LAND-USE_LTO_2014
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR494109.fastq.gz | Fastqsanger.gz | |||
| ERR494110.fastq.gz | Fastqsanger.gz | |||
| ERR494111.fastq.gz | Fastqsanger.gz | |||
| ERR494112.fastq.gz | Fastqsanger.gz | |||
| ERR494113.fastq.gz | Fastqsanger.gz |
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Project description:Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems and perhaps more important is the insensitivity of the biological end points that we have used to assess these impacts. In this paper we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land use practices in the surrounding watershed using advanced machine learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data shows that gill tissues are far more responsive and provide superior discrimination of land use classes than hepatopancreas and that transcript encoding proteins involved in energy productions, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P450. Keywords: Comparative genomics, ecogenomics. Tissue differences, impacts of land use and contaminants on gene expression. Oysters were collected from 11 tidal creeks in Georgia, South Carolina and North Carolina at sites variously impacted by human development. A total of 267 individuals were examined for gene expression profiles in gill and hepatopancreas tissues for a total of 534 arrays. The data were filtered though standard tools and ultimately analyzed using advance machine learning techniques.
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