Reports

Bioquik writes all outputs to the specified output directory.

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Per-file motif counts

For each FASTA file processed, Bioquik generates:

<filename>_motif_counts.csv

Each CSV contains motif counts for that file.

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Combined reports

from bioquik.reports import combine_csv

df = combine_csv(out_dir)

This reads all *_motif_counts.csv files in out_dir and concatenates them.

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Writing summaries

from bioquik.reports import write_summary

write_summary(df, out_dir, json_out=True)

Outputs:

• combined_counts.csv (always)

• summary.json (optional)

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Plots

Bioquik includes optional visualization utilities for summarizing motif distributions.

Enabling Visualization

Plotting functionality is not included in the minimal installation. To enable visual outputs, install Bioquik with the visualization extra:

pip install bioquik[viz]

This installs Matplotlib, which is required for rendering figures.

What Bioquik Generates

When plotting is enabled, the following images are created in the output directory:

• motif_distribution.png: Bar chart of total motif counts aggregated across input FASTA files.

• motif_heatmap.png: Heatmap showing how motif counts vary by file.

These are created automatically by the CLI when analysis completes.

Using Plotting Functions Programmatically

If you’re working interactively in Python, you can directly generate the same plots:

from bioquik.plotter import plot_distribution, plot_heatmap
from bioquik.reports import combine_csv

# Combine outputs from prior motif scans
df = combine_csv(out_dir)

# Generate the plots
plot_distribution(df, out_dir)
plot_heatmap(df, out_dir)

Both functions will save images directly to out_dir, allowing integration into custom pipelines.

Visualization is entirely optional and does not affect core counting or summary reporting functionality.