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PRJNA917645 SRP415607

Shallow shotgun vs 16S, healthy adults, California

Shallow shotgun sequencing reduces technical variation in microbiome analysis

78 stool samples from healthy adults in California, USA, comparing shallow shotgun and 16S rRNA sequencing for technical variation and taxonomic resolution; curated design field labels the study cross-sectional. machine-written summary

78 samples · 78 runs · US · Age unknown · 2023 · 70.7 Mbp / sample

Sources
✓Sequence archive 546○Abstract / description○Full text○Supplementary tables✓External resources 156–User contribution–Human verified

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

what each source gave
✓ Sequence archive546 sample values (collection date, country, latitude, location region, longitude, subject id, timepoint)
○ Abstract / descriptionread; no usable statement
○ Full textread; no usable statement or table · PMC10175443
○ Supplementary tablesfiles read; no table could be matched to the samples
✓ External resources156 sample values (health condition, health condition detail) · from GMrepo v3
– User contributionnothing uploaded yet
– Human verifiednot yet checked by a person · I checked this project — verify it
Samples at a glance
1 sampling locations · 1 country
Age
adult78
Condition
healthy_control78
Country
US78
Body site
primary78
Sequencing depth per sample · this project all projects
all projects, 0–0.03 Gbp: 25,080 samples (4 %)this project, 0–0.03 Gbp: 11 samplesall projects, 0.03–0.1 Gbp: 17,066 samples (3 %)this project, 0.03–0.1 Gbp: 39 samplesall projects, 0.1–0.3 Gbp: 29,179 samples (5 %)this project, 0.1–0.3 Gbp: 28 samplesall projects, 0.3–1 Gbp: 47,044 samples (8 %)all projects, 1–3 Gbp: 124,983 samples (22 %)all projects, 3–10 Gbp: 253,728 samples (44 %)all projects, 10–30 Gbp: 72,533 samples (13 %)all projects, 30–100 Gbp: 3,024 samples (1 %)all projects, 100–300 Gbp: 106 samples (0 %)all projects, ≥ 300 Gbp: 9 samples (0 %)project median 0.07 Gbp per sample0130Gbp
Sampling design and relatedness

longitudinal

5 of 5 people sampled more than once (78 samples; median 16, up to 16 per person) · follow-up median 1 d (max 1 d) · median interval between samples 1 d (time from timepoint numbers, unit from the stated sampling schedule). Schedule stated by the source: “stool on day 1, day 2, day 8 and day 9 (twice daily/weekly collection cycle)” (time origin: enrolment); stated follow-up 8 d.

11: day 01: day 01: day 01: day 01: day 01: day 01: day 01: day 01: day 11: day 11: day 11: day 11: day 11: day 11: day 11: day 122: day 02: day 02: day 02: day 02: day 02: day 02: day 02: day 02: day 12: day 12: day 12: day 12: day 12: day 12: day 12: day 133: day 03: day 03: day 03: day 03: day 03: day 03: day 03: day 03: day 13: day 13: day 13: day 13: day 13: day 166: day 06: day 06: day 06: day 06: day 06: day 06: day 06: day 06: day 16: day 16: day 16: day 16: day 16: day 16: day 16: day 199: day 09: day 09: day 09: day 09: day 09: day 09: day 09: day 09: day 19: day 19: day 19: day 19: day 19: day 19: day 19: day 10 d0 d0 d1 d1 d

Each row is one person (5 shown); dots are samples at days since that person's first sample.

How longitudinal and relatedness information is derived

Community composition
Prevotella 26.9%Bacteroides 8.3%Phocaeicola 6.6%Alistipes 5.8%Faecalibacterium 4.1%Roseburia 2.2%Stercorousia 1.8%CAG-841 1.1%CAG-245 1.0%Zag111 0.9%UBA10677 0.9%CAG-269 0.8%not assigned to a genus 32.1%other genera 7.6%

Genera, mean relative abundance over 25 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).

Field coverage
fieldsamplescoverageR1R2R3R4
age_category78 100%
country78 100%78
health_condition78 100%78
subject_id78 100%78
collection_date78 100%78
detailed_location78 100%
health_condition_detail78 100%78
latitude78 100%78
location_region78 100%78
longitude78 100%78
timepoint_label78 100%78
Papers (1)
Details
Archive description

The microbiome is known to play a role in many human diseases, but identifying key microbes and their functions generally requires large studies due to the vast number of species and genes, and the high levels of intra-individual and inter-individual variation. 16S amplicon sequencing of the rRNA gene is commonly used for large studies due to its comparatively low sequencing cost, but it has poor taxonomic and functional resolution. Deep shotgun sequencing is a more accurate and comprehensive alternative for small studies, but can be cost-prohibitive for biomarker discovery in large populations. Shallow or moderate-depth shotgun metagenomics may serve as a viable alternative to 16S sequencing for large-scale and/or dense longitudinal studies, but only if resolution and reproducibility are comparable. Here we applied both 16S and shallow shotgun stool microbiome sequencing to a cohort of 5 subjects sampled twice daily and weekly, with technical replication at the DNA extraction and the library preparation/sequencing steps, for a total of 80 16S samples and 80 shallow shotgun sequencing samples. We found that shallow shotgun sequencing produced lower technical variation and higher taxonomic resolution than 16S sequencing, at a much lower cost than deep shotgun sequencing. These findings suggest that shallow shotgun sequencing provides a more specific and more reproducible alternative to 16S sequencing for large-scale microbiome studies where costs prohibit deep shotgun sequencing and where bacterial species are expected to have good coverage in whole-genome reference databases.

How this project entered the registry

Classified as human gut / stool shotgun DNA metagenome of age unknown by opus_adjudicated with confidence 0.70. Assay shotgun_dna.

  • host run.host_tax_id · “9606”
  • host run.scientific_name · “human gut metagenome”
  • body site study.description · “shallow shotgun stool microbiome sequencing”
Evidence examples (one per field × route)
samplefieldvaluerouteconf.sourcequote
SAMN32544719collection_date2017R10.90biosample.attribute:collection_date2017
SAMN32544719countryUSR10.90sample.attr.geo_loc_nameUSA: California
SAMN32544719health_conditionhealthy_controlR20.75external.gmrepo sourcephenotype: Health
SAMN32544719health_condition_detailHealthR20.75external.gmrepo sourcephenotype: Health
SAMN32544719latitude37.7740R10.90biosample.attribute:lat_lon37.773972 N 122.431297 W
SAMN32544719location_regionCaliforniaR10.85biosample.attribute:geo_loc_nameUSA: California
SAMN32544719longitude-122.4313R10.90biosample.attribute:lat_lon37.773972 N 122.431297 W
SAMN32544719subject_id1R10.85sample.attr.subject1
SAMN32544719timepoint_label1R10.85sample.attr.week1
Sequencing

78 runs over 78 samples · mean 85.9 Mbp per run (median 70.7 Mbp, total 6.70 Gbp; 78 runs with a base count) · layout SINGLE 78 · platform ILLUMINA 78 · models NextSeq 500 (78) · 32% of runs with a Sandpiper profile · first public 2023-01-10

Authors (6)

La Reau AJ first · Strom NB · Filvaroff E · Mavrommatis K · Ward TL · Knights D last

Downloads

PRJNA917645.csv.gz (2.5 KB) · parquet (73.0 KB) — all 78 samples, every field with route and confidence · PRJNA917645_determinations.csv.gz (4.5 KB) — 702 evidence rows

Samples (78 · every field this project has values for · hover a cell for its source)
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