Prediction and differential analysis of RNA secondary structure

ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction

BACKGROUND

Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have similar functions, therefore, it is important to accurately predict ncRNAs families to identify their functions. There are several methods available to solve the prediction problem of ncRNAs family, whose main ideas can be divided into two categories, including prediction based on the secondary structure features of ncRNAs, and prediction according to sequence features of ncRNAs. The first type of prediction method requires a complicated process and has a low accuracy in obtaining the secondary structure of ncRNAs, while the second type of method has a simple prediction process and a high accuracy, but there is still room for improvement. The existing methods for ncRNAs family prediction are associated with problems such as complicated prediction processes and low accuracy, in this regard, it is necessary to propose a new method to predict the ncRNAs family more perfectly.

RESULTS

A deep learning model-based method, ncDENSE, was proposed in this study, which predicted ncRNAs families by extracting ncRNAs sequence features. The bases in ncRNAs sequences were encoded by one-hot coding and later fed into an ensemble deep learning model, which contained the dynamic bi-directional gated recurrent unit (Bi-GRU), the dense convolutional network (DenseNet), and the Attention Mechanism (AM). To be specific, dynamic Bi-GRU was used to extract contextual feature information and capture long-term dependencies of ncRNAs sequences. AM was employed to assign different weights to features extracted by Bi-GRU and focused the attention on information with greater weights. Whereas DenseNet was adopted to extract local feature information of ncRNAs sequences and classify them by the full connection layer. According to our results, the ncDENSE method improved the Accuracy, Sensitivity, Precision, F-score, and MCC by 2.08[Formula: see text], 2.33[Formula: see text], 2.14[Formula: see text], 2.16[Formula: see text], and 2.39[Formula: see text], respectively, compared with the suboptimal method.

CONCLUSIONS

Overall, the ncDENSE method proposed in this paper extracts sequence features of ncRNAs by dynamic Bi-GRU and DenseNet and improves the accuracy in predicting ncRNAs family and other data.

Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure

RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

The authors present DiffScan, an advanced tool for normalization and differential analysis of RNA structure probing experiments, combining their power in deciphering the dynamic RNA structurome and facilitating the discovery of RNA regulatory functions.

Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.

Discovering functional motifs in long noncoding RNAs

Long noncoding RNAs (lncRNAs) are products of pervasive transcription that closely resemble messenger RNAs on the molecular level, yet function through largely unknown modes of action. The current model is that the function of lncRNAs often relies on specific, typically short, conserved elements, connected by linkers in which specific sequences and/or structures are less important. This notion has fueled the development of both computational and experimental methods focused on the discovery of functional elements within lncRNA genes, based on diverse signals such as evolutionary conservation, predicted structural elements, or the ability to rescue loss-of-function phenotypes. In this review, we outline the main challenges that the different methods need to overcome, describe the recently developed approaches, and discuss their respective limitations. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

DNA nanomachine for visual detection of structured RNA and double stranded DNA

Visual detection of ssRNA and dsDNA amplicons was achieved at room temperature without the need for a probe-analyte annealing stage. This approach uses a DNA nanostructure equipped with two analyte-binding arms. Highly selective binding of the third arm leads to the formation of a G-quadruplex structure capable of changing the solution color.

Generation of 3'UTR knockout cell lines by CRISPR/Cas9-mediated genome editing

In addition to the protein code, messenger RNAs (mRNAs) also contain untranslated regions (UTRs). 3'UTRs span the region between the translational stop codon and the poly(A) tail. Sequence elements located in 3'UTRs are essential for pre-mRNA processing. 3'UTRs also contain elements that can regulate protein abundance, localization, and function. At least half of all human genes use alternative cleavage and polyadenylation (APA) to further diversify the regulatory potential of protein functions. Traditional gene editing approaches are designed to disrupt the production of functional proteins. Here, we describe a method that allows investigators to manipulate 3'UTR sequences of endogenous genes for both single- 3'UTR and multi-3'UTR genes. As 3'UTRs can regulate individual functions of proteins, techniques to manipulate 3'UTRs at endogenous gene loci will help to disentangle multi-functionality of proteins. Furthermore, the ability to directly examine the impact of gene regulatory elements in 3'UTRs will provide further insights into their functional significance.

A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network

Background

Studies have shown that RNA secondary structure, a planar structure formed by paired bases, plays diverse vital roles in fundamental life activities and complex diseases. RNA secondary structure profile can record whether each base is paired with others. Hence, accurate prediction of secondary structure profile can help to deduce the secondary structure and binding site of RNA. RNA secondary structure profile can be obtained through biological experiment and calculation methods. Of them, the biological experiment method involves two ways: chemical reagent and biological crystallization. The chemical reagent method can obtain a large number of prediction data, but its cost is high and always associated with high noise, making it difficult to get results of all bases on RNA due to the limited of sequencing coverage. By contrast, the biological crystallization method can lead to accurate results, yet heavy experimental work and high costs are required. On the other hand, the calculation method is CROSS, which comprises a three-layer fully connected neural network. However, CROSS can not completely learn the features of RNA secondary structure profile since its poor network structure, leading to its low performance.

Results

In this paper, a novel end-to-end method, named as “RPRes, was proposed to predict RNA secondary structure profile based on Bidirectional LSTM and Residual Neural Network.

Conclusions

RPRes utilizes data sets generated by multiple biological experiment methods as the training, validation, and test sets to predict profile, which can compatible with numerous prediction requirements. Compared with the biological experiment method, RPRes has reduced the costs and improved the prediction efficiency. Compared with the state-of-the-art calculation method CROSS, RPRes has significantly improved performance.

Applications of phage-derived RNA-based technologies in synthetic biology

As the most abundant biological entities with incredible diversity, bacteriophages (also known as phages) have been recognized as an important source of molecular machines for the development of genetic-engineering tools. At the same time, phages are crucial for establishing and improving basic theories of molecular biology. Studies on phages provide rich sources of essential elements for synthetic circuit design as well as powerful support for the improvement of directed evolution platforms. Therefore, phages play a vital role in the development of new technologies and central scientific concepts. After the RNA world hypothesis was proposed and developed, novel biological functions of RNA continue to be discovered. RNA and its related elements are widely used in many fields such as metabolic engineering and medical diagnosis, and their versatility led to a major role of RNA in synthetic biology. Further development of RNA-based technologies will advance synthetic biological tools as well as provide verification of the RNA world hypothesis. Most synthetic biology efforts are based on reconstructing existing biological systems, understanding fundamental biological processes, and developing new technologies. RNA-based technologies derived from phages will offer abundant sources for synthetic biological components. Moreover, phages as well as RNA have high impact on biological evolution, which is pivotal for understanding the origin of life, building artificial life-forms, and precisely reprogramming biological systems. This review discusses phage-derived RNA-based technologies terms of phage components, the phage lifecycle, and interactions between phages and bacteria. The significance of RNA-based technology derived from phages for synthetic biology and for understanding the earliest stages of biological evolution will be highlighted.