MicroVerse-16Sense:

A snakemake workflow for processing 16s ONT data

Usages:

snakemake -s Snakefile.smk   --use-conda

Requirements:

• Singularity or docker
• Edit and update the config file

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Apart from the pipeline, you can also use the individual scripts to perform abundance, alpha diversity, and beta diversity analyses.

###Abundance Plot

Script:
script/plot_abundance.py

Usage:

python script/plot_abundance.py <input_dir> <output_dir> [top_n]

Inputs:

• input_dir: Path to the directory containing *.kraken2_bracken.report files.
• output_dir: Directory to save the results.
• top_n (default = 10): Number of top species to display in the stacked bar plot. (optional)
  

Output:

• raw_counts_for_deseq2.txt: Combined species-level count table for all samples.
• composition_plot.pdf: Stacked bar plot showing relative abundances of the top species across samples, saved in the specified output directory.

Example python script/plot_abundance.py data/kraken_reports/ results/ 10

Description:
Reads Bracken/Kraken2 report files (*.kraken2_bracken.report) from a directory, aggregates species-level abundances, and generates:

• A combined count table (for DESeq2 or downstream analyses)
• A stacked bar plot showing the top 10 most abundant species per sample

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Alpha Diversity

Script:
script/alpha_diversity.py

Usage:

python script/alpha_diversity.py <abundance_file> <metadata_file> <output_dir> [group_col] [show_pvalues]

Example: python script/alpha_diversity.py data/abundance.tsv data/metadata.tsv results/ Group 1

Inputs:

• abundance_file: Tab-delimited table of species/OTU counts (rows = species, columns = samples).
• metadata_file: Tab-delimited metadata linking samples to experimental groups.
• output_dir: Directory for saving results.
• group_col (default = "Group"): Column in metadata to define sample groups. (optional)
• show_pvalues (default = 0): Whether to display p-values on the plots (0 = no, 1 = yes). (optional)

Output:

• alpha_diversity_plots.pdf or alpha_diversity_with_pvalues.pdf: Boxplots summarizing alpha diversity metrics (Shannon, Simpson, Observed OTUs, Chao1) per sample group.

Description:
Calculates alpha diversity metrics (Shannon, Simpson, Observed, Chao1) from a species abundance table, and generates grouped boxplots with optional p-value annotations.

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Beta Diversity

Script:
script/beta_diversity.py

Usage:

python script/beta_diversity.py <abundance_file> <metadata_file> <output_dir> [group_col] [metrics_comma_separated] [clr_flags_comma_separated]

Example python script/beta_diversity.py data/abundance.tsv data/metadata.tsv results/ Group braycurtis,jaccard,euclidean false,false,false

Inputs:

• abundance_file: Tab-delimited table of species/OTU counts (rows = species, columns = samples).
• metadata_file: Tab-delimited metadata linking samples to experimental groups.
• output_dir: Directory to save results.
• group_col (default = "Group"): Column in metadata for grouping samples. (optional)
• metrics_comma_separated (default = "braycurtis,jaccard,euclidean"): Beta diversity metrics to compute. (optional)
• clr_flags_comma_separated (default = false,false,false): Whether to apply CLR transformation for each metric (true or false). (optional)

Output:

• beta_diversity_plots.pdf: PCoA scatterplots for the selected beta diversity metrics, colored by sample group, saved in the specified output directory.

Description:
Computes beta diversity metrics (Bray–Curtis, Jaccard, Euclidean, and UniFrac) from an abundance table, performs Principal Coordinates Analysis (PCoA), and plots PCoA scatterplots with non-overlapping sample labels.