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

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-relat...

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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
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Abstract	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.
AbstractList	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 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 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.
Author	Liu, Guangming Xie, Guo Hei, Xinhong Liu, Yajun Li, Aimin
Author_xml	– sequence: 1 givenname: Yajun orcidid: 0000-0002-5113-4709 surname: Liu fullname: Liu, Yajun email: liuyajun@xaut.edu.cn organization: Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology – sequence: 2 givenname: Aimin surname: Li fullname: Li, Aimin organization: Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology – sequence: 3 givenname: Guo surname: Xie fullname: Xie, Guo organization: Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, School of Information Technology and Equipment Engineering, Xi’an University of Technology – sequence: 4 givenname: Guangming surname: Liu fullname: Liu, Guangming organization: Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology – sequence: 5 givenname: Xinhong surname: Hei fullname: Hei, Xinhong organization: Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology
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Snippet	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...
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SubjectTerms	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
Title	Computational Methods and Online Resources for Identification of piRNA-Related Molecules
URI	https://link.springer.com/article/10.1007/s12539-021-00428-5 https://www.ncbi.nlm.nih.gov/pubmed/33886096 https://www.proquest.com/docview/2533762008 https://www.proquest.com/docview/2516843993
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