Comparative Mitochondrial Genomes of Curcuma longa and Curcuma kwangsiensis
by Yunyi Zhou·Updated 2mo ago
15.9 KB1files
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Description
Complete mitochondrial genome assemblies for the medicinal plants Curcuma longa and Curcuma kwangsiensis, revealing structural and evolutionary features. The dataset includes analyses of genome size, gene content, codon usage, RNA editing, and phylogenetic relationships. Yunyi Zhou published the data on figshare in April 2026.
Use Cases
Comparative analysis of mitochondrial genome structure based on reported genome sizes of 7.66 Mb and 7.96 Mb.
Studying gene conservation and pseudogenization based on the 39 core protein-coding genes and the pseudogenization of sdh3 and rpl10.
Analyzing evolutionary selection pressures based on reported purifying and episodic positive selection on specific genes like atp8 and nad4.
Investigating cross-organelle gene transfer based on the 25 incorporated plastid-derived fragments.
Phylogenetic reconstruction within Curcuma based on the reported monophyletic clade with 100 bootstrap support.
Strengths
Includes complete mitochondrial genome assemblies for two species with specific sizes: 7.66 Mb for C. longa and 7.96 Mb for C. kwangsiensis.
Reports detailed genomic features such as conserved GC content (43.7–43.9%) and 39 core protein-coding genes.
Provides comparative metrics like sequence identity within conserved blocks (97.7%) and a phylogenetic substitution rate of 0.004 per site.
Limitations
Row count and column-level documentation are absent; field semantics must be inferred after download.
The dataset is small (15.9 KB), suggesting it contains summary or analysis results rather than raw sequence data.
Description metadata is limited; actual data quality and structure require manual inspection after download.
Provenance
Source
figshare, author Yunyi Zhou.
Collection Method
Sequencing and assembly of complete mitochondrial genomes, followed by comparative genomic analysis.
Freshness
Last updated 2026-04-15 05:47:05.
Data is provided in XLSX format; analysis likely requires bioinformatics or statistical software.