Back

Sequence characterization of the complete chloroplast genome of Eupatorium fortunei

yan, k.; Ran, J.; Bao, S.; Zhang, N.; Zhao, W.; Ma, Y.; Ishimwe, M. A.

2022-04-18 bioinformatics
10.1101/2022.04.18.488663 bioRxiv
Show abstract

Eupatorium fortunei has been utilized as herbal medicine to cure various diseases in various regions of the world especially China, Korea, and other Asian countries. The available literature shows Eupatorium fortunei contain anti-metastatic, anti-angiogenic, anti-bacterial, and anti-oxidant, along with compounds to cure human cancer. In present study, Eupatorium fortunei was used as an experimental material, and its genome was sequenced based on high-throughput sequencing technology. We assembled the complete chloroplast genome of Eupatorium fortunei and obtained NCBI accession number (OK545755). We analyzed chloroplast genome structure of Eupatorium fortunei and systematic analysis was conducted, including the size of the entire genome sequence, genome volume, large single-copy (LSC), small single-copy (SSC), inverted repeat region (IR)-LSC/SSC boundaries, etc. The obtained results showed that chloroplast genome of Eupatorium fortunei is a typical tetrad structure with a total length of 152,401 bp and a sum of 133 annotated genes, including eight ribosomal RNA (rRNA)-encoding genes, 37 transfer RNA (tRNA)-encoding genes, 87 protein-encoding genes, and one pseudo-gene. A sum of 29 Single Sequence Repeats (SSR)s were identified for mono-nucleotide, di-nucleotide and tri-nucleotide microsatellites with mono-nucleotides in majority. The phylogenetic analysis revealed that Eupatorium fortunei is more closely related to Litothamnus nitidus and belongs to the same branch. The genome assembly and analysis of the chloroplast genome of Eupatorium fortunei provides more accurate evidence for further taxonomic identification and localization of Asteraceae/Compositae plant.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.