TODO: COMPLETE Mastering Quality Control in Omics with NanoPlot

Setting Up NanoPlot with Conda and Docker

NanoPlot is a powerful plotting tool for long‑read sequencing data and alignments. It generates publication‑quality visualizations (e.g., read length histograms, quality plots, bivariate scatter plots) and a comprehensive HTML summary.

In this guide, we'll cover:

• Installing NanoPlot via Conda
• Running NanoPlot in a Docker container
• A simple example workflow using a FASTQ file

1. Prerequisites

• Python 3.6+ (for Conda installation)
• Conda (Miniconda or Anaconda)
• Docker (if using the containerized approach)
• A sample FASTQ file from your long‑read sequencing run

2. Installing NanoPlot with Conda

The NanoPlot package is available on Bioconda. First, ensure you have the bioconda channel enabled:

# Add channels if not already present
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge

# Create (or activate) your analysis environment
conda create -n nanoplot_env python=3.9 -y
conda activate nanoplot_env

# Install NanoPlot
conda install nanoplot -y

To upgrade later:

conda update nanoplot -y

3. Running NanoPlot in Docker

If you prefer containerized workflows (no local Python setup), you can run NanoPlot via Docker:

1. Pull the official NanoPlot image (if available) or build your own.

   # Pull from Docker Hub (hypothetical image)
   docker pull quay.io/biocontainers/nanoplot:latest
   

2. Build a simple Dockerfile (if no pre-built image is available):

   # Dockerfile
   FROM continuumio/miniconda3:latest
   
   # Install NanoPlot from Bioconda
   RUN conda config --add channels defaults \
       && conda config --add channels bioconda \
       && conda config --add channels conda-forge \
       && conda install nanoplot -y \
       && conda clean --all -y
   
   # Set entrypoint
   ENTRYPOINT ["NanoPlot"]
   

   Build it:

   docker build -t nanoplot:latest .
   

3. Run NanoPlot on your data:

   docker run --rm -v $(pwd):/data nanoplot:latest \
     --fastq /data/reads.fastq.gz \
     --outdir /data/nanoplot_output \
     --prefix myrun_
   

This mounts your current directory into the container (/data) and outputs results back to your host system.

4. Example Usage

Assume you have a compressed FASTQ file reads.fastq.gz in your working directory.

4.1 Quick Run (Conda)

# Activate environment
conda activate nanoplot_env

# Plot read length histogram and bivariate quality plot
NanoPlot \
  --fastq reads.fastq.gz \
  --loglength \
  -o nanoplot_results \
  --prefix sample1_

4.2 Docker Run

docker run --rm -v $(pwd):/data nanoplot:latest \
  --fastq /data/reads.fastq.gz \
  --loglength \
  --outdir /data/nanoplot_results \
  --prefix sample1_

After completion, you’ll find:

• sample1_STATS.html — interactive HTML report
• Multiple PNG/SVG plots:
  • Read length histogram
  • Log-transformed length histogram
  • Length vs. quality scatter (hex/kde)
  • Quality over time
  • And more…

5. Tips & Tricks

• Threads: use -t N or --threads N to speed up processing on multicore machines.
• Filtering: drop short or low-quality reads with --minlength 1000 or --minqual 7.
• Custom plots: specify formats (-f pdf svg) and plot types (--plots dot hex).
• Store data: use --store to save extracted metrics in a pickle for downstream analyses.

6. Conclusion

NanoPlot makes visualizing long‑read sequencing data straightforward, whether via Conda or Docker. With a few simple commands you can generate publication‑ready figures and detailed HTML reports.

Feel free to explore more options in the NanoPlot documentation and customize your workflow!

Happy plotting!