标题： MirLibSpark：用于多库功能注释的可扩展NGS植物微RNA预测流程 显示英文标题

标题： MirLibSpark: A Scalable NGS Plant MicroRNA Prediction Pipeline for Multi-Library Functional Annotation

摘要： 下一代测序技术的出现极大地增加了转录组数据的规模。 尽管已经提出了许多用于新型微小RNA（miRNA）预测的独立算法和工作流程，但很少有针对处理来自大基因组的大规模序列数据而设计的，甚至更少的工具能够通过分析多个文库进一步注释功能性的miRNA。 我们提出了一种改进的管道，以实现高体积数据设施的处理，该管道基于Apache Spark框架实现了mirLibSpark。 该管道是目前最快的实际方法，并且与标准相比提供了准确性提升。 在本文中，我们提供了一个独立且完全自动化的分布式功能性miRNA预测器。 它是一个高效且准确的miRNA预测器，并具有功能洞察力。 此外，它符合植物miRNA预测的黄金标准要求。 显示英文摘要

摘要： The emergence of the Next Generation Sequencing increases drastically the volume of transcriptomic data. Although many standalone algorithms and workflows for novel microRNA (miRNA) prediction have been proposed, few are designed for processing large volume of sequence data from large genomes, and even fewer further annotate functional miRNAs by analyzing multiple libraries. We propose an improved pipeline for a high volume data facility by implementing mirLibSpark based on the Apache Spark framework. This pipeline is the fastest actual method, and provides an accuracy improvement compared to the standard. In this paper, we deliver the first distributed functional miRNA predictor as a standalone and fully automated package. It is an efficient and accurate miRNA predictor with functional insight. Furthermore, it compiles with the gold-standard requirement on plant miRNA predictions.