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Computational identification of miRNAs, their targets and functions in three-spined stickleback (Gasterosteus aculeatus)
Chaturvedi, A; Raeymaekers, M; Volckaert, M (2014). Computational identification of miRNAs, their targets and functions in three-spined stickleback (Gasterosteus aculeatus). Mol. Ecol. Resour. 14(4): 768-777.
In: Molecular Ecology Resources. Blackwell Publishing: Oxford. ISSN 1755-098X, more
Peer reviewed article  

Available in  Authors 

    Gasterosteus aculeatus Linnaeus, 1758 [WoRMS]
Author keywords
    database; evolution; Gasterosteus aculeatus; microRNA; targets;three-spined stickleback

Authors  Top 
  • Chaturvedi, A
  • Raeymaekers, M
  • Volckaert, M

    An intriguing question in biology is how the evolution of gene regulation is shaped by natural selection in natural populations. Among the many known regulatory mechanisms, regulation of gene expression by microRNAs (miRNAs) is of critical importance. However, our understanding of their evolution in natural populations is limited. Studying the role of miRNAs in three-spined stickleback, an important natural model for speciation research, may provide new insights into adaptive polymorphisms. However, lack of annotation of miRNA genes in its genome is a bottleneck. To fill this research gap, we used the genome of three-spined stickleback to predict miRNAs and their targets. We predicted 1486 mature miRNAs using the homology-based miRNA prediction approach. We then performed functional annotation and enrichment analysis of these targets, which identified over-represented motifs. Further, a database resource (GAmiRdb) has been developed for dynamically searching miRNAs and their targets exclusively in three-spined stickleback. Finally, the database was used in two case studies focusing on freshwater adaptation in natural populations. In the first study, we found 44 genomic regions overlapping with predicted miRNA targets. In the second study, we identified two SNPs altering the MRE seed site of sperm-specific glyceraldehyde-3-phosphate gene. These findings highlight the importance of the GAmiRdb knowledge base in understanding adaptive evolution.

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