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PRJEB7949 ERP008951

Fecal metagenomes in IBD vs healthy controls, UK

The fecal microbiome was studied in a group of IBD suffers and compared to a control group's fecal microbiome

40 fecal shotgun metagenome samples from the United Kingdom, in a case-control design comparing individuals with IBD (unspecified type) to healthy controls, 20 samples per group. machine-written summary

40 samples · 40 runs · GB · Age unknown · 2015 · 3.31 Gbp / sample

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Sources

✓Sequence archive 284○Abstract / description○Full text○Supplementary tables✓External resources 36–User contribution–Human verified

Checked this project's papers and archive records yourself? Mark it human-verified.

what each source gave

✓ Sequence archive	284 sample values (collection date, country, health condition, health condition detail, latitude, location region, longitude, subject id)
○ Abstract / description	read; no usable statement
○ Full text	read; no usable statement or table · PMC10636326, PMC11009399, PMC12058177, PMC12341049, PMC12771841, PMC13151518, PMC6359901, PMC9052204
○ Supplementary tables	files read; no table could be matched to the samples
✓ External resources	36 sample values (health condition, health condition detail) · from GMrepo v3
– User contribution	nothing uploaded yet
– Human verified	not yet checked by a person · I checked this project — verify it

Samples at a glance

1 sampling locations · 1 country

Age

unknown40

Condition

healthy_control20

ibd_unspecified20

Country

GB40

Body site

primary40

Sequencing depth per sample · this project all projects

Sampling design and relatedness

longitudinal — not resolved

20 of 20 people sampled more than once (40 samples; median 2, up to 2 per person).

How longitudinal and relatedness information is derived

Community composition

Phocaeicola 14.2%Bacteroides 7.4%Faecalibacterium 7.4%Prevotella 5.8%Roseburia 4.6%Alistipes 4.5%Lachnospira 3.4%Parabacteroides 2.2%Sutterella 2.1%Klebsiella 1.5%Gemmiger 1.4%Acetatifactor 1.4%not assigned to a genus 7.9%other genera 36.1%

Genera, mean relative abundance over 34 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).

Field coverage

field	samples	coverage	R1	R2
age_category	0	0%
country	40	100%	40
health_condition	40	100%	22	18
subject_id	40	100%	40
collection_date	40	100%	40
detailed_location	40	100%
health_condition_detail	40	100%	22	18
latitude	40	100%	40
location_region	40	100%	40
longitude	40	100%	40

Papers (9)

Kelpie: generating full-length 'amplicons' from whole-metagenome datasets. 2019 PMID 30723610 PMC6359901 own_data
Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data. 2025 PMID 40334224 PMC12058177 own_data
CD74+CCL5+ effector CD8+ T cells drive mucosal inflammation and predict biologics response in inflammatory bowel disease. 2025 PMID 41318676 PMC12771841 own_data
Quantifying and Cataloguing Unknown Sequences within Human Microbiomes. 2022 PMID 35258340 PMC9052204 unsure
Bacterial indole-3-lactic acid affects epithelium-macrophage crosstalk to regulate intestinal homeostasis. 2023 PMID 37903267 PMC10636326 unsure

4 more

Plasmids in the human gut reveal neutral dispersal and recombination that is overpowered by inflammatory diseases. 2024 PMID 38605009 PMC11009399
Multi-omics integration reveals functional signatures of gut microbiome in atherosclerosis. 2025 PMID 40785047 PMC12341049
Parasutterella excrementihominis exacerbates experimental colitis and colitis-associated colorectal cancer via pathogenic NETosis activation. 2026 PMID 41448880 PMC13151518
Plasmid dispersal and recombination across human gut microbiomes is neutral, but overpowered by inflammatory diseases 2023

Details

Archive description

Ten IBD and 10 healthy individual's fecal microbiomes were determined by NGS sequencing, to identify features that are unique to each group. This was done using shot-gun metagenomics of DNA extracted from fecal samples and run on a HiSeq 2500. Sample affiliation: Sample Description Library Name Dilution Name Tag Barcode Lane Read HEALTHY 1 LIB6632 LDI6150 1: Index 23 (GAGTGG) L001 R1 HEALTHY 1 LIB6632 LDI6150 1: Index 23 (GAGTGG) L001 R2 HEALTHY 1 LIB6632 LDI6150 1: Index 23 (GAGTGG) L002 R1 HEALTHY 1 LIB6632 LDI6150 1: Index 23 (GAGTGG) L002 R2 HEALTHY 2 LIB6633 LDI6151 1: Index 25 (ACTGAT) L001 R1 HEALTHY 2 LIB6633 LDI6151 1: Index 25 (ACTGAT) L001 R2 HEALTHY 2 LIB6633 LDI6151 1: Index 25 (ACTGAT) L002 R1 HEALTHY 2 LIB6633 LDI6151 1: Index 25 (ACTGAT) L002 R2 HEALTHY 3 LIB6634 LDI6152 1: Index 2 (CGATGT) L001 R1 HEALTHY 3 LIB6634 LDI6152 1: Index 2 (CGATGT) L001 R2 HEALTHY 3 LIB6634 LDI6152 1: Index 2 (CGATGT) L002 R1 HEALTHY 3 LIB6634 LDI6152 1: Index 2 (CGATGT) L002 R2 HEALTHY 4 LIB6635 LDI6153 1: Index 3 (TTAGGC) L001 R1 HEALTHY 4 LIB6635 LDI6153 1: Index 3 (TTAGGC) L001 R2 HEALTHY 4 LIB6635 LDI6153 1: Index 3 (TTAGGC) L002 R1 HEALTHY 4 LIB6635 LDI6153 1: Index 3 (TTAGGC) L002 R2 HEALTHY 5 LIB6636 LDI6154 1: Index 4 (TGACCA) L001 R1 HEALTHY 5 LIB6636 LDI6154 1: Index 4 (TGACCA) L001 R2 HEALTHY 5 LIB6636 LDI6154 1: Index 4 (TGACCA) L002 R1 HEALTHY 5 LIB6636 LDI6154 1: Index 4 (TGACCA) L002 R2 HEALTHY 6 LIB6637 LDI6155 1: Index 7 (CAGATC) L001 R1 HEALTHY 6 LIB6637 LDI6155 1: Index 7 (CAGATC) L001 R2 HEALTHY 6 LIB6637 LDI6155 1: Index 7 (CAGATC) L002 R1 HEALTHY 6 LIB6637 LDI6155 1: Index 7 (CAGATC) L002 R2 HEALTHY 7 LIB6638 LDI6156 1: Index 9 (GATCAG) L001 R1 HEALTHY 7 LIB6638 LDI6156 1: Index 9 (GATCAG) L001 R2 HEALTHY 7 LIB6638 LDI6156 1: Index 9 (GATCAG) L002 R1 HEALTHY 7 LIB6638 LDI6156 1: Index 9 (GATCAG) L002 R2 HEALTHY 8 LIB6639 LDI6157 1: Index 15 (ATGTCA) L001 R1 HEALTHY 8 LIB6639 LDI6157 1: Index 15 (ATGTCA) L001 R2 HEALTHY 8 LIB6639 LDI6157 1: Index 15 (ATGTCA) L002 R1 HEALTHY 8 LIB6639 LDI6157 1: Index 15 (ATGTCA) L002 R2 HEALTHY 9 LIB6640 LDI6158 1: Index 16 (CCGTCC) L001 R1 HEALTHY 9 LIB6640 LDI6158 1: Index 16 (CCGTCC) L001 R2 HEALTHY 9 LIB6640 LDI6158 1: Index 16 (CCGTCC) L002 R1 HEALTHY 9 LIB6640 LDI6158 1: Index 16 (CCGTCC) L002 R2 HEALTHY10 LIB6641 LDI6159 1: Index 18 (GTCCGC) L001 R1 HEALTHY10 LIB6641 LDI6159 1: Index 18 (GTCCGC) L001 R2 HEALTHY10 LIB6641 LDI6159 1: Index 18 (GTCCGC) L002 R1 HEALTHY10 LIB6641 LDI6159 1: Index 18 (GTCCGC) L002 R2 IBD 1 LIB6642 LDI6160 1: Index 20 (GTGGCC) L001 R1 IBD 1 LIB6642 LDI6160 1: Index 20 (GTGGCC) L001 R2 IBD 1 LIB6642 LDI6160 1: Index 20 (GTGGCC) L002 R1 IBD 1 LIB6642 LDI6160 1: Index 20 (GTGGCC) L002 R2 IBD 2 LIB6643 LDI6161 1: Index 21 (GTTTCG) L001 R1 IBD 2 LIB6643 LDI6161 1: Index 21 (GTTTCG) L001 R2 IBD 2 LIB6643 LDI6161 1: Index 21 (GTTTCG) L002 R1 IBD 2 LIB6643 LDI6161 1: Index 21 (GTTTCG) L002 R2 IBD3 LIB6644 LDI6162 1: Index 22 (CGTACG) L001 R1 IBD3 LIB6644 LDI6162 1: Index 22 (CGTACG) L001 R2 IBD3 LIB6644 LDI6162 1: Index 22 (CGTACG) L002 R1 IBD3 LIB6644 LDI6162 1: Index 22 (CGTACG) L002 R2 IBD4 LIB6645 LDI6163 1: Index 27 (ATTCCT) L001 R1 IBD4 LIB6645 LDI6163 1: Index 27 (ATTCCT) L001 R2 IBD4 LIB6645 LDI6163 1: Index 27 (ATTCCT) L002 R1 IBD4 LIB6645 LDI6163 1: Index 27 (ATTCCT) L002 R2 IBD5 LIB6646 LDI6164 1: Index 1 (ATCACG) L001 R1 IBD5 LIB6646 LDI6164 1: Index 1 (ATCACG) L001 R2 IBD5 LIB6646 LDI6164 1: Index 1 (ATCACG) L002 R1 IBD5 LIB6646 LDI6164 1: Index 1 (ATCACG) L002 R2 IBD6 LIB6647 LDI6165 1: Index 5 (ACAGTG) L001 R1 IBD6 LIB6647 LDI6165 1: Index 5 (ACAGTG) L001 R2 IBD6 LIB6647 LDI6165 1: Index 5 (ACAGTG) L002 R1 IBD6 LIB6647 LDI6165 1: Index 5 (ACAGTG) L002 R2 IBD7 LIB6648 LDI6166 1: Index 6 (GCCAAT) L001 R1 IBD7 LIB6648 LDI6166 1: Index 6 (GCCAAT) L001 R2 IBD7 LIB6648 LDI6166 1: Index 6 (GCCAAT) L002 R1 IBD7 LIB6648 LDI6166 1: Index 6 (GCCAAT) L002 R2 IBD8 LIB6649 LDI6167 1: Index 8 (ACTTGA) L001 R1 IBD8 LIB6649 LDI6167 1: Index 8 (ACTTGA) L0 …

How this project entered the registry

Classified as human gut / stool shotgun DNA metagenome of age unknown by sonnet_x2 with confidence 0.68. Assay shotgun_dna.

host run.scientific_name · “human gut metagenome”
body site study.title · “fecal microbiome was studied in”

Evidence examples (one per field × route)

sample	field	value	route	conf.	source	quote
SAMEA3170869	collection_date	2012-10-11	R1	0.90	biosample.attribute:collection_date	2012-10-11
SAMEA3170869	country	GB	R1	0.90	sample.attr.geographic_location_country_and_or_sea	United Kingdom
SAMEA3170869	health_condition	healthy_control	R1	0.70	sample.name_rule:library_name	HEALTHY 1_1
SAMEA3170887	health_condition	healthy_control	R2	0.75	external.gmrepo source	phenotype: Health
SAMEA3170869	health_condition_detail	HEALTHY 1_1	R1	0.70	sample.name_rule:library_name	HEALTHY 1_1
SAMEA3170887	health_condition_detail	Health	R2	0.75	external.gmrepo source	phenotype: Health
SAMEA3170869	latitude	51.5179	R1	0.90	biosample.attribute:geographic_location_latitude+geographic_location_longitude	51.517858 -0.174660
SAMEA3170869	location_region	England	R1	0.60	biosample.attribute:geographic_location_latitude+geographic_location_longitude	51.517858 -0.174660
SAMEA3170869	longitude	-0.1747	R1	0.90	biosample.attribute:geographic_location_latitude+geographic_location_longitude	51.517858 -0.174660
SAMEA3170869	subject_id	HEALTHY 1	R1	0.70	sample.name_rule:library_name	HEALTHY 1_1

Sequencing

40 runs over 40 samples · mean 3.39 Gbp per run (median 3.31 Gbp, total 135.80 Gbp; 40 runs with a base count) · layout PAIRED 40 · platform ILLUMINA 40 · models Illumina HiSeq 2500 (40) · 85% of runs with a Sandpiper profile · first public 2015-09-01

Authors (33)

Modha S first · Robertson DL · Hughes J · Orton RJ last · Zorea A first · Pellow D · Levin L · Pilosof S · Friedman J · Shamir R · Mizrahi I last · Shi H first · Wu M · Wu X · Liu Z · Jiang S · Li G · Yang Y · Fu Y · Wang Q · Zhang G · Cheng L last · Tan H first · Huang L · Wang J · Huang H · Lin Z · Yang S · Shi Y · Li J · Zhang H · Zhou Y · Huang C last

Downloads

PRJEB7949.csv.gz (1.9 KB) · parquet (71.4 KB) — all 40 samples, every field with route and confidence · PRJEB7949_determinations.csv.gz (3.4 KB) — 320 evidence rows

Samples (40 · every field this project has values for · hover a cell for its source)

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