The document summarizes microbiome biomarker data from the American Gut Project. It describes the project's goal of characterizing participants' gut, skin, and oral bacteria to better understand relationships between microbiome and lifestyle/health factors. Over 4,600 samples from 3,624 participants have been sequenced and analyzed. The analyzed samples included some from patients that provided multiple sample types (mouth & skin, mouth & stool). T-Bioinfo then performed analyses including mapping samples to identify bacteria, generating abundance tables, and using machine learning methods to identify correlations between bacterial species from different body sites. Preliminary conclusions identified outstanding samples and correlated oral and stool bacteria.

1. Microbiome Biomarkers from the
Human Gut Microbiome
Based on samples from the American Gut Project (http://americangut.org)

2. American Gut Project Background and Summary
Human microbiome reference datasets provide epidemiological context for researchers. This enables them to uncover
new insights into their own data through meta-analyses. In addition, large and comprehensive reference sets offer a
means to develop or test hypotheses and can pave the way for addressing practical study design considerations such as
sample size decisions. Through engaging the general public, the American Gut Project aims to address many of the issues
present in existing reference resources, characterizing health and disease, lifestyle, and dietary choices of the
participants while extending its efforts globally through international collaborations.
American Gut Project has completed sequencing and analysis of gut, skin, and oral bacteria from the first 4,658 samples
of 3,624 participants in the American Gut study.
Pine Biotech selected all microbiome samples from patients that gave more than one type of sample: Mouth & Skin,
Mouth & Stool for further analysis.
16S gene (leading to two distinct datasets—V1-3 and V3-5)
• http://www.ebi.ac.uk/ena/data/view/ERP012803&display=html

3. Data Information
Laboratory: Knight Labs
Extracted Molecule: 16 s rRNA /rDNA
Extraction Protocol: PowerMag Soil DNA Isolation Kit
Extraction Link: https://microbio.me/AmericanGut/faq/#faq25
Protocol Link: http://www.earthmicrobiome.org/emp-standard-
protocols/dna-extraction-protocol/
Instrument Model: Illumina MiSeq
16S software package: QIIME using raw sequence data
http://qiime.org
Sample #: 651
Sample Type: Mouthl, Skin, Stool
Study:http://www.ebi.ac.uk/ena/data/view/PRJEB11419

4. T-Bioinfo Analysis Steps
1) mapping on a database of known 16S rRNA databases
2) generating Abundance tables for samples with both
skin and stool samples; samples with both stool and
mouth.
3) Using the BiAssociation /P-clustering method to find pairs
of associated bacteria and detect clusters between bacterial
species from two separate body sites such as stool and skin.
Microbiome Data Analysis Machine Learning

5. Preliminary Conclusions
Unsupervised analysis has identified “outstanding” samples
Identify highly correlated stool and oral bacteria- determined blindly
More Updates soon!