De Novo Transcriptomic Analysis of Cowpea (Vigna Unguiculata L. Walp.) for Genic SSR Marker Development

Overview

Journal BMC Genet

Publisher Biomed Central

Specialties Biotechnology
Molecular Biology

Date 2017 Jul 12

PMID 28693419

Citations 14

Authors

Honglin Chen

Lixia Wang

Xiaoyan Liu

Liangliang Hu

Suhua Wang

Xuzhen Cheng

Affiliations

Soon will be listed here.

Abstract

Background: Cowpea [Vigna unguiculata (L.) Walp.] is one of the most important legumes in tropical and semi-arid regions. However, there is relatively little genomic information available for genetic research on and breeding of cowpea. The objectives of this study were to analyse the cowpea transcriptome and develop genic molecular markers for future genetic studies of this genus.

Results: Approximately 54 million high-quality cDNA sequence reads were obtained from cowpea based on Illumina paired-end sequencing technology and were de novo assembled to generate 47,899 unigenes with an N50 length of 1534 bp. Sequence similarity analysis revealed 36,289 unigenes (75.8%) with significant similarity to known proteins in the non-redundant (Nr) protein database, 23,471 unigenes (49.0%) with BLAST hits in the Swiss-Prot database, and 20,654 unigenes (43.1%) with high similarity in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Further analysis identified 5560 simple sequence repeats (SSRs) as potential genic molecular markers. Validating a random set of 500 SSR markers yielded 54 polymorphic markers among 32 cowpea accessions.

Conclusions: This transcriptomic analysis of cowpea provided a valuable set of genomic data for characterizing genes with important agronomic traits in Vigna unguiculata and a new set of genic SSR markers for further genetic studies and breeding in cowpea and related Vigna species.

Citing Articles

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De Novo Transcriptome Assembly of Rice Bean () and Characterization of WRKY Transcription Factors Response to Aluminum Stress.

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Expression Profiles of Microsatellites in Fruit Tissues of and Development of Efficient EST-SSR Markers.

Chen W, Yang H, Zhong S, Zhu J, Zhang Q, Li Z Genes (Basel). 2022; 13(8).

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