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Simple sequence repeats (SSRs) markers in fish genomic research and their acceleration via next-generation sequencing and computational approaches

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Abstract

Simple sequence repeats (SSRs) are becoming a choice of markers in fish genetic research due to their abundance in the genome, co-dominant nature, high polymorphism and ability to reproduce. Thus, in this review, we have discussed regarding SSRs markers developed in fishes using different techniques. These markers have been used for revealing genetic variability, strain and species identification, genetic linkage map construction and parentage assignment in fish genetic research. Recently, high-throughput sequencing platform has been widely used in non-model fishes for genome/transcriptome sequencing to understand genomic information. The rapid progress in fish genomic research has been made due to sequencing platform along with their low cost for sequencing and use of the advanced computational tools for generated data analysis. We have shown that different next-generation sequencing platforms have been applied in the genomic studies for SSRs markers identification in fishes with evidence. We have depicted the use of various computational tools/algorithms for the SSRs identification from genome/transcriptome data. However, we also highlighted existing challenges in high-throughput sequencing data analysis as well as the current need of computationally deep analysis software/tools/expertise. The purpose of this review is to get envisage on the various possibilities, which can be harnessed via these new technologies and advanced computational tools for SSRs marker development via genome/transcriptome sequencing of aquaculture species.

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The work was supported by the Indian Council of Agricultural Research, Ministry of Agriculture, Government of India.

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Correspondence to Kiran Dashrath Rasal.

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Sundaray, J.K., Rasal, K.D., Chakrapani, V. et al. Simple sequence repeats (SSRs) markers in fish genomic research and their acceleration via next-generation sequencing and computational approaches. Aquacult Int 24, 1089–1102 (2016). https://doi.org/10.1007/s10499-016-9973-4

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