کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8649144 | 1570835 | 2018 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Genome-wide analysis of nucleotide binding site-leucine-rich repeats (NBS-LRR) disease resistance genes in Gossypium hirsutum
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
دانش گیاه شناسی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Nucleotide binding site-leucine-rich repeats (NBS-LRR) disease resistance genes (R genes) are the main class of R genes in plants. In order to fully understand the structure characteristic and composition of NBS-LRR R genes in upland cotton, and further reveal their roles in against to various pathogens, all NBS-LRR R genes in upland cotton were gathered, then their physical located distribution on chromosomes were analyzed. Next, all these upland cotton NBS-LRR R genes were performed by homologous comparison analysis compared with Arabidopsis thaliana (A. thaliana) NBS-LRR R genes. The results showed that cotton NBS-LRR R genes are distributed on 26 chromosomes with clustered or single permutation characters. These R genes can be divided into two subfamilies of group CC-NBS-LRR (315 genes) and TIR-NBS-LRR (122 genes). The CC-NBS-LRR subtype can be further classified into five groups: A, B, C, D and NL-A. The TIR-NBS-LRR subtype contains seven groups: B, C, D, E, G, TN-A and putative genes. Some cotton NBS-LRR R genes have homologous genes with those of A. thaliana, but most of them presented obvious differences compared with Arabidopsis NBS-LRR R genes. The basic information on the classification of cotton NBS-LRR R genes would be helpful for further ongoing their functional exploration and finally revealing their roles in against to various pathogens.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physiological and Molecular Plant Pathology - Volume 104, December 2018, Pages 1-8
Journal: Physiological and Molecular Plant Pathology - Volume 104, December 2018, Pages 1-8
نویسندگان
Jinyan Shi, Mingzhen Zhang, Weibo Zhai, Jing Meng, Huan Gao, Wenwei Zhang, Rong Han, Fangjun Qi,