Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data. (25th January 2021)

• Record Type: Journal Article
• Authors: Fu, Yuhua; Fan, Pengyu; Wang, Lu; Shu, Ziqiang; Zhu, Shilin; Feng, Siyuan; Li, Xinyun; Qiu, Xiaotian; Zhao, Shuhong; Liu, Xiaolei
• Is Part Of: Journal of animal science. Volume 99:Number 2(2021)
• Publication Date: 2021-01-25
• Subjects: high-throughput sequencing -- identification -- miRNA -- pig -- target prediction; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skab018
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs ( Sus scrofa ). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.