Dataset Information

Dysidea avara combined omics

ABSTRACT: Dysidea avara bacterial metagenomes and whole-tissue metatranscriptomes

PROVIDER: PRJEB24806 | ENA |

REPOSITORIES: ENA

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Dataset's files

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			Action	DRS
	ERR2283023_1.fastq.gz	Fastqsanger.gz
	ERR2283023_2.fastq.gz	Fastqsanger.gz
	ERR2283024_1.fastq.gz	Fastqsanger.gz
	ERR2283024_2.fastq.gz	Fastqsanger.gz
	ERR2283025_1.fastq.gz	Fastqsanger.gz

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Project description:Characterization of microbial communities at the genomic, transcriptomic, proteomic and metabolomic levels, with a special interest on lipid accumulating bacterial populations, which are naturally enriched in biological wastewater treatment systems and may be harnessed for the conversion of mixed lipid substrates (wastewater) into biodiesel. The project aims to elucidate the genetic blueprints and the functional relevance of specific populations within the community. It focuses on within-population genetic and functional heterogeneity, trying to understand how fine-scale variations contribute to differing lipid accumulating phenotypes. Insights from this project will contribute to the understanding the functioning of microbial ecosystems, and improve optimization and modeling strategies for current and future biological wastewater treatment processes. This project contains datasets derived from the same biological wastewater treatment plant. The data includes metagenomes, metatranscriptomes, metaproteomes and organisms isolated in pure cultures. Characterization of microbial communities at the genomic, transcriptomic, proteomic and metabolomic levels, with a special interest on lipid accumulating bacterial populations, which are naturally enriched in biological wastewater treatment systems and may be harnessed for the conversion of mixed lipid substrates (wastewater) into biodiesel. The project aims to elucidate the genetic blueprints and the functional relevance of specific populations within the community. It focuses on within-population genetic and functional heterogeneity, trying to understand how fine-scale variations contribute to differing lipid accumulating phenotypes. Insights from this project will contribute to the understanding the functioning of microbial ecosystems, and improve optimization and modeling strategies for current and future biological wastewater treatment processes. This project contains datasets derived from the same biological wastewater treatment plant. The data includes metagenomes, metatranscriptomes, metaproteomes and organisms isolated in pure cultures.

Dataset Information

Dysidea avara combined omics

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