April 05, 2021

Interpretive Summary: Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

Interpretive Summary: Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

By Anne Zinn

Since their discovery, microRNAs have played an important role in the study of the molecular function in pigs, but, despite the broad variety of available microRNA research tools and methods, their application to the identification, annotation, and target prediction of microRNAs in nonmodel organisms is limited. A recent study published in the Journal of Animal Science collected nearly all public sRNA-seq data to sequence and to improve the annotation for known microRNAs and identify novel microRNAs that have not yet been annotated in pigs. This study attempted to solve the common problems of microRNA identification and target prediction in a non-model organism, Sus scrofa, using massive public sRNA/RNA sequencing data The research team predicted that the target genes for these microRNAs using a conjoint analysis with a large amount of RNA-seq data, which demonstrated huge potential values of massive public data to mine of non-model biological functions.

Using publicly available sRNA-seq data for pigs, the present study improved the annotation of currently known miRNAs in pigs and pointed out some potential mistakes in miRBase as well as revealed current deficiencies in microRNA annotation. In addition, the research identified 811 novel microRNAs using sequence homology, positional homology, and detectable rate, which was about twice the number of known miRNAs; this has enriched the current miRNA annotations in pigs. This research method not only provided expression-level evidence for traditional methods, but also identified some targeting relationships in the non binding site pattern.

Overall, the present study demonstrated the comprehensive application potential of massive public data in microRNA annotation, identification, and target prediction in pigs and provides a new way of thinking about the application of public data in non-model organisms.

The full paper can be found on the Journal of Animal Science website.