NATpare: a pipeline for high-throughput prediction and functional analysis of nat-siRNAs

Genome-wide view and characterization of natural antisense transcripts in Cannabis Sativa L

Natural Antisense Transcripts (NATs) are a kind of complex regulatory RNAs that play crucial roles in gene expression and regulation. However, the NATs in Cannabis Sativa L., a widely economic and medicinal plant rich in cannabinoids remain unknown. In this study, we comprehensively predicted C. sativa NATs genome-wide using strand-specific RNA sequencing (ssRNA-Seq) data, and validated the expression profiles by strand-specific quantitative reverse transcription PCR (ssRT-qPCR). Consequently, a total of 307 NATs were predicted in C. sativa, including 104 cis- and 203 trans- NATs. Functional enrichment analysis demonstrated the potential involvement of the C. sativa NATs in DNA polymerase activity, RNA-DNA hybrid ribonuclease activity, and nucleic acid binding. Finally, 18 cis- and 376 trans- NAT-ST pairs were predicted to produce 621 cis- and 5,679 trans- small interfering RNA (nat-siRNAs), respectively. These nat-siRNAs were potentially involved in the biosynthesis of cannabinoids and cellulose. All these results will shed light on the regulation of NATs and nat-siRNAs in C. sativa.

The Multiverse of Plant Small RNAs: How Can We Explore It?

Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20–24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes—microRNAs (miRNAs) and small interfering RNAs (siRNAs)—which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.

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.

RNA Drug Delivery Using Biogenic Nanovehicles for Cancer Therapy

RNA-based therapies have been promising method for treating all kinds of diseases, and four siRNA-based drugs and two mRNA-based drugs have been approved and are on the market now. However, none of them is applied for cancer treatment. This is not only because of the complexity of the tumor microenvironment, but also due to the intrinsic obstacles of RNAs. Until now, all kinds of strategies have been developed to improve the performance of RNAs for cancer therapy, especially the nanoparticle-based ones using biogenic materials. They are much more compatible with less toxicity compared to the ones using synthetic polymers, and the most widely studied biogenic materials are oligonucleotides, exosomes, and cell membranes. Particular characteristics make them show different capacities in internalization and endosomal escape as well as specific targeting. In this paper, we systematically summarize the RNA-based nano-delivery systems using biogenic materials for cancer therapy, and we believe this review will provide a valuable reference for researchers involved in the field of biogenic delivery and RNA-based therapies for cancer treatment.

Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq

Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.