miReader: Discovering Novel miRNAs in Species without Sequenced Genome

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.与计算方法相辅相成，下一代测序（Next Generation Sequencing, NGS）相关技术对微小RNA（microRNA, miRNA）生物学领域的研究发现产生了重大影响，其中包括新型微小RNA的鉴定工作。然而时至今日，所有微小RNA鉴定工具均必须依赖参考基因组或基因组序列的获取。本研究首次提出了一种全新方法miReader，该方法无需依赖基因组/参考序列即可鉴定新型微小RNA。该方法利用下一代测序读段数据构建高精度微小RNA模型，其核心为以最佳优先树（Best-First Tree）作为基础分类器的多提升（Multi-boosting）算法。该方法已通过涵盖人类、植物、线虫、斑马鱼以及果蝇等多个物种的海量实验数据进行了全面测试，始终保持90%以上的准确率。利用该工具对未进行基因组测序的植物芒草（Miscanthus）的Illumina读段数据进行分析，本研究成功鉴定出21个新型成熟微小RNA双链候选体。考虑到微小RNA鉴定工作需要处理高通量数据，本研究将整套方法实现为独立的并行架构工具。本研究有望为多数物种的微小RNA鉴定领域带来积极影响，使得基因组序列的获取不再成为必要条件。