Abstract
Current progress in high-throughput sequencing has opened up avenues to produce massive quantities of sequencing data from non-model fishes at an affordable cost. Thus, data analysis is also evolving at a rapid pace because of cutting edge computational tools. With the development and availability of experimental technologies and computational approaches, the field of MicroRNA (miRNA) biology has advanced over the last decade. MicroRNAs can play an important role in gene modulation via post-transcriptional gene regulation during acclimation and adaptation, in case of adverse conditions or climate change for example. These are useful and substantial contributors to regulatory networks of development and adaptive plasticity in fishes. Next generation sequencing technologies have extensively been used for solving biological questions in non-model fishes, where data pertaining to genome or transcriptome are either scant or totally unavailable. The data generated through this process have been used for gene discovery, variant identification, marker discovery and miRNA identification. Here, we discuss the role of miRNA in gene regulation pertaining to fish and its investigation via sequencing platforms, as well as the current use of computational algorithms for miRNA analysis. The purpose of this review is to examine the use of miRNA in aquaculture and further to investigate new technologies and advanced computational tools. However, our review also emphasizes existing challenges for miRNA investigations carried out via high-throughput sequencing and the growing demand for computationally intensive analysis software. This work along with assembled information on the known miRNAs in fish species will be useful while undertaking future studies for understanding the role of miRNAs.
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The work was supported by Indian Council of Agricultural Research, Ministry of Agriculture, Government of India.
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Rasal, K.D., Nandanpawar, P.C., Swain, P. et al. MicroRNA in aquaculture fishes: a way forward with high-throughput sequencing and a computational approach. Rev Fish Biol Fisheries 26, 199–212 (2016). https://doi.org/10.1007/s11160-016-9421-6
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DOI: https://doi.org/10.1007/s11160-016-9421-6