16S rRNA Microbiome Analysis Support

We provide a comprehensive service from pipeline construction using Qiime2/DADA2/SILVA to quality control, taxonomy assignment, α/β diversity analysis, and visualization.

• Examples of applicable samples: Saliva, stool, environmental samples, etc., capable of batch analysis of 400 samples
• Requirements definition → Analysis environment construction → Pipelining → Report/reproduction procedure

Key points of reference research (Japanese summary)

In Alzheimer’s disease (AD) model mice, a decrease in gut microbiota diversity, an increase in the Firmicutes/Bacteroidetes ratio, and translocation of bacteria to the pancreas and other organs were observed.
Dietary supplementation with L-arginine and limonoids improved the diversity of the gut microbiota and tended to suppress inflammation, oxidative stress, and neurodegeneration in the liver, pancreas, and brain.
This research suggests that gut-pancreas-liver-brain interactions may be involved in AD pathology, supporting the hypothesis that correcting the gut environment may help slow the progression of neurodegeneration.
(Source: Minamisawa et al., 2021; PubMed/MDPI)

For details, please refer to the original paper.

• PubMed Abstract: Minamisawa M. et al., Life 12(1):34, 2021.
• MDPI article page (JS required): Life 12(1):34.

Implementation details

environment construction

• Selection and assembly of analysis hardware
• Building Python/Anaconda on Linux
• Creating a Qiime2 pipeline

Quality control and pretreatment

• FASTQ (paired-end) quality check by DADA2
• Cropping/Filtering/Denoising
• High-quality ASV table creation

Annotation and analysis

• SILVA DB construction and reference settings
• Taxonomy assignment
• α/β diversity analysis (Shannon, Faith PD / UniFrac, etc.)

visualization

• taxonomy-bar-plot
• Heat map (relative abundance and indicator bacteria)
• Principal coordinate analysis (PCoA)

Reproducibility and transferability

• Steps to reproduce fixed parameter version
• Semi-automation using Snakemake/shell etc. (optional)
• Proposal for recalculating results/comparing differences

Report

• Clarification of analysis policy/preprocessing conditions
• Interpretation of statistical tests and visualizations
• Proposal for next action (hypothesis → additional analysis)

Strengths

• On a scale of 400 samples , parameters were optimized stepwise while observing the results , and repeated analysis was carried out from different angles.
• We provide more exploratory and flexible analysis than general contract services (going back and forth between hypothesis testing and visualization).
• We provide comprehensive support from requirements definition to environment and pipeline construction and reporting .

Representative deliverables (examples)

How to proceed

1. Requirements hearing : Objectives, hypotheses, and benchmark definition
2. Environment and data reception : Determine metadata format/naming rules
3. Pretreatment/QC : Tentative determination of DADA2 conditions → Small-scale test
4. Main analysis : taxonomy, diversity, visualization, statistics
5. Report : Result interpretation, parameters/reproduction steps, next actions

inquiry

Let us know your requirements and data situation, and we’ll propose the appropriate scope and implementation plan.