Center for Systems Biology, Soochow University, Suzhou 215006, China.
Nucleic Acids Res. 2012 May;40(10):4298-305. doi: 10.1093/nar/gks043. Epub 2012 Jan 28.
With the development of next-generation sequencing (NGS) techniques, many software tools have emerged for the discovery of novel microRNAs (miRNAs) and for analyzing the miRNAs expression profiles. An overall evaluation of these diverse software tools is lacking. In this study, we evaluated eight software tools based on their common feature and key algorithms. Three deep-sequencing data sets were collected from different species and used to assess the computational time, sensitivity and accuracy of detecting known miRNAs as well as their capacity for predicting novel miRNAs. Our results provide useful information for researchers to facilitate their selection of the optimal software tools for miRNA analysis depending on their specific requirements, i.e. novel miRNAs discovery or miRNA expression profile analysis of sequencing data sets.
随着下一代测序 (NGS) 技术的发展,涌现出许多用于发现新的 microRNAs (miRNAs) 和分析 miRNAs 表达谱的软件工具。然而,这些不同软件工具的综合评估仍然缺乏。在这项研究中,我们基于它们的常见特征和关键算法评估了八种软件工具。从不同物种中收集了三个深度测序数据集,用于评估检测已知 miRNAs 的计算时间、灵敏度和准确性,以及它们预测新 miRNAs 的能力。我们的研究结果为研究人员提供了有用的信息,以便根据他们的特定需求(即新 miRNAs 的发现或测序数据集的 miRNAs 表达谱分析),为他们选择最佳的 miRNA 分析软件工具提供便利。