miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification

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.

In silico identification of conserved miRNAs in the genome of fibre biogenesis crop Corchorus capsularis

Corchorus capsularis, commonly known as jute occupies the leading position in the production of natural fibre alongside lower environmental threat. Small noncoding ~21 to 24 nucleotides long microRNAs play significant roles in regulating the gene expression as well as different functions in cellular growth and development. Here, the study adopted a comprehensive in silico approach to identify and characterize the conserved miRNAs in the genome of C. capsularis including functional annotation of specific gene targets. Expressed Sequence Tags (ESTs) based homology search of 3350 known miRNAs of dicotyledons were allowed against 763 non-redundant ESTs of jute genome, resulted in the prediction of 5 potential miRNA candidates belonging five different miRNA families (miR1536, miR9567-3p, miR4391, miR11300, and miR8689). The putative miRNAs were composed of 18 nucleotides having a range of -0.49 to -1.56 MFEI values and 55%–61% of (A + U) content in their pre-miRNAs. A total of 1052 gene targets of putative miRNAs were identified and their functions were extensively analyzed. Most of the gene targets were involved in plant growth, cell cycle regulation, organelle synthesis, developmental process and environmental responses. Five gene targets, namely, NAC Domain Containing Protein, WRKY DNA binding protein, 3-dehydroquinate synthase, S-adenosyl-L-Met–dependent methyl transferase and Vascular-related NAC-Domain were found to be involved in the lignin biosynthesis, phenylpropanoid pathways and secondary wall formation. The present study might accelerate the more miRNA discovery, strengthening the complete understanding of miRNAs association in the cellular basis of lignin biosynthesis towards the production of high standard jute products.