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

Abstract

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.

Database of Potential Promoter Sequences in the Capsicum annuum Genome

“…Cumulatively, our studies indicate that classification of promoter sequences makes it possible to identify about 5–30 times more PPSs compared to the number of promoter sequences near already annotated genes. The reason for such a large difference in the number of actual promoters and PPSs could lie in that there are unannotated genes, such as those encoding microRNAs, which are also transcribed by RNA polymerase II [ 41 , 42 , 43 ]. This notion is supported by the results obtained using the TSSFinder program, which found TSSs in over 50% of the PPSs identified with MAHDS.…”

Machine learning for plant microRNA prediction: A systematic review

“…This section describes the features used to develop machine learning models. The principles of the machine learning approach for miRNA or miRNA precursor (pre-miRNA) discovery are based on three major characteristics (features) of miRNAs or pre-miRNAs; structural, thermodynamical, and sequence-based features [47]. Features such as hairpin length, hairpin-loop length, bulge size and location, base-pairing, minimum free energy (mfe), triplet elements, and distance of the miRNA from the loop of the hairpin precursor are under structural features.…”