Personal-Genome-Pipeline is a local genome analysis app for Windows users who want to process whole genome sequencing data on their own computer.
It uses Docker to keep the setup contained and avoids cloud upload. You keep your files on your machine while the pipeline runs.
Use it to review common genome data, look at likely trait links, and work with outputs such as VCF files and related reports.
Open the repository page here:
Use that page to get the project files and follow the Windows setup steps below.
Before you start, check that your PC can handle the job.
- Windows 10 or Windows 11
- 16 GB RAM or more
- 100 GB free disk space
- A modern CPU with at least 4 cores
- Docker Desktop for Windows
- A genome data file in a common format such as FASTQ, BAM, or VCF
- Stable internet access for the first setup and package download
If you plan to process full whole genome data, more RAM and more disk space will help.
This pipeline brings together common steps used in personal genomics.
- Variant calling with DeepVariant
- Variant review with ClinVar data
- Support for GRCh38 reference workflows
- Mitochondrial analysis
- Structural variant checks
- Telomere-related output
- Pharmacogenomics review with PharmCAT
- Polygenic risk score style reports
- Local output files you can store on your PC
Follow these steps in order.
Download Docker Desktop from the official Docker site and install it on Windows.
After install, open Docker Desktop and wait until it shows that it is running.
Open the repository page:
Use the green Code button on the page and download the project as a ZIP file, or clone it if you already know how to do that.
If you downloaded a ZIP file, extract it to a simple folder such as:
C:\Personal-Genome-Pipeline
Open File Explorer and go to the folder where you extracted the files.
Look for files such as:
docker-compose.ymlREADME.md- setup or run scripts
- pipeline config files
If the project includes a Docker Compose file, use it to start the app.
Open PowerShell in the project folder and run:
docker compose up
If the project provides a Windows start file, double-click that file instead.
If the project uses a batch file, it may be named something like:
start.bat
The first run can take a while.
Docker may need to download images and build parts of the app. Let it finish before you close anything.
If the pipeline starts a local web page, open the address shown in the terminal.
Common addresses include:
http://localhost:3000http://localhost:8080http://127.0.0.1:8000
If the project uses a desktop window instead, wait for that window to appear.
A normal workflow looks like this:
- Open the app
- Load your genome file or sample folder
- Pick the analysis you want
- Start the run
- Wait for the process to finish
- Open the report or output folder
Most users will start with a VCF file or a completed whole genome dataset. If you have raw reads, the pipeline can process those first and then move through the later analysis steps.
This project works with common genome data files and result files.
- FASTQ for raw sequence reads
- BAM for aligned reads
- VCF for variant results
- CSV or TSV for tabular output
- HTML or PDF for reports
If you see folders named input, output, or results, keep your files in the right folder before you run the pipeline.
These steps help avoid common setup issues.
- Keep the project in a short folder path
- Close other heavy apps before you start
- Make sure Docker Desktop is running
- Give Docker enough memory in its settings
- Use a disk with enough free space for large genome files
If the app feels slow, the data size is usually the cause. Whole genome files are large, and normal home PCs need time to finish the work.
A simple run may look like this:
- Download the repository
- Install Docker Desktop
- Open the project folder
- Start the pipeline
- Add your sample file
- Run the analysis
- Open the output folder
- Review the report files
The pipeline can produce a range of files that help you review your genome data.
- Variant lists
- ClinVar matches
- Pharmacogenomics results
- Mitochondrial findings
- Structural variant calls
- Score files for trait review
- Summary reports in HTML or text form
Some outputs are meant for review, not for diagnosis. Use them as data files and reports that help you understand your sample.
- Restart your PC
- Open Docker Desktop again
- Check that virtualization is on in BIOS
- Make sure no older Docker install is blocking it
- Close large apps
- Give Docker more memory
- Move files to an SSD
- Make sure you have enough free disk space
- Check the file name
- Check the file folder
- Use a short path like
C:\GenomeData\sample.vcf - Make sure the file type matches what the pipeline expects
- Start it from PowerShell, not by double-clicking files that need terminal output
- Read the error message before closing the window
- Check that Docker is running first
This pipeline is built to run on your own computer.
- No cloud account needed
- No file upload required
- Your genome data stays local
- Docker keeps the environment isolated from other apps
This project covers topics like:
- ancestry
- bioinformatics
- cancer predisposition
- ClinVar
- DeepVariant
- Docker
- genomics
- GRCh38
- mitochondrial analysis
- personal genomics
- pharmacogenomics
- PharmCAT
- polygenic risk scores
- structural variants
- telomere analysis
- variant calling
- VCF
- whole genome sequencing
A typical project layout may include:
docker-compose.ymlfor starting servicesconfig/for pipeline settingsdata/for input filesoutput/for resultsscripts/for helper commandsdocs/for extra notes
If the folder names differ, use the names found in the repository files.
If you are new to this, follow this order:
- Install Docker Desktop
- Download the repository
- Extract the files
- Open the project folder
- Start the pipeline
- Load your genome file
- Review the output files