Data-directed RNA secondary structure prediction using probabilistic modeling

Abstract

Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.

An efficient simulated annealing algorithm for the RNA secondary structure prediction with Pseudoknots

“…As shown in Fig. 2, when the base is paired, the sequence numbers of the paired bases are exchanged and stored in Y(X), then Y(X) = (1,14,13,12,5,6,7,8,9,10,11,4,3,2,15). Each mapping string Y(X) is a candidate solution, the solution with MFE is the optimal solution, which is the most stable secondary structure.…”

“…For the calculation of the free energy of RNA secondary structure, the stem energy is defined as a negative, the energy of loop is defined as a positive, and the free single strand does not participate. Deng found that the molecular free energy is related to a single complementary base pair, but adjacent base pairs also affect the free energy calculation of the molecule [5]. In the secondary structure prediction, if the free energy calculation of each part does not affect each other, the free energy of the entire structure is accumulated form the energy of each part, and the calculation principle is shown in Eq.…”

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions

“…It is pointed out in [135] that this simple metric is not informative enough, as it is heavily biased by performances of short RNAs. To resolve this issue, this study proposed to use a “sequence-length-weighted average” (SLW-average) to replace the plain average.…”

“…showed, using mock-probe simulations, that Deigan et al . ‘s approach can give relatively poor performance when input data deviate from its assumed model [135]. To alleviate this problem and thereby provide broader applicability, several other methods have been developed.…”

Metrics for rapid quality control in RNA structure probing experiments

“…Importantly, coverage disparities within transcriptome-wide data make them more prone to this deficiency, which can be remedied by sensible integration of coverage information. Resolving such ambiguity may also improve data-directed structure inference (Deng et al, 2016).…”