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

  1. Pierce Radecki
  2. Rahul Uppuluri
  3. Sharon Aviran

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Abstract

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.

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  1. Version published to 10.1093/nargab/lqab073
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    SciScore for 10.1101/2021.04.27.441661: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

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    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The Scikit-learn Python module (v0.24) was utilized to perform these computations.
    Scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    50 replicates of each scheme were generated for the performance benchmarks using in-house Python scripts.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Averaging and Integrating HDSL over mRNA Coding Sequences: We delineated the regions surrounding the 432 genes in the Mustoe data into 4 groups: (1) start site; ±30 nt around AUG, (2) 5’UTR; −70 to −31 nt from AUG, (3) 3’ UTR; +1 to +40 from STOP codon, and (4) coding sequences; +31 nt from AUG to the STOP codon.
    STOP
    suggested: (STOP, RRID:SCR_005322)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In this way, patteRNA helps mitigate the computational limitations of such methods, especially for those who do not have advanced computing hardware at their disposal. Finally, although analyses in this study generally focus on using patteRNA to derive information on structuredness via hairpins, the method itself is fundamentally a versatile structure-mining algorithm which has been demonstrated to effectively search for putative functional motifs across in transcriptome-wide data (57). Our analysis of the SARS-CoV-2 5’UTR is distinguished from the others by a comparison of HDSL with specific structures that have been validated in a plethora of ways, including NMR spectroscopy (78). We remarked on a great correspondence of HDSL peaks and stable structural elements, indicating that HDSL captures more than just local structure—it retains information on specific motifs with high resolution. This observation is important in the context of our analysis of Corley et al.’ s fSHAPE data. Namely, the increase in HDSL around sites with high fSHAPE (Figure 6B) suggests the possibility that RBP frequently associate not only in the context of structured regions, but specifically in the context of hairpin-like elements. RBP which recognize sequence motifs in hairpin-loops have previously been identified (83, 84), but our results demonstrate the plausibility that the association between hairpin elements and RBP is more prevalent than previously thought. This is not entirely unexpected, as R...

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.04.27.441661v1 on bioRxiv