Computational Methods and Online Resources for Identification of piRNA-Related Molecules

• Published in: Interdisciplinary sciences : computational life sciences Vol. 13; no. 2; pp. 176 - 191
• Main Authors: Liu, Yajun, Li, Aimin, Xie, Guo, Liu, Guangming, Hei, Xinhong
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.06.2021; Springer Nature B.V
• Subjects: Annotations; Bioinformatics; Biomedical and Life Sciences; Clusters; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Computer applications; Datasets; Health Sciences; Internet resources; Life Sciences; Mathematical and Computational Physics; Medicine; Molecular modelling; Non-coding RNA; Organisms; Review; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; PiRNA; Identification methods; Databases
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-021-00428-5

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.