Computational identification of protein binding sites on RNAs using high-throughput RNA structure-probing data

RNA Structure Determination by High-Throughput Structural Analysis

RNA functions are closely linked with their structures. Therefore, elucidating the secondary structure of RNAs provides crucial information regarding their function. The chemical modification or RNase-mediated digestion of single-stranded RNA has been utilized to experimentally reveal RNA secondary structures. Owing to advances in high-throughput sequencing technology and chemical analysis, RNA structural analyses that enable structural profiling at the transcriptomic scale in living cells have been developed. Here, we provide an overview of the high-throughput RNA structural (HTS) analyses and describe the computational processing steps of recent HTS analysis pipelines: PROBer, BUMHMM, and reactIDR.

reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction

Background

Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.

Results

Here, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures.

Conclusions

We have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2645-4) contains supplementary material, which is available to authorized users.

Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

Computational analysis of RNA structures with chemical probing data

RNAs play various roles, not only as the genetic codes to synthesize proteins, but also as the direct participants of biological functions determined by their underlying high-order structures. Although many computational methods have been proposed for analyzing RNA structures, their accuracy and efficiency are limited, especially when applied to the large RNAs and the genome-wide data sets. Recently, advances in parallel sequencing and high-throughput chemical probing technologies have prompted the development of numerous new algorithms, which can incorporate the auxiliary structural information obtained from those experiments. Their potential has been revealed by the secondary structure prediction of ribosomal RNAs and the genome-wide ncRNA function annotation. In this review, the existing probing-directed computational methods for RNA secondary and tertiary structure analysis are discussed.