miReader: Discovering Novel miRNAs in Species without Sequenced Genome

Abstract

Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences. The approach used NGS read data to build highly accurate miRNA models, molded through a Multi-boosting algorithm with Best-First Tree as its base classifier. It was comprehensively tested over large amount of experimental data from wide range of species including human, plants, nematode, zebrafish and fruit fly, performing consistently with >90% accuracy. Using the same tool over Illumina read data for Miscanthus, a plant whose genome is not sequenced; the study reported 21 novel mature miRNA duplex candidates. Considering the fact that miRNA discovery requires handling of high throughput data, the entire approach has been implemented in a standalone parallel architecture. This work is expected to cause a positive impact over the area of miRNA discovery in majority of species, where genomic sequence availability would not be a compulsion any more.

In-silico identification of miRNAs and their regulating target functions in Ocimum basilicum

“…Wang et al, 2004;Adai et al, 2005). Majority of bioinformatics tool available for miRNA prediction requires genome sequence or next generation sequencing data of plants (Wu et al, 2011;Yang and Li, 2011;Xie et al, 2012;Jha and Shankar, 2013). Deficiency of the genome sequences works as a restrictive aspect to predict miRNAs and targets in medicinal plants.…”

Newly identified miRNAs may contribute to aerenchyma formation in sugarcane roots

“…Several publications reported the identification of miRNAs in biofuel crops such as Miscanthus giganteus (Jha & Shankar, ), Setaria italica (Yadav, Muthamilarasan, Pandey, Khan, & Prasad, ; Yi, Xie, Liu, Qi, & Yu, ), Sorghum bicolor (Calviño, Bruggmann, & Messing, ), Z. mays (Aravind et al, ; Liu et al, ), and Saccharum sp. (Gentile, Dias, Mattos, Ferreira, & Menossi, and references therein; Li et al, ; Thiebaut et al, ; Su et al, ; Yang et al, ).…”

BrumiR: A toolkit for de novo discovery of microRNAs from sRNA-seq data

“…On the other hand, new methods such as miReader (23), MirPlex (24), and mirnovo (14), in particular using machine-learning approaches, were specifically developed as an alternative to discover miRNAs in species without a reference genome. In the case of mirnovo, the initial step involves the clustering of the sRNA-seq reads performing an all-vs-all read comparison that is followed by a subsequent classification of the clusters into putative miRNAs using pre-trained models.…”