Abstract
Castanopsis mekongensis is highly valued in the furniture industry for its good wood texture, an endemic species in Yunnan province, southwest China. In our study, the chloroplast genome of C. mekongensis was assembled and characterized based on Illumina pair-end sequencing data. The total genome size of C. mekongensis was 160,699 bp, displayed a typical quadripartite structure including a pair of inverted repeat (IRs, 25,714 bp) regions separated by a large single-copy (LSC, 90,278 bp) region and a small single-copy (SSC, 18,963 bp) region. The overall guanine–cytosine (GC) content was 36.8%. We annotated 130 genes in the genome, containing 85 protein-coding genes, 37 tRNA genes, and eight rRNA genes, 12 genes contain a single intron, and two genes have two introns. The result of phylogenetic analysis based on maximum-likelihood (ML) phylogenetic tree indicated that C. mekongensis was most closely related to C. hainanensis.
Highlights
Castanopsis mekongensis is highly valued in the furniture industry for its good wood texture, an endemic species in Yunnan province, southwest China
Castanopsis mekongensis is an evergreen plant that belongs to the family Fagaceae, which is mainly distributed in Yunnan province, southwest China (Xia and Tong 1998)
We reported the complete chloroplast genome sequence of C. mekongensis employing highthroughput sequencing
Summary
Castanopsis mekongensis is highly valued in the furniture industry for its good wood texture, an endemic species in Yunnan province, southwest China. Castanopsis mekongensis is an evergreen plant that belongs to the family Fagaceae, which is mainly distributed in Yunnan province, southwest China (Xia and Tong 1998). To better develop effective protecting strategies, we should understand the phylogenetic relationships between C. mekongensis and other Castanopsis species in the first step. Due to its stable structure and uniparental inheritance, the chloroplast genome is extremely useful for studying the origin and evolution of plants at different taxonomic levels (OsunaMascaro et al 2018).
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