Bacteroides uniformis
ENA first public
2024-09-21
INSDC center name
EMBL Heidelberg
INSDC status
public
Submitter Id
spire_mag_01098044
assembly quality
Many fragments with little to no review of assembly other than reporting of standard assembly statistics
assembly software
megahit 1.2.9
binning parameters
default
binning software
metabat 2.12.1
broad-scale environmental context
human digestive system
broker name
EBI-EMG
collection date
2011-11
completeness score
74.38
completeness software
checkm2 0.1.3
contamination score
0.72
environmental medium
stool
geographic location (country and/or sea)
Germany
geographic location (latitude)
missing: third party data
geographic location (longitude)
missing: third party data
investigation type
metagenome-assembled genome
isolation_source
human gut metagenome
local environmental context
human gut
metagenomic source
human gut metagenome
project name
To gain more insight into the interconnection between microbiota, obesity and diet, we analyzed the metagenome of 16 patients following a weight-loss program based on a very low calorie inulin-enriched formula diet. In this longitudinal study we used shotgun sequencing of stool DNA to look at taxonomic and functional changes at different phases of the program. In 2 years we sequenced fecal samples at six time points, the baseline (before intervention, = T0), after diet intervention, (3 months later: T3), after reintroduction phase (T6), and three time points at the maintenance phase i.e. month 12 (T12), month 18 (T18) and month 24 (T24). In parallel, clinical parameters were monitored in order to establish potential relationships between microbiota and obesity-associated-disease particularly hypertension, insulin resistance, fatty liver and the metabolic syndrome. Moreover we analyzed the marker potential of microbial parameters for weight loss maintenance success.
sample derived from
SAMN03922502
sample name
spire_mag_01098044
scientific_name
Bacteroides uniformis
sequencing method
Illumina HiSeq 2500
taxonomic identity marker
multi-marker approach