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Computational identification of mi RNA s, their targets and functions in three‐spined stickleback ( G asterosteus aculeatus )
Author(s) -
Chaturvedi Anurag,
Raeymaekers Joost A. M.,
Volckaert Filip A. M.
Publication year - 2014
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.12223
Subject(s) - biology , stickleback , three spined stickleback , rna , gene , genome , computational biology , genetics , evolutionary biology , fish <actinopterygii> , fishery
Abstract 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 micro RNA s (mi RNA s) is of critical importance. However, our understanding of their evolution in natural populations is limited. Studying the role of mi RNA s in three‐spined stickleback, an important natural model for speciation research, may provide new insights into adaptive polymorphisms. However, lack of annotation of mi RNA genes in its genome is a bottleneck. To fill this research gap, we used the genome of three‐spined stickleback to predict mi RNA s and their targets. We predicted 1486 mature mi RNA s using the homology‐based mi RNA prediction approach. We then performed functional annotation and enrichment analysis of these targets, which identified over‐represented motifs. Further, a database resource ( GA mi R db) has been developed for dynamically searching mi RNA s 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 mi RNA targets. In the second study, we identified two SNP s altering the MRE seed site of sperm‐specific glyceraldehyde‐3‐phosphate gene. These findings highlight the importance of the GA mi R db knowledge base in understanding adaptive evolution.