Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits

Abstract Tea (Camellia sinensis, (L.) Kuntze) is considered as most popular drink across the world and it is widely consumed beverage for its several health-benefit characteristics. These positive traits primarily rely on its regulatory networks of different metabolic pathways. Development of micros...

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Main Authors: Anjan Hazra, Nirjhar Dasgupta, Chandan Sengupta, Sauren Das
Format: Article
Language:English
Published: BMC 2017-07-01
Series:BMC Research Notes
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13104-017-2577-x
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spelling doaj-431a1b26da7941eb946001dac98076102020-11-25T02:43:18ZengBMCBMC Research Notes1756-05002017-07-011011610.1186/s13104-017-2577-xExtrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traitsAnjan Hazra0Nirjhar Dasgupta1Chandan Sengupta2Sauren Das3Agricultural and Ecological Research Unit, Indian Statistical InstituteAgricultural and Ecological Research Unit, Indian Statistical InstituteDepartment of Botany, University of KalyaniAgricultural and Ecological Research Unit, Indian Statistical InstituteAbstract Tea (Camellia sinensis, (L.) Kuntze) is considered as most popular drink across the world and it is widely consumed beverage for its several health-benefit characteristics. These positive traits primarily rely on its regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions are being worthwhile for reviewing the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea, the trait-specific Simple Sequence Repeat (SSR) markers, leading to be useful in marker assisted breeding technique, are yet to be identified. Micro RNAs are short, non-coding RNA molecules, involved in post transcriptional mode of gene regulation and thus effects on related phenotype. Present study deals with identification of the microsatellite motifs within the reported and predicted miRNA precursors that are effectively followed by designing of primers from SSR flanking regions in order to PCR validation. In addition to the earlier reports, two new miRNAs are predicting here from tea expressed tag sequence database. Furthermore, 18 SSR motifs are found to be in 13 of all 33 predicted miRNAs. Trinucleotide motifs are most abundant among all followed by dinucleotides. Since, miRNA based SSR markers are evidenced to have significant role on genetic fingerprinting study, these outcomes would pave the way in developing novel markers for tagging tea specific agronomic traits as well as substantiating non-conventional breeding program.http://link.springer.com/article/10.1186/s13104-017-2577-xMicro RNASimple sequence repeatsTea qualityTrait specific marker
collection DOAJ
language English
format Article
sources DOAJ
author Anjan Hazra
Nirjhar Dasgupta
Chandan Sengupta
Sauren Das
spellingShingle Anjan Hazra
Nirjhar Dasgupta
Chandan Sengupta
Sauren Das
Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits
BMC Research Notes
Micro RNA
Simple sequence repeats
Tea quality
Trait specific marker
author_facet Anjan Hazra
Nirjhar Dasgupta
Chandan Sengupta
Sauren Das
author_sort Anjan Hazra
title Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits
title_short Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits
title_full Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits
title_fullStr Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits
title_full_unstemmed Extrapolative microRNA precursor based SSR mining from tea EST database in respect to agronomic traits
title_sort extrapolative microrna precursor based ssr mining from tea est database in respect to agronomic traits
publisher BMC
series BMC Research Notes
issn 1756-0500
publishDate 2017-07-01
description Abstract Tea (Camellia sinensis, (L.) Kuntze) is considered as most popular drink across the world and it is widely consumed beverage for its several health-benefit characteristics. These positive traits primarily rely on its regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions are being worthwhile for reviewing the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea, the trait-specific Simple Sequence Repeat (SSR) markers, leading to be useful in marker assisted breeding technique, are yet to be identified. Micro RNAs are short, non-coding RNA molecules, involved in post transcriptional mode of gene regulation and thus effects on related phenotype. Present study deals with identification of the microsatellite motifs within the reported and predicted miRNA precursors that are effectively followed by designing of primers from SSR flanking regions in order to PCR validation. In addition to the earlier reports, two new miRNAs are predicting here from tea expressed tag sequence database. Furthermore, 18 SSR motifs are found to be in 13 of all 33 predicted miRNAs. Trinucleotide motifs are most abundant among all followed by dinucleotides. Since, miRNA based SSR markers are evidenced to have significant role on genetic fingerprinting study, these outcomes would pave the way in developing novel markers for tagging tea specific agronomic traits as well as substantiating non-conventional breeding program.
topic Micro RNA
Simple sequence repeats
Tea quality
Trait specific marker
url http://link.springer.com/article/10.1186/s13104-017-2577-x
work_keys_str_mv AT anjanhazra extrapolativemicrornaprecursorbasedssrminingfromteaestdatabaseinrespecttoagronomictraits
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AT chandansengupta extrapolativemicrornaprecursorbasedssrminingfromteaestdatabaseinrespecttoagronomictraits
AT saurendas extrapolativemicrornaprecursorbasedssrminingfromteaestdatabaseinrespecttoagronomictraits
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