病毒病是为害苹果的重要致病因子，开展病毒种类鉴定是防控苹果病毒病的首要任务。由于苹果长期通过嫁接的方式进行繁育，导致树体感染多种病毒，加之苹果病毒的浓度较低，序列变异较大，致使苹果病毒种类的鉴定尤为困难。此外，现有的分子检测技术主要为聚合酶链式反应和分子杂交技术，这些技术基于已知病毒核苷酸序列为基础开展鉴定，而对于未知病毒，由于其核苷酸序列尚未测定，则无法采用这些分子技术进行鉴定。针对这一瓶颈，该文提供了一个自然生长的苹果树的small RNA（sRNA）深度测序的数据集，通过结合生物信息学方法对原始数据进行过滤、sRNA组装及Blast比对，可准确地分析和判断自然条件下病毒的种类，为苹果病毒的鉴定带来了新的突破，同时也为苹果无毒苗木的判定提供技术支撑。

Virus disease is an important pathogenic factor of apple. Viral species identification plays the most important role in the prevention and control of apple virus diseases. Fruit trees are generally propagated through a-long-period grafting, leading to the plants are easily infected by a variety of viruses. Moreover, the viruses infecting fruit trees always remain a low concentration and a big sequence variation, thus it is particularly difficult to be identified. Additionally, the known molecular protocols majorly include PCR and molecular hybridization, both of which are available to identify known viruses based on their nucleic acid sequences while not for those unreported viruses with their sequences still unknown. In order to break this bottleneck, this study provided a dataset obtained by deep sequencing of small RNA (sRNA) from a naturally cultivated apple trees, combined with bioinformatics methods to filter original data, assembled sRNAs into contigs, and subjected to Blast analysis of the resulting contigs with the sequences in NCBI to analyze and determine virus species, which should bring a new breakthrough for the identification of apple viruses, and provide a technological support to determinate virus-free apple seedlings.