The equation is simple: fiber in → SCFAs out → better health.

👉 How to boost them? Eat more prebiotic fibers:

Whole grains (oats, barley)

Legumes (beans, lentils)

Cruciferous veggies (broccoli, kale, cabbage)

Garlic, onions, carrots

Resistant starch (green bananas, cooked & cooled rice/potatoes)

Low SCFAs are linked to obesity, diabetes, IBD, and even depression.
High SCFAs are linked to longevity, stable metabolism, and a resilient gut.

When they ferment fiber, they release Short Chain Fatty Acids (SCFAs):

Butyrate → protects your gut lining, reduces inflammation, lowers colon cancer risk.

Propionate → regulates appetite & blood sugar.

Acetate → fuels muscle and brain, supports heart health.

8. Visualization & Interpretation

Generate figures like heatmaps, bar plots, or network diagrams (R, GraphPad Prism)

Interpret results in the context of your research question or environment.

7. Diversity & Statistical Analysis

Compute alpha diversity (within-sample) and beta diversity (between-sample).

Use statistical tools to detect significant differences or correlations (GraphPad Prism, R)

6. Functional Annotation

Predict genes and assign functions using tools like PROKKA, EggNOG-mapper, or KEGG.

Understand metabolic pathways and functional potential of the community. (Humann3 is a good tool for this)

5. Taxonomic Profiling

Identify which microbes are present using tools like Kraken2, MetaPhlAn, or Kaiju.

Generate a taxonomic composition profile for your sample (Krona charts)

4. Metagenomic Assembly (Optional)

Assemble reads into longer contigs using tools like MEGAHIT or metaSPAdes.

This helps reconstruct genomes from complex communities.

3. Quality Control (QC)

Assess raw reads using tools like FastQC.

Trim adapters, remove low-quality reads, and filter contaminants (e.g., host DNA). Use tools like fastp, trimmomatic or cutadapt.

2. Library Preparation & Sequencing

Prepare DNA libraries suitable for high-throughput sequencing (Illumina, PacBio, Nanopore).

Perform shotgun sequencing to capture all genetic material in the sample.

Further downstream analysis can be performed on this output data (ASV table and Taxonomy file) using packages such as phyloseq, deseq2, etc (in R).

This analysis can also be performed in the QIIME2 conda environment which provides additional tools and features.

1. Check read quality
2. Quality trimming and filtering
3. Estimating error rates
4. Run the DADA2 core sample inference algorithm
5. Merge paired reads
6. Create an ASV (amplicon sequence variant) table
7. Chimera removal
8. Determine run statistics
9. Assign taxonomy to the sequences