Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1214405 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Cymbopogon spp. are grass species widely used around the world as medicinal plants and in various industries for the production of perfumes and pharmaceuticals. Despite their extensive use, there are relatively few studies at the genomic and transcriptomic levels. In this study, transcriptomic data were generated for two species—Cymbopogon flexuosus and Cymbopogon winterianus—to investigate evolutionary aspects and the expression of genes associated with secondary metabolite biosynthesis. The transcriptome assembly revealed a total of 18,286 and 22,458 transcripts for C. flexuosus and C. winterianus, respectively. Furthermore, the reads were mapped against the genomes of related species, including Andropogon gerardii, Sorghum bicolor, Saccharum officinarum, Miscanthus sinensis, Miscanthus lutarioriparius, and Zea mays, to assess their conservation across these genomes. The results indicated which species are more closely related to the Cymbopogon genus and highlighted key differences in metabolic pathways.
2025-08-30 | GSE306450 | GEO
Project description:Comparative transcriptome mining of five different genotypes of Cymbopogon
| PRJNA874201 | ENA
Project description:De Novo transcriptome of Cymbopogon flexuosus
| PRJNA587392 | ENA
Project description:Cymbopogon martini Transcriptome or Gene expression
| PRJNA727712 | ENA
Project description:PFGRC has developed a cost effective alternative to complete genome sequencing in order to study the genetic differences between closely related species and/or strains. The comparative genomics approach combines Gene Discovery (GD) and Comparative Genomic Hybridization (CGH) techniques, resulting in the design and production of species microarrays that represent the diversity of a species beyond just the sequenced reference strain(s) used in the initial microarray design. These species arrays may then be used to interrogate hundreds of closely related strains in order to further unravel their evolutionary relationships. The Pneumococcus are among most deadly pathogens world-wide. The infections and outbreaks caused by this pathogens is quite frequent despite existing diagnostic network and therapeutic means. Therefore, developing reliable diagnostic tools and efficient (broad-spectrum) therapeutics for Streptococcus pneumoniae remain a public health priority for every country in world today. The comparative genomics study will provide the largest hitherto genomic data sets regarding this pathogen.These large data sets will enable us as well as other members of scientific community to conduct comprehensive data mining in the form of gene association studies with statistical power and significance.
2011-08-01 | GSE29501 | GEO
Project description:PFGRC has developed a cost effective alternative to complete genome sequencing in order to study the genetic differences between closely related species and/or strains. The comparative genomics approach combines Gene Discovery (GD) and Comparative Genomic Hybridization (CGH) techniques, resulting in the design and production of species microarrays that represent the diversity of a species beyond just the sequenced reference strain(s) used in the initial microarray design. These species arrays may then be used to interrogate hundreds of closely related strains in order to further unravel their evolutionary relationships. The Neissiria are among most deadly pathogens world-wide. The infections and outbreaks caused by this pathogens is quite frequent despite existing diagnostic network and therapeutic means. Therefore, developing reliable diagnostic tools and efficient (broad-spectrum) therapeutics for Neisseria meningitidis remain a public health priority for every country in world today. The comparative genomics study will provide the largest hitherto genomic data sets regarding this pathogen.These large data sets will enable us as well as other members of scientific community to conduct comprehensive data mining in the form of gene association studies with statistical power and significance.
2011-07-31 | GSE30813 | GEO