PanGraphViewer -- show panenome graph in an easy way

Versions and dependences

Here we provide two application versions:

● Desktop-based application
● Web browser-based application

Overall, Python3 is needed to run this software and we highly recommend using miniconda3 to install all python3 libraries.

● On Windows system, you can download miniconda3 at

● On macOS system, you can download miniconda3 at  

● On Linux system,  you can download miniconda3 at

After the installation of miniconda3, you can follow the steps below to ensure panGraphViewer can be executed.

Desktop-based panGraphViewer

Library installation for the desktop-based version

Steps on different systems

  • If you use Windows system, you may need to find or search Anaconda Powershell Prompt (miniconda3) first and then open it.

  • If you use macOS or Linux system, you may open Terminal first and then type the command line below

    $ export PATH=/full/path/to/miniconda3/bin:$PATH # modify the path based on your ENV

After the steps above, you can install the python3 libraries by typing:

conda config --add channels conda-forge
conda config --add channels bioconda
conda install pyqt pyqtwebengine configparser pandas bokeh==2.2.3 dna_features_viewer natsort attrdict networkx 

If you use pip, you can install the python3 libraries like:

pip install PyQt5 PyQtWebEngine configparser pandas bokeh==2.2.3 dna_features_viewer natsort attrdict networkx

or you can use pip to install like (need to go to the panGraphViewerApp directory first)

pip install -r requirement.txt ## On Linux or macOS system
pip install -r requirement_windows.txt ## On Windows system


  1. On Linux or macOS system, pysam is needed. You may install this package using

    $ conda install pysam 
  2. On Windows platforms, as pysam is not available, we use a windows-version samtools package instead. Additional libraries below are needed and can be installed using

    > conda install m2-base pyfaidx

Start the desktop-based version

  1. On Linux or macOS system, you may use the command line below in Terminal to open the software.

    $ cd /full/path/to/panGraphViewer/panGraphViewerApp # modify the path based on your ENV
    $ python
  2. On Windows system, you may search and open Anaconda Prompt (miniconda3) first and then move to the panGraphViewer directory. For example, if you have put panGraphViewer on your Desktop and the opened Anaconda Prompt (miniconda3) is in your C drive, you may use the command line below to start the program:

    > cd C:\Users\%USERNAME%\Desktop\panGraphViewer\panGraphViewerApp
    > python

    If you have put panGraphViewer on other drive, you may need to move to the target drive first. For instance, the target drive is D, you can move to the drive by typing D: in Anaconda Prompt (miniconda3) and then move to the panGraphViewer directory to execute

    Please NOTE that on Windows system, you need to use backslash \ rather than the common slash / to move to the target directory.

  3. The logging information will show in Anaconda Prompt (miniconda3) or Terminal depending on the system you use (Will be good for you to monitor the status of the application).

Web-based panGraphViewer

To meet different requirments, we have also created a web-based panGraphViewer. Basically, most functions provided in the Desktop-based version have been implemented in the Web browser-based version. Users can install this version locally or directly deploy this online. The web browser-based verison offers administrative functions to help create accounts for different users.

Library installation for the web-based version

Depending on the systems used, users can use pip directly to install the needed python3 libraries after moving to the panGraphViewerWeb directory.

pip install -r requirement.txt ## On Linux or macOS system
pip install -r requirement_windows.txt ## On Windows system

As mentioned in the desktop-based version, pysam cannot be installed on Windows systems, users need to install alternatives on Windows by using

> conda install m2-base pyfaidx

For Linux or macOS users, pysam can be installed directly using

$ conda install pysam

Start the web-based version

After the installation above, users can move to the panGraphViewerWeb directory by referring to the steps mentioned in the desktop version through Terminal or Anaconda Prompt (miniconda3).

Note that the folder needed here is panGraphViewerWeb.

Once moving to the panGraphViewerWeb directory, users can start the application by typing

python runserver <IPaddress>  ## on local machine the IPaddress can be: localhost:8004

or users can use the CMD below to start the Web browser-based version

$ bash   ## On linux or macOS system.
> run.bat ## On Windows system

Note: the IP in can be modified accordingly

Once the words Starting development server at http://localhost:8004/ or similar infomation is shown, user can open a browser to open the web-based panGraphViewer.

The admin page is http://localhost:8004/admin and the inital admin info is:

Account: admin
password: abcd1234

Note: please use the go back button provided by the web browser to move back rather than directly clicking the corresponding functions in the web page to perform analyses.

The Files needed in the application

The rGFA file

  1. If you have multiple high-quality genome assemblies from different individuals, you may use minigraph (Linux preferred) to generate a reference GFA (rGFA) file.

    Before the running, the header of the fasta file needs modifying. For example, if you have a fasta file from Sample1 with a header like:


    You may modify the header to:


    On Linux, the command lines that can be used to achieve this are:

    $ sample="" ## the name of the sample. For instance: Sample1
    $ fasta="" ## full path to the fasta file
    $ name=`echo $fasta | rev | cut -d"." -f2-| rev`
    $ sed -e "s/>/>${sample}||/g" $fasta > ${name}.headerModified.fasta

    We also provide a python script to help this conversion. The script can be found in the scripts folder under panGraphViewer --> panGraphViewerApp. Or users can use the UI to convert by clicking Tools --> Format Conversion --> Modify FASTA Header.

    usage: [-h] [--version] [-f FASTA] [-n NAME] [-o OUTPUT]
    rename the header of a given fasta file
    optional arguments:
      -h, --help  show this help message and exit
      --version   show program's version number and exit
      -f FASTA    a fasta format file
      -n NAME     name of the sample
      -d DELIM    delimiter. Default: '||'
      -o OUTPUT   the output directory

    Please NOTE that:

    I). If you do not modify the header of your fasta file and directly use minigraph to generate the rGFA file, panGraphViewer can still read the file, while many features, such as where the node comes from would not show in detail. A warning message will display in both UI and the opened Terminal or powershell.

    II). For the sample name, please DO NOT include ||.

  2. If you don't have an rGFA file, but a GFA file, you may try to follow the standard here to convert your GFA file into an rGFA file. After generating an rGFA file, you can use this software to visualise the graph of interest.

The VCF file

We also accept a VCF file to show the graph. Basically, a reference FASTA file is optional if the VCF is a standard one. The program will automatically check the input VCF file and evaluate if the VCF file meets the requirement. If not, a message will show.

VCF filtration is highly recommended before plot the graph.

We also provide a method to help convert a VCF file to an rGFA file. Users can perform the conversion directly through the interface provided in the application or directly use under the panGraphViewer --> panGraphViewerApp --> scripts folder.

Note: If there are many variations in the VCF file, we recommend using directly to convert by chromosomes rather than converting entirely. This will save a lot of computing resource when plot graphs.

The usage of is shown below. Both Windows and Linux/macOS users can directly use this script to convert a VCF file to an rGFA file.

usage: [-h] [--version] [-f FASTA] [-b BACKBONE] [-v VCF] [-o OUTPUT] [-c [CHR [CHR ...]]] [-n NTHREAD]
Convert a vcf file to an rGFA file
optional arguments:
    -h, --help          show this help message and exit
    --version           show program's version number and exit
    -f FASTA            a fasta format file that from the backbone sample
    -b BACKBONE         the name of the backbone sample
    -v VCF              the vcf file
    -o OUTPUT           the output directory
    -c [CHR [CHR ...]]  the name of the chromosome(s) [default: all chroms]
    -n NTHREAD          number of threads [default: 4]

The BED file

Basically, the BED file should contain the annoation information from the backbone sample. There should be at least 6 columns in the BED file.

Column Information
1 Chromosome ID
2 Gene start position
3 Gene end position
4 Gene ID
5 Score (or others; the program does not use the info in this column)
6 Orientation

Users can load the BED file to check the overlaps between variations and genes. By default, genes overlapping with more than 2 nodes will be shown in the dropdown menu. A gene list will be saved in the output directory after parsing the BED file.


The minimum computing resource needed

The minimum computing resource needed for running the application

Memory:  1Gb
Threads: 2

Which application should I use

For the desktop-based application, it is optimized on Windows 10 and macOS Big Sur. Ubuntu 18.04.5 is also tested. For Linux operating system version below Ubuntu 18.04.5 or equivalent, such as Ubuntu 16.04, PyQtWebEngine may not work properly. For other versions of operating systems, the desktop-based application may still work, however, the layout of the application may differ.

For the web browser-based version, we suggest running in Linux or macOS environment. If users want to run on Windows systems, Windows 10 or above is recommended. Users can also use docker to run the web browser-based version. However, WSL is needed to run the docker version on Windows 10 or above.

The backbone sample

The backbone sample is the one used as the main sequence provider to produce the pangenome graph or the reference sample to produce the VCF file. In the pangenome graph, most of the nodes are from the backbone sample (shared by all) with some nodes (variations) from other samples.

The colors showed in the graph

Each sample uses one particular colour and the most frequent colour should be the one used for the backbone sample. The colours are randomly selected by the program from a desgined colour palletes.

The type of graphs

We provides two kinds of graph plots in the program to achieve a good performance and visualisation. By default, if the number of checked nodes <= 200, vis.js based graph will show. Otherwise, a cytoscape.js based graph will show. Users can change the settings in the desktop-based application.

The shapes showed in the graph

If you use a VCF file to show graphs, we use different nodes shapes to represent different kinds of variants. For instance, in the default settings for the vis.js based graph, dot represent SNP, triangle represents deletion, triangleDown reprsents insertion, database represents duplication, text shows inversion and star represent translocation. Users can change the corresponding settings to select preferred node shapes to represent different variations on the desktop-based application.

How to use the program

For more detailed steps to run panGrapViewer, please refer to the Manual

Different variations

If users use a VCF file to generate a graph genome, when moving the mouse to the graph node, the program will automatically show the variation types, such as SNP(single nucleotide polymorphism), INS (insertion), INV (inversion) and DUP (duplication). The corresponding nodes from the backbone sample will also be linked and shown.

Enjoy using panGraphViewer!