piRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the types of tools for the identification of piRNAs based on five aspects: datasets, features, classifiers, performance, and usability. We found the precision of 2lpiRNApred was the highest in datasets of model organisms, piRNN had a better performance of datasets of non-model organisms, and 2L-piRNA had the fastest recognition speed of all tools. In addition, we presented an overview of piRNA databases. The databases were divided into six categories: basic annotation, comprehensive annotation, isoform, cluster, target, and disease. We found that piRNA data of non-model organisms, piRNA target data, and piRNA–disease-associated data should be strengthened. Our review might assist researchers in selecting appropriate tools or datasets for their studies, reveal potential problems and shed light on future bioinformatics studies.

piRNAs 是一类非编码小分子 RNA，与 PIWI 家族相互作用，具有多种重要且多样的生物学功能。本综述旨在提供指导方针并为 piRNA 研究做出贡献。我们专注于piRNA相关分子的四种识别模型，包括piRNA、piRNA簇、piRNA靶点和疾病相关的piRNA。我们从五个方面评估了用于识别 piRNA 的工具类型：数据集、特征、分类器、性能和可用性。我们发现2lpiRNApred在模式生物数据集中的精度最高，piRNN在非模式生物数据集中表现更好，2L-piRNA在所有工具中识别速度最快。此外，我们还概述了 piRNA 数据库。数据库分为六类：基本注释、综合注释、同种型、簇、目标和疾病。我们发现应该加强非模式生物的piRNA数据、piRNA靶标数据和piRNA-疾病相关数据。我们的评论可能会帮助研究人员为他们的研究选择合适的工具或数据集，揭示潜在的问题并为未来的生物信息学研究提供启示。