miRNAFinder: A Comprehensive Web Resource for Plant Pre-microRNA Classification

microRNAs (miRNAs) are known as one of the small non-coding RNA molecules, which control the expressions of genes at the RNA level. They typically range 20-24 nucleotides in length and can be found in the plant and animal kingdoms and in some viruses. Computational approaches have overcome the limitations in the experimental methods and have performed well in identifying miRNAs. Compared to mature miRNAs, precursor miRNAs (pre-miRNAs) are long and have a hairpin loop structure with structural features. Therefore, most in-silico tools are implemented for the pre-miRNAs identification. This study presents a multilayer perceptron (MLP) based classifier implemented using 180 features under sequential, structural, and thermodynamic feature categories for plant pre-miRNA identification. This classifier has a 92% accuracy, 94% specificity, and 90% sensitivity. We have further tested this model with other small non-coding RNA types and obtained 78% accuracy. Furthermore, we introduce a novel dataset to train and test machine learning models, addressing the overlapping data issue in positive training and testing datasets presented in PlantMiRNAPred, a study done by Xuan et al. for the classification of real and pseudo plant pre-miRNAs. The new dataset and the classifier are deployed on a web server which is freely accessible via http://mirnafinder.shyaman.me/.

Salient Features, data and algorithms for microRNA screening from plants: A review on the gains and pitfalls of machine learning techniques

: The era of Big Data and high-throughput genomic technology has enabled scientists to have a clear view of plant genomic profiles. However, it has also led to a massive need of computational tools and strategies to interpret this data. In this scenario of huge data inflow, machine learning (ML) approaches are emerging to be the most promising for analysing heterogeneous and unstructured biological datasets. Extending its application to healthcare and agriculture, ML approaches are being useful for microRNA (miRNA) screening as well. Identification of miRNAs is a crucial step towards understanding post-transcriptional gene regulation and miRNA-related pathology. The use of ML tools is becoming indispensable in analysing such data and identifying species-specific, non-conserved miRNA. However, these techniques have their own benefits and lacunas. In this review, we discuss the current scenario and pitfalls of ML based tools for plant miRNA identification and provide some insights into the important features, the need for deep learning models and direction in which studies are needed.

Computational methods for the ab initio identification of novel microRNA in plants: a systematic review

Background MicroRNAs (miRNAs) play a vital role as post-transcriptional regulators in gene expression. Experimental determination of miRNA sequence and structure is both expensive and time consuming. The next-generation sequencing revolution, which facilitated the rapid accumulation of biological data has brought biology into the “big data” domain. As such, developing computational methods to predict miRNAs has become an active area of inter-disciplinary research. Objective The objective of this systematic review is to focus on the developments of ab initio plant miRNA identification methods over the last decade. Data sources Five databases were searched for relevant articles, according to a well-defined review protocol. Study selection The search results were further filtered using the selection criteria that only included studies on novel plant miRNA identification using machine learning. Data extraction Relevant data from each study were extracted in order to carry out an analysis on their methodologies and findings. Results Results depict that in the last decade, there were 20 articles published on novel miRNA identification methods in plants of which only 11 of them were primarily focused on plant microRNA identification. Our findings suggest a need for more stringent plant-focused miRNA identification studies. Conclusion Overall, the study accuracies are of a satisfactory level, although they may generate a considerable number of false negatives. In future, attention must be paid to the biological plausibility of computationally identified miRNAs to prevent further propagation of biologically questionable miRNA sequences.