NGS Data Analysis on Windows with WSL2: A Practical Workflow

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Scope: This tutorial is for education and research
workflows. It does not constitute a validated clinical bioinformatics
pipeline.
Last reviewed: 29 September 2026.

Many widely used bioinformatics tools are designed for Linux. Windows
Subsystem for Linux 2 (WSL2) gives Windows users a real Linux
environment without requiring a separate computer or a conventional
dual-boot installation. That makes it useful for learning NGS analysis,
testing commands, and running modest research workloads.

WSL2 does not turn a laptop into a high-performance computing
cluster, however. Whole-genome data can require hundreds of gigabytes of
storage, substantial memory, and long compute times. The goal of this
guide is therefore a reliable starting environment and a small,
verifiable FASTQ quality-control workflow—not a claim that every
production pipeline belongs on a personal computer.

Quick answer: Install the current WSL2 release with
wsl --install, keep active FASTQ and BAM files inside the
Linux filesystem rather than /mnt/c, create a strictly
configured Bioconda environment, and begin with integrity checks plus
FastQC/MultiQC. Use a governed server, HPC system, or cloud platform
when the data size, uptime, collaboration, or clinical controls exceed a
personal workstation.

What you will build

By the end of the tutorial, you will have:

• WSL2 with Ubuntu installed and updated;
• a dedicated project directory inside the Linux filesystem;
• a reproducible Conda environment containing FastQC, MultiQC,
  Samtools, and BCFtools;
• a simple workflow for checking FASTQ files and collecting
  quality-control reports;
• a checklist for deciding when local WSL2 is adequate and when to
  move to a server, HPC system, or cloud workflow.

1. Check hardware and
free storage first

For a small training dataset, 8 GB RAM can be workable, while 16 GB
or more is more comfortable. Real exome and genome projects may require
far more memory and disk space. Keep at least two to three times the
compressed input size available when a workflow will create uncompressed
intermediates, alignments, indexes, and temporary files. This is a
planning heuristic; actual needs depend on the tool and dataset.

In PowerShell, check available WSL distributions and versions:

wsl --status
wsl --version
wsl --list --verbose

New installations normally use WSL2 by default. If an existing Ubuntu
installation shows version 1, convert it from an Administrator
PowerShell session:

wsl --set-version Ubuntu 2

2. Install or update WSL2

On supported Windows 10 and Windows 11 systems, Microsoft provides a
one-command installation. Open PowerShell as Administrator and run:

wsl --install

Restart when prompted, launch Ubuntu, and create the Linux username
and password requested during first setup. Then update WSL from
PowerShell:

wsl --update

Inside Ubuntu, update the distribution packages:

sudo apt update
sudo apt upgrade -y
sudo apt install -y build-essential curl wget git unzip pigz

3. Put NGS data in the
Linux filesystem

This is the most important WSL performance decision in the
tutorial.

Microsoft recommends storing files in the WSL filesystem when Linux
command-line tools will process them. A directory such as
/home/yourname/ngs-project is normally faster for repeated
FASTQ, BAM, and index access than /mnt/c/Users/..., which
crosses the Windows–Linux filesystem boundary.

Create a project structure:

mkdir -p ~/ngs-project/{raw_data,qc,results,logs,envs}
cd ~/ngs-project

Windows File Explorer can open the current Linux directory with:

explorer.exe .

Keep a protected copy of irreplaceable raw data elsewhere. WSL
storage is convenient working space, not a substitute for a backup
policy.

4. Install a
reproducible tool environment

Conda-compatible environments make it easier to pin tool versions and
recreate a workflow. Install Miniforge using the current installer from
its official repository, then open a new shell. Configure the Bioconda
channels in the documented order. Current Bioconda guidance no longer
includes the defaults channel:

conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict

Create a named environment:

conda create -n ngs-qc -y fastqc multiqc samtools bcftools seqkit
conda activate ngs-qc

Record exact versions:

fastqc --version
multiqc --version
samtools --version | head -n 2
bcftools --version | head -n 2
conda env export --from-history > envs/ngs-qc.yml
conda list --explicit > envs/ngs-qc-explicit.txt

The history file is readable and portable; the explicit file gives a
stricter record for recreating the same platform-specific
environment.

5. Confirm FASTQ files
before analysis

Copy or download a small training dataset into
~/ngs-project/raw_data. Avoid beginning with clinical or
irreplaceable samples. List files and inspect compressed FASTQ structure
without extracting the entire dataset:

cd ~/ngs-project
ls -lh raw_data
gzip -t raw_data/*.fastq.gz
zcat raw_data/sample_R1.fastq.gz | head -n 8

gzip -t checks compressed-file integrity. A FASTQ record
consists of four lines: an identifier beginning with @, the
sequence, a + line, and encoded base-quality characters.
Looking at two records can catch an obvious file mix-up, but it does not
replace systematic quality control.

For paired-end data, verify that both mates exist and have matching
read counts:

seqkit stats raw_data/*.fastq.gz

6. Run FastQC and
summarize with MultiQC

Run FastQC with a modest number of threads. Increase this only if the
computer has enough processors and memory:

mkdir -p qc/fastqc
fastqc --threads 4 --outdir qc/fastqc raw_data/*.fastq.gz \
  2>&1 | tee logs/fastqc.log

Combine reports:

multiqc qc/fastqc --outdir qc/multiqc \
  2>&1 | tee logs/multiqc.log

Open the result in Windows:

explorer.exe qc/multiqc/multiqc_report.html

Pay attention to per-base sequence quality, adapter content,
duplication, overrepresented sequences, GC distribution, and read-length
consistency. A warning is not automatically a failed experiment. For
example, expected amplicon libraries and RNA-seq libraries can have
characteristic biases. Interpret each module in the context of the
library design and downstream analysis.

7. Add checksums and a
small run manifest

Checksums help detect accidental file changes during transfer or
storage:

sha256sum raw_data/*.fastq.gz > raw_data/SHA256SUMS
sha256sum -c raw_data/SHA256SUMS

Create a minimal manifest recording sample identifiers, read files,
library type, reference build, and source. Do not encode patient names
or other identifying information in filenames. For sensitive data,
follow your institution’s access, encryption, retention, and audit
requirements; a personal WSL installation may not meet them.

8. Control WSL2
resources only when necessary

WSL2 resource limits are configured in
%UserProfile%\.wslconfig on Windows. For example:

[wsl2]
memory=12GB
processors=6
swap=8GB

After changing this file, restart WSL from PowerShell:

wsl --shutdown

Do not copy these values blindly. Leave enough CPU and memory for
Windows, and monitor actual workloads. An excessively low limit can
cause tools to terminate; an excessively high allocation can make the
rest of the computer unresponsive.

9. Understand the limits of
WSL2

WSL2 is well suited to learning, developing scripts, running QC on
small or moderate datasets, and testing containerized workflows.
Consider a Linux server, HPC cluster, or governed cloud environment when
you need:

• large cohorts or whole-genome scale processing;
• long-running workflows that must survive laptop restarts;
• controlled access to human genomic data;
• validated clinical pipelines;
• job scheduling, team audit trails, or reproducible shared
  infrastructure.

GATK’s published platform guidance targets Linux and other
POSIX-compatible systems and says native Windows is unsupported. WSL2
provides a Linux environment, but every individual tool or production
pipeline should still be checked against its own supported-platform
documentation. For important work, validate output against a known
dataset rather than assuming that successful installation proves
analytical correctness.

Common problems

Analysis is unexpectedly
slow

Check whether data are under /mnt/c. Move the working
dataset to ~/ngs-project and retry. Also check free storage
with df -h and memory with free -h.

A command
disappears after reopening Ubuntu

Activate the environment again:

conda activate ngs-qc

If conda itself is missing, reopen the shell after
installation or run the initialization command supplied by the
installer.

WSL cannot see all
memory or processors

Review %UserProfile%\.wslconfig, then run
wsl --shutdown from PowerShell. Reopen Ubuntu and check
free -h and nproc.

Paired files have
different read counts

Stop before alignment. Confirm naming, download completeness,
trimming history, and whether a preprocessing step intentionally removed
orphaned reads.

A reproducibility checklist

Before extending this setup into alignment or variant analysis,
record:

• Windows, WSL, and Ubuntu versions;
• tool versions and the exported environment file;
• input checksums;
• reference genome build and exact reference-file checksums;
• commands, parameters, timestamps, and log files;
• expected outputs tested on a public reference dataset.

The next useful step is to learn how FASTQ quality scores work, then
move through alignment, SAM/BAM/CRAM handling, and variant formats in a
deliberate sequence. If you are designing a small validation amplicon
around a known genomic position, Sci Chores’ Pronto Primer
may help with the primer-design stage; it is not part of the NGS
computational pipeline.

Sources and further reading

• Microsoft:
  Install WSL
• Microsoft:
  Working across Windows and Linux file systems
• Microsoft:
  Advanced settings configuration in WSL
• Bioconda
  documentation
• FastQC
  project
• MultiQC
  documentation
• Samtools organisation and HTS
  specifications
• Broad Institute
  GATK

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Taimoor Khan

A molecular biologist and aspiring bioinformatician with a passion for genomics, rare diseases, and precision medicine. With an MPhil in Molecular Biology and experience in genetics and genomics, he has contributed to clinical research projects on rare genetic disorders. He’s passionate about making genomic data meaningful — and ultimately helpful — for patients and clinicians alike.