of the data. Regardless, it’s worth noting that data of such poor quality is uncommon, especially in light of on-going improvements to experimental protocols and probe quality ( 8 , 10 , 69 , 71 ). Our results also demonstrate the adaptability of the DOM and its robustness to non-Gaussian data, which render the method broadly applicable.…”

Section: Resultsmentioning

confidence: 99%

Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets

NAR Genomics and Bioinformatics

Aviran

2021

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g. transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

“…Briefly, they expose RNA to chemical reagents or enzymes that react with parts of the molecule in a structure-dependent manner; for example, when using common acylation reagents, single stranded nucleotides react more strongly than double stranded regions. This reaction induces the formation of adducts or cleavages, which can then be detected during sequencing as either truncations or mutations in reverse-transcribed cDNA fragments (18)(19)(20)(21)(22)(23). The rate of truncation or mutation at each nucleotide is then quantified and converted into a measure called reactivity that summarizes the nucleotide's structural context; the reactivities across a transcript are termed its reactivity profile (20,24).…”

Section: Introductionmentioning

confidence: 99%

Accurate Detection of RNA Stem-Loops in Structurome Data Reveals Widespread Association with Protein Binding Sites

Deshpande

et al. 2021

Preprint

RNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of its pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets its statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbor thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.