ncRNA Identification Research Articles

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github (https://github.com/Zhenj-Nie/MFPINC) the data and source code can also be downloaded for free.

Non-coding RNA identification with pseudo RNA sequences and feature representation learning

Distinguishing non-coding RNAs (ncRNAs) from coding RNAs is very important in bioinformatics. Although many methods have been proposed for solving this task, it remains highly challenging to further improve the accuracy of ncRNA identification. In this paper, we propose a coding potential predictor using feature representation learning based on pseudo RNA sequences named CPPFLPS. In this method, we use the pseudo RNA sequences generated by simulating RNA sequence mutations as new samples for data augmentation, and six string operations simulating RNA sequence mutations are considered: base replacement, base insertion, base deletion, subsequence reversion, subsequence repetition and subsequence deletion. In the feature representation learning framework, different types of pseudo RNA sequences are added to the training set to form new training sets that can be used to train baseline classifiers, thus obtaining baseline models. The resulting labels of these baseline models are used as feature vectors to represent RNA sequences, and the resulting feature vectors acquired after feature selection are used to train a predictive model for distinguishing ncRNAs from coding RNAs. Our method achieves better performance compared with that of existing state-of-the-art methods. The implementation of the proposed method is available at https://github.com/chenxgscuec/CPPFLPS.

PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework.

There is evidence that non-coding RNAs play significant roles in the regulation of nutrient homeostasis, development, and stress responses in plants. Accurate identification of ncRNAs is the first step in determining their function. While a number of machine learning tools have been developed for ncRNA identification, no dedicated tool has been developed for ncRNA identification in plants. Here, an automated machine learning tool, PINC is presented to identify ncRNAs in plants using RNA sequences. First, we extracted 91 features from the sequence. Second, we combined the F-test and variance threshold for feature selection to find 10 features. The AutoGluon framework was used to train models for robust identification of non-coding RNAs from datasets constructed for four plant species. Last, these processes were combined into a tool, called PINC, for the identification of plant ncRNAs, which was validated on nine independent test sets, and the accuracy of PINC ranged from 92.74% to 96.42%. As compared with CPC2, CPAT, CPPred, and CNIT, PINC outperformed the other tools in at least five of the eight evaluation indicators. PINC is expected to contribute to identifying and annotating novel ncRNAs in plants.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants.

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

Swarm Intelligence-based Hierarchical Clustering for Identification of ncRNA using Covariance Search Model

Covariance Model (CM) has been quite effective in finding potential members of existing families of non-coding Ribonucleic Acid (ncRNA) identification and has provided ex-cellent accuracy in genome sequence database. However, it has significant drawbacks with family-specific search. An existing Hierarchical Agglomerative Clustering (HAC) technique merged overlapping sequences which is known as combined CM (CCM). However, the structural information will be discarded, and the sequence features of each family will be significantly diluted as the number of original structures increases. Additionally, it can only find members of the existing families and is not useful in finding potential members of novel ncRNA families. Furthermore, it is also important to construct generic sequence models which can be used to recognize new potential members of novel ncRNA families and define unknown ncRNA sequence as the potential members for known families. To achieve these objectives, this study proposes to implement Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to ensure the CCMs have the best quality for every level of dendrogram hierarchy. This study will also apply distance matrix as the criteria to measure the compatibility between two CMs. The proposed techniques will be using five gene families with fifty sequences from each family from Rfam database which will be divided into training and testing dataset to test CMs combination method. The proposed techniques will be compared to the existing HAC in terms of identification accuracy, sum of bit-scores, and processing time, where each of these performance measurements will be statistically validated.

Dynamics of DNA Methylation and Its Functions in Plant Growth and Development.

Epigenetic modifications in DNA bases and histone proteins play important roles in the regulation of gene expression and genome stability. Chemical modification of DNA base (e.g., addition of a methyl group at the fifth carbon of cytosine residue) switches on/off the gene expression during developmental process and environmental stresses. The dynamics of DNA base methylation depends mainly on the activities of the writer/eraser guided by non-coding RNA (ncRNA) and regulated by the developmental/environmental cues. De novo DNA methylation and active demethylation activities control the methylation level and regulate the gene expression. Identification of ncRNA involved in de novo DNA methylation, increased DNA methylation proteins guiding DNA demethylase, and methylation monitoring sequence that helps maintaining a balance between DNA methylation and demethylation is the recent developments that may resolve some of the enigmas. Such discoveries provide a better understanding of the dynamics/functions of DNA base methylation and epigenetic regulation of growth, development, and stress tolerance in crop plants. Identification of epigenetic pathways in animals, their existence/orthologs in plants, and functional validation might improve future strategies for epigenome editing toward climate-resilient, sustainable agriculture in this era of global climate change. The present review discusses the dynamics of DNA methylation (cytosine/adenine) in plants, its functions in regulating gene expression under abiotic/biotic stresses, developmental processes, and genome stability.

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide ncRNA data in FASTA format which is well suited for biomedical purposes. However, for ncRNA analysis and classification, statistical learning methods require hidden numerical features from the data. Furthermore, in the literature, a wealth of sequence intrinsic features has been proposed for ncRNA identification. The extraction of hidden features, their analysis, and usage of a suitable set of features is crucial for the performance of any statistical learning method. To alleviate the posed challenges, we generated 96 feature datasets from ncRNA widely used features. The feature datasets are based on RNACentral and consist of species, ncRNA types, and expert databases that are available on the FexRNA platform. Additionally, the feature datasets are explored and analysed to provide statistical information, univariate, and bivariate analysis. We sought to determine which of these 17 features would be most appropriate to use in developing ncRNA classification approaches. For feature selection (FS), a two-phase hierarchical FS framework based on correlation and majority voting is proposed and evaluated on 5 species. The FexRNA platform provides information about ncRNA feature analysis and selection.

Revisiting the Relationships Between Genomic G + C Content, RNA Secondary Structures, and Optimal Growth Temperature.

Over twenty years ago Galtier and Lobry published a manuscript entitled "Relationships between Genomic G + C Content, RNA Secondary Structure, and Optimal Growth Temperature" in the Journal of Molecular Evolution that showcased the lack of a relationship between genomic G + C content and optimal growth temperature (OGT) in a set of about 200 prokaryotes. Galtier and Lobry also assessed the relationship between RNA secondary structures (rRNA stems, tRNAs) and OGT, and in this case a clear relationship emerged. Increasing structured RNA G + C content (particularly in regions that are double-stranded) correlates with increased OGT. Both of these fundamental relationships have withstood test of many additional sequences and spawned a variety of different applications that include prediction of OGT from rRNA sequence and computational ncRNA identification approaches. In this work, I present the motivation behind Galtier and Lobry's original paper and the larger questions addressed by the work, how these questions have evolved over the last two decades, and the impact of Galtier and Lobry's manuscript in fields beyond these questions.

Versatile interactions and bioinformatics analysis of noncoding RNAs.

Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.

Evaluation of deep learning in non-coding RNA classification

Non-coding (nc) RNA plays a vital role in biological processes and has been associated with diseases such as cancer. Classification of ncRNAs is necessary for understanding the underlying mechanisms of the diseases and to design effective treatments. Recently, deep learning has been employed for ncRNA identification and classification and has shown promising results. In this study, we review the progress of ncRNA type classification, specifically lncRNA, lincRNA, circular RNA and small ncRNA, and present a comprehensive comparison of six deep learning based classification methods published in the past two years. We identify research gaps and challenges of ncRNA types, such as the classification of subclasses of lncRNA, transcript length and compositional variation, dependency on database searches and the high false positive rate of existing approaches. We suggest future directions for cross-species performance deviation, deep learning model selection and sequence intrinsic features. Many functions of RNA strands that do not code for proteins are still to be deciphered. Methods to classify different groups of non-coding RNA increasingly use deep learning, but the landscape is diverse and methods need to be categorized and benchmarked to move forward. The authors take a close look at six state-of-the-art deep learning non-coding RNA classifiers and compare their performance and architecture.

Roles of Non-Coding RNAs in Normal Human Brain Development, Brain Tumor, and Neuropsychiatric Disorders.

One of modern biology’s great surprises is that the human genome encodes only ~20,000 protein-coding genes, which represents less than 2% of the total genome sequence, and the majority of them are transcribed into non-coding RNAs (ncRNAs). Increasing evidence has shown that ncRNAs, including miRNAs, long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), play important roles in regulating a wide range of biological processes of the human brain. They not only regulate the pathogenesis of brain tumors, but also the development of neuropsychiatric diseases. This review provides an integrated overview of the roles of ncRNAs in normal human brain function, brain tumor development, and neuropsychiatric disease. We discussed the functions and molecular mechanisms of miRNAs, lncRNAs, and circRNAs in normal brain function and glioma, respectively, including those in exosome vesicles that can act as a molecular information carrier. We also discussed the regulatory roles of ncRNAs in the development of neuropsychiatric diseases. Lastly, we summarized the currently available platforms and tools that can be used for ncRNA identification and functional exploration in human diseases. This study will provide comprehensive insights for the roles of ncRNAs in human brain function and disease.

Discovery and validation of novel and distinct RNA regulators for ribosomal protein S15 in diverse bacterial phyla.

BackgroundAutogenous cis-regulators of ribosomal protein synthesis play a critical role in maintaining the stoichiometry of ribosome components. Structured portions within an mRNA transcript typically interact with specific ribosomal proteins to prevent expression of the entire operon, thus balancing levels of ribosomal proteins across transcriptional units. Three distinct RNA structures from different bacterial phyla have demonstrated interactions with S15 to regulate gene expression; however, these RNAs are distributed across a small fraction of bacterial diversity.ResultsWe used comparative genomics in combination with analysis of existing transcriptomic data to identify three novel putative RNA structures associated with the S15 coding region in microbial genomes. These structures are completely distinct from those previously published and encompass potential regulatory regions including ribosome-binding sites. To validate the biological relevance of our findings, we demonstrate that an example of the Alphaproteobacterial RNA from Rhizobium radiobacter specifically interacts with S15 in vitro, and allows in vivo regulation of gene expression in an E. coli reporter system. In addition, structural probing and nuclease protection assays confirm the predicted secondary structure and indicate nucleotides required for protein interaction.ConclusionsThis work illustrates the importance of integrating comparative genomic and transcriptomic approaches during de novo ncRNA identification and reveals a diversity of distinct natural RNA regulators that support analogous biological functions. Furthermore, this work indicates that many additional uncharacterized RNA regulators likely exist within bacterial genomes and that the plasticity of RNA structure allows unique, and likely independently derived, solutions to the same biological problem.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-657) contains supplementary material, which is available to authorized users.

RNA-CODE: a noncoding RNA classification tool for short reads in NGS data lacking reference genomes.

The number of transcriptomic sequencing projects of various non-model organisms is still accumulating rapidly. As non-coding RNAs (ncRNAs) are highly abundant in living organism and play important roles in many biological processes, identifying fragmentary members of ncRNAs in small RNA-seq data is an important step in post-NGS analysis. However, the state-of-the-art ncRNA search tools are not optimized for next-generation sequencing (NGS) data, especially for very short reads. In this work, we propose and implement a comprehensive ncRNA classification tool (RNA-CODE) for very short reads. RNA-CODE is specifically designed for ncRNA identification in NGS data that lack quality reference genomes. Given a set of short reads, our tool classifies the reads into different types of ncRNA families. The classification results can be used to quantify the expression levels of different types of ncRNAs in RNA-seq data and ncRNA composition profiles in metagenomic data, respectively. The experimental results of applying RNA-CODE to RNA-seq of Arabidopsis and a metagenomic data set sampled from human guts demonstrate that RNA-CODE competes favorably in both sensitivity and specificity with other tools. The source codes of RNA-CODE can be downloaded at http://www.cse.msu.edu/~chengy/RNA_CODE.

Identification and expression levels of pig miRNAs in skeletal muscle

MicroRNAs are a class of naturally occurring non-coding RNAs. Typically they are ∼22 nucleotides long and suppress translation of their targets genes. Several laboratories have attempted to identify miRNAs from pig muscle and the bioinformatics strategies using ESTs have proved to be successful for this aim. In this study we report an in silico identification of ncRNA in pig EST libraries focusing on novel pig miRNAs and further investigated the differential expression of pigs miRNAs (known and novel) by quantitative real-time PCR during pre- and postnatal stage from Commercial and local breed Piau pigs skeletal muscle tissue. We identified two miRNAs not yet described in pigs: hsa-miR-1207-5p and hsa-miR-665. Besides, we found 288 target genes for hsa-miR-1207-5p and 214 for hsa-miR-665; from them, four are muscle specific genes. Through expression analyses, differences were found between pre- and postnatal stages and genetics groups. The findings of miRNAs and their muscle-specific targets in pigs will be helpful for understanding the function and processing of this RNA class in the future. Besides, the miRNAs differentially expressed between Commercial and Piau breeds suggest that they can be used to uncover phenotypic differences across different genetic groups.

Chain-RNA: A Comparative ncRNA Search Tool Based on the Two-Dimensional Chain Algorithm

Noncoding RNA (ncRNA) identification is highly important to modern biology. The state-of-the-art method for ncRNA identification is based on comparative genomics, in which evolutionary conservations of sequences and secondary structures provide important evidence for ncRNA search. For ncRNAs with low sequence conservation but high structural similarity, conventional local alignment tools such as BLAST yield low sensitivity. Thus, there is a need for ncRNA search methods that can incorporate both sequence and structural similarities. We introduce chain-RNA, a pairwise structural alignment tool that can effectively locate cross-species conserved RNA elements with low sequence similarity. In chain-RNA, stem-loop structures are extracted from dot plots generated by an efficient local-folding algorithm. Then, we formulate stem alignment as an extended 2D chain problem and employ existing chain algorithms. Chain-RNA is tested on a data set containing annotated ncRNA homologs and is applied to novel ncRNA search in a transcriptomic data set. The experimental results show that chain-RNA has better tradeoff between sensitivity and false positive rate in ncRNA prediction than conventional sequence similarity search tools and is more time efficient than structural alignment tools. The source codes of chain-RNA can be downloaded at http://sourceforge.net/projects/chain-rna/ or at http://www.cse.msu.edu/~leijikai/chain-rna/.

Efficient known ncRNA search including pseudoknots

BackgroundSearching for members of characterized ncRNA families containing pseudoknots is an important component of genome-scale ncRNA annotation. However, the state-of-the-art known ncRNA search is based on context-free grammar (CFG), which cannot effectively model pseudoknots. Thus, existing CFG-based ncRNA identification tools usually ignore pseudoknots during search. As a result, dozens of sequences that do not contain the native pseudoknots are reported by these tools. When pseudoknot structures are vital to the functions of the ncRNAs, these sequences may not be true members.ResultsIn this work, we design a pseudoknot search tool using multiple simple sub-structures, which are derived from knot-free and bifurcation-free structural motifs in the underlying family. We test our tool on a contiguous 22-Mb region of the Maize Genome. The experimental results show that our work competes favorably with other pseudoknot search methods.ConclusionsOur sub-structure based tool can conduct genome-scale pseudoknot-containing ncRNA search effectively and efficiently. It provides a complementary pseudoknot search tool to Infernal. The source codes are available at http://www.cse.msu.edu/~chengy/knotsearch.

Designing Filters for Fast-Known NcRNA Identification

Detecting members of known noncoding RNA (ncRNA) families in genomic DNA is an important part of sequence annotation. However, the most widely used tool for modeling ncRNA families, the covariance model (CM), incurs a high-computational cost when used for genome-wide search. This cost can be reduced by using a filter to exclude sequences that are unlikely to contain the ncRNA of interest, applying the CM only where it is likely to match strongly. Despite recent advances, designing an efficient filter that can detect ncRNA instances lacking strong conservation while excluding most irrelevant sequences remains challenging. In this work, we design three types of filters based on multiple secondary structure profiles (SSPs). An SSP augments a regular profile (i.e., a position weight matrix) with secondary structure information but can still be efficiently scanned against long sequences. Multi-SSPbased filters combine evidence from multiple SSP matches and can achieve high sensitivity and specificity. Our SSP-based filters are extensively tested in BRAliBase III data set, Rfam 9.0, and a published soil metagenomic data set. In addition, we compare the SSPbased filters with several other ncRNA search tools including Infernal (with profile HMMs as filters), ERPIN, and tRNAscan-SE. Our experiments demonstrate that carefully designed SSP filters can achieve significant speedup over unfiltered CM search while maintaining high sensitivity for various ncRNA families. The designed filters and filter-scanning programs are available at our website: www.cse.msu.edu/~yannisun/ssp/.

Genome-scale NCRNA homology search using a Hamming distance-based filtration strategy.

BackgroundNCRNAs (noncoding RNAs) play important roles in many biological processes. Existing genome-scale ncRNA search tools identify ncRNAs in local sequence alignments generated by conventional sequence comparison methods. However, some types of ncRNA lack strong sequence conservation and tend to be missed or mis-aligned by conventional sequence comparison.ResultsIn this paper, we propose an ncRNA identification framework that is complementary to existing sequence comparison tools. By integrating a filtration step based on Hamming distance and ncRNA alignment programs such as FOLDALIGN or PLAST-ncRNA, the proposed ncRNA search framework can identify ncRNAs that lack strong sequence conservation. In addition, as the ratio of transition and transversion mutation is often used as a discriminative feature for functional ncRNA identification, we incorporate this feature into the filtration step using a coding strategy. We apply Hamming distance seeds to ncRNA search in the intergenic regions of human and mouse genomes and between the Burkholderia cenocepacia J2315 genome and the Ralstonia solanacearum genome. The experimental results demonstrate that a carefully designed Hamming distance seed can achieve better sensitivity in searching for poorly conserved ncRNAs than conventional sequence comparison tools.ConclusionsHamming distance seeds provide better sensitivity as a filtration strategy for genome-wide ncRNA homology search than the existing seeding strategies used in BLAST-like tools. By combining Hamming distance seeds matching and ncRNA alignment, we are able to find ncRNAs with sequence similarities below 60%.

Bioinformatic Challenges of Big Data in Non-Coding RNA Research

Bioinformatic Challenges of Big Data in Non-Coding RNA Research

Adjacent Nucleotide Dependence in ncRNA and Order-1 SCFG for ncRNA Identification

BackgroundNon-coding RNAs (ncRNAs) are known to be involved in many critical biological processes, and identification of ncRNAs is an important task in biological research. A popular software, Infernal, is the most successful prediction tool and exhibits high sensitivity. The application of Infernal has been mainly focused on small suspected regions. We tried to apply Infernal on a chromosome level; the results have high sensitivity, yet contain many false positives. Further enhancing Infernal for chromosome level or genome wide study is desirable.MethodologyBased on the conjecture that adjacent nucleotide dependence affects the stability of the secondary structure of an ncRNA, we first conduct a systematic study on human ncRNAs and find that adjacent nucleotide dependence in human ncRNA should be useful for identifying ncRNAs. We then incorporate this dependence in the SCFG model and develop a new order-1 SCFG model for identifying ncRNAs.ConclusionsWith respect to our experiments on human chromosomes, the proposed new model can eliminate more than 50% false positives reported by Infernal while maintaining the same sensitivity. The executable and the source code of programs are freely available at http://i.cs.hku.hk/~kfwong/order1scfg.