Fu Xiaonan, Dong Daoyuan
Department of Biochemistry, Virginia Tech, Blacksburg, VA, USA.
Department of Chemistry and Biochemistry, University of the Sciences, Philadelphia, PA, USA.
Methods Mol Biol. 2018;1751:109-125. doi: 10.1007/978-1-4939-7710-9_8.
The vital role of microRNAs (miRNAs) involved in gene expression regulation has been confirmed in many biological processes. With the growing power and reducing cost of next-generation sequencing, more and more researchers turn to apply this high-throughput method to solve their biological problems. For miRNAs with known sequences, their expression profiles can be generated from the sequencing data. It also allows us to identify some novel miRNAs and explore the sequence variations under different conditions. Currently, there are a handful of tools available to analyze the miRNA sequencing data with separated or combined features, such as reads preprocessing, mapping and differential expression analysis. However, to our knowledge, a hands-on guideline for miRNA sequencing data analysis covering all steps is not available. Here we will utilize a set of published tools to perform the miRNA analysis with detailed explanation. Particularly, the miRNA target prediction and annotation may provide useful information for further experimental verification.
微小RNA(miRNA)在基因表达调控中所起的关键作用已在许多生物学过程中得到证实。随着新一代测序技术的功能日益强大且成本不断降低,越来越多的研究人员开始转向应用这种高通量方法来解决他们的生物学问题。对于已知序列的miRNA,可以从测序数据中生成它们的表达谱。这也使我们能够识别一些新的miRNA,并探索不同条件下的序列变异。目前,有一些工具可用于分析具有分离或组合特征的miRNA测序数据,例如 reads 预处理、映射和差异表达分析。然而,据我们所知,尚无涵盖所有步骤的miRNA测序数据分析实用指南。在此,我们将利用一组已发表的工具,对miRNA分析进行详细解释。特别是,miRNA靶标预测和注释可能为进一步的实验验证提供有用信息。