Metagenome-assembled genome: ERR1190760_bin.22_CONCOCT_v1.1_MAG
uncultured Enterococcus sp.
This sample represents a Third Party Annotation (TPA) Metagenome-Assembled Genome (MAG) assembled from the metagenomic run ERR1190760 of study ERP013562.
ENA-FIRST-PUBLIC
2023-01-03
ENA-LAST-UPDATE
2023-01-03
External Id
SAMEA14083348
INSDC center alias
EBI
INSDC center name
European Bioinformatics Institute
INSDC first public
2023-01-03T00:33:46Z
INSDC last update
2023-01-03T00:33:46Z
INSDC status
public
Submitter Id
ERR1190760_bin.22_CONCOCT_v1.1_MAG
assembly quality
Many fragments with little to no review of assembly other than reporting of standard assembly statistics
assembly software
metaspades_v3.10.0
binning parameters
Default
binning software
CONCOCT v1.1
broker name
EMG broker account, EMBL-EBI
collection date
2014-01-01
completeness score
57.93
completeness software
CheckM
contamination score
0.99
environment (biome)
Host-associated
environment (feature)
Human
environment (material)
Digestive system
geographic location (country and/or sea)
China
geographic location (latitude)
55.676098
geographic location (longitude)
12.568337
investigation type
metagenome-assembled genome
isolation_source
human gut metagenome
metagenomic source
human gut metagenome
project name
Obesity has become a global epidemic as a high risk factor for type 2 diabetes, cardiovascular diseases and cancer. Besides human genetics, our gut microbiome is another contributor to human obesity. Here we perform metagenome-wide association studies (MGWAS) on fecal samples from a case-control cohort including 95 Chinese young adults with obesity and 105 normal-weight controls, and 23 obese patients after weight-loss treatment. We discover and validate microbial species deregulated in obesity, and show the changes of gut microbial composition after weight-loss treatment.
sample derived from
SAMEA3708686
sample name
ERR1190760_bin.22_CONCOCT_v1.1_MAG
scientific_name
uncultured Enterococcus sp.
sequencing method
Illumina HiSeq 2500
taxonomic identity marker
multi-marker approach