Plant MicroRNA Identification and Annotation Using Deep Sequencing Data

MicroRNAs (miRNAs) are endogenous non-coding small RNAs, which regulate gene expression at the post-transcriptional level. A large number of studies have revealed that they play key roles in diverse life activities, such as growth and development. In the last decade, deep sequencing technology has generated substantial small RNA sequencing (sRNA-Seq) data. Meanwhile, numerous tools have been developed to identify miRNAs from these sRNA-Seq data, resulting in a surge of miRNA annotations. Among these tools, the series of miRDeep-P and miRDeep-P2 have been widely used in plant miRNA annotation. Here, we employed miRDeep-P2 to demonstrate the plant miRNA annotation processes step by step using the deep sequencing data.

MicroRNA annotation in plants: current status and challenges

Last two decades, the studies on microRNAs (miRNAs) and the numbers of annotated miRNAs in plants and animals have surged. Herein, we reviewed the current progress and challenges of miRNA annotation in plants. Via the comparison of plant and animal miRNAs, we pinpointed out the difficulties on plant miRNA annotation and proposed potential solutions. In terms of recalling the history of methods and criteria in plant miRNA annotation, we detailed how the major progresses made and evolved. By collecting and categorizing bioinformatics tools for plant miRNA annotation, we surveyed their advantages and disadvantages, especially for ones with the principle of mimicking the miRNA biogenesis pathway by parsing deeply sequenced small RNA (sRNA) libraries. In addition, we summarized all available databases hosting plant miRNAs, and posted the potential optimization solutions such as how to increase the signal-to-noise ratio (SNR) in these databases. Finally, we discussed the challenges and perspectives of plant miRNA annotations, and indicated the possibilities offered by an all-in-one tool and platform according to the integration of artificial intelligence.

MicroRNA Expression Analysis Using Small RNA Sequencing Discovery and RT-qPCR-Based Validation

miRNAs are small noncoding RNA molecules that function as regulators of gene expression. Deregulated miRNA expression has been reported in various diseases including cancer. Due to their small size and high degree of homology, accurate quantification of miRNA expression is technically challenging. In this chapter, we present two different technologies for miRNA quantification: small RNA sequencing and RT-qPCR.

A Comprehensive Prescription for Plant miRNA Identification

microRNAs (miRNAs) are tiny ribo-regulatory molecules involved in various essential pathways for persistence of cellular life, such as development, environmental adaptation, and stress response. In recent years, miRNAs have become a major focus in molecular biology because of their functional and diagnostic importance. This interest in miRNA research has resulted in the development of many specific software and pipelines for the identification of miRNAs and their specific targets, which is the key for the elucidation of miRNA-modulated gene expression. While the well-recognized importance of miRNAs in clinical research pushed the emergence of many useful computational identification approaches in animals, available software and pipelines are fewer for plants. Additionally, existing approaches suffers from mis-identification and annotation of plant miRNAs since the miRNA mining process for plants is highly prone to false-positives, particularly in cereals which have a highly repetitive genome. Our group developed a homology-based in silico miRNA identification approach for plants, which utilizes two Perl scripts "SUmirFind" and "SUmirFold" and since then, this method helped identify many miRNAs particularly from crop species such as Triticum or Aegliops. Herein, we describe a comprehensive updated guideline by the implementation of two new scripts, "SUmirPredictor" and "SUmirLocator," and refinements to our previous method in order to identify genuine miRNAs with increased sensitivity in consideration of miRNA identification problems in plants. Recent updates enable our method to provide more reliable and precise results in an automated fashion in addition to solutions for elimination of most false-positive predictions, miRNA naming and miRNA mis-annotation. It also provides a comprehensive view to genome/transcriptome-wide location of miRNA precursors as well as their association with transposable elements. The "SUmirPredictor" and "SUmirLocator" scripts are freely available together with a reference high-confidence plant miRNA list.