Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Bacteria

ABSTRACT: Bacterial microbiome data (16S sequences) Raw sequence reads

PROVIDER: PRJNA633179 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR11817700_1.fastq.gz	Fastqsanger.gz
	SRR11817700_2.fastq.gz	Fastqsanger.gz
	SRR11817800_1.fastq.gz	Fastqsanger.gz
	SRR11817800_2.fastq.gz	Fastqsanger.gz
	SRR11817701_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 238

Similar Datasets

Bacteria

Project description:Bacterial 16S Ribosomal RNA data Raw sequence reads

| PRJNA534433 | ENA

Bacteria

Project description:Bacterial 16S rRNA gene sequences extracted from lamb respiratory samples. Raw sequence reads

| PRJNA317719 | ENA

Morus alba Raw protein sequence reads

Project description:Morus alba L. Raw protein sequence reads and sequence

2024-02-08 | PXD049451 |

Bacteria

Project description:Detection of multiple bacterial species (16S Metagenomics) Raw sequence reads

| PRJNA472453 | ENA

Bacteria

Project description:16S bacterial amplicons from biological soil crust samples Raw sequence reads

| PRJNA807621 | ENA

Metaproteomic analysis of the infant fecal microbiome

Project description:A metaproteomics analysis was conducted on the infant fecal microbiome to characterize global protein expression in 8 samples obtained from infants with a range of early-life experiences. Samples included breast-, formula- or mixed-fed, mode of delivery, and antibiotic treatment and one set of monozygotic twins. Although label-free mass spectrometry-based proteomics is routinely used for the identification and quantification of thousands of proteins in complex samples, the metaproteomic analysis of the gut microbiome presents particular technical challenges. Among them: the extreme complexity and dynamic range of member taxa/species, the need for matched, well-annotated metagenomics databases, and the high inter-protein sequence redundancy/similarity between related members. In this study, a metaproteomic approach was developed for assessment of the biological phenotype and functioning, as a complement to 16S rRNA sequencing analysis to identify constituent taxa. A sample preparation method was developed for recovery and lysis of bacterial cells, followed by trypsin digestion, and pre-fractionation using Strong Cation Exchange chromatography. Samples were then subjected to high performance LC-MS/MS. Data was searched against the Human Microbiome Project database, and a homology-based meta-clustering strategy was used to combine peptides from multiple species into representative proteins. Bacterial taxonomies were also identified, based on species-specific protein sequences, and protein metaclusters were assigned to pathways and functional groups. The results obtained demonstrate the applicability of this approach for performing qualitative comparisons of human fecal microbiome composition, physiology and metabolism, and also provided a more detailed assessment of microbial composition in comparison to 16S rRNA.

2019-03-29 | PXD006033 | Pride

Project description:16S sequences from patient's microbiome Raw sequence reads

| PRJNA1124986 | ENA

Bacteria

Project description:Bacterial 16S rRNA gene sequences extracted from sheep exhaled breathe condensate and lung brushing samples with controls. Raw sequence reads

| PRJNA337937 | ENA

Bacteria

Project description:soil bacterial Raw sequence reads

| PRJNA891063 | ENA

Bacteria

Project description:Bacterial isolate raw sequence reads

| PRJNA1037062 | ENA