Plotly BioVisualization with Python

#️⃣ What is the purpose of this repo?

Tremendous advances in the field of biotechnology enable researchers and scientists to produce and obtain a diverse and huge amount of biological data. Therefore, there is a corresponding increase in the way of visualizing the biological data to represent information and science through art in a concise and meaningful way.

In this respect, Plotly has recently emerged and it can be used to create interactive charts and state-of-the-art graphs in a wide range of visualization applications including visualization of sequences, genomes, alignments, gene expressions.

So, I have wanted to learn how to use this library with Python, create some great charts and shared them.

#️⃣ How it looks like in general?

#️⃣ Requirements and Installation

• Create a directory and download all files or clone the repository via Git using the following command:

  git clone https://github.com/furkanmtorun/Plotly_BioVisualization.git

• Install the required packages if you do not already:

  pip install -r requirements.txt

• Then, the turn to run it:

  python Plotly_BioVisualization.py

• It's ready to use now! Open your web browser and go to:

  http://127.0.0.1:8050

If you did not install pip yet, please follow the instruction here.

In the development process, Python 3 has been used. The use of Python 2.x might cause compatibility issues.

#️⃣ How to create interactive graphs and charts?

• #️⃣ Needle Plot

  • Purpose: It allows you to illustrate the mutations or other changes on the corresponding positions of amino acids within the protein sequence together with the protein domains.

  • Data structure: JSON / Dict = { x: [], y: [], domains: [], mutationGroups: [] }

    

  • Coding (just review the code, the comments are over there!):

    

  • How it looks like?:

• #️⃣ Sequence/Alignment Viewer

  • Purpose: It allows you to visualize the genomics and transcriptomic sequence with several features such as coverage, gaps, consensus, and heatmap overview.

  • Data Structure: FASTA / Clustal

  • Coding (just review the code, the comments are over there!):

  • How it looks like?

• #️⃣ Manhattan Plot

  • Purpose: Manhattan Plot is a type of scatter plot and commonly used in genome-wide association studies (GWAS) to visualize display significant SNPs efficiently.

    The genome-wide significance threshold was set as 5e-8 and plotted with green line, and the most significant SNPs are colored in red.

  • Coding (just review the code, the comments are over there!):

  • How it looks like?

    

#️⃣ Sources & References

• Hossain, S. (2019). Visualization of Bioinformatics Data with Dash Bio. Proceedings of the 18th Python in Science Conference. doi: 10.25080/majora-7ddc1dd1-012.
• Plotly Dash Bio Announcement Blog Post: https://medium.com/plotly/announcing-dash-bio-ed8835d5da0c
• Plotly Dash Bio Page: https://dash.plot.ly/dash-bio
  • Alignment Chart Details: https://dash.plot.ly/dash-bio/alignmentchart
  • Manhattan Plot Details: https://dash.plot.ly/dash-bio/manhattanplot
  • Needle Plot Details: https://dash.plot.ly/dash-bio/needleplot
• Plotly Bioinformatics Notebook: https://plot.ly/python/v3/ipython-notebooks/bioinformatics/
• Plotly HTML Components Page: https://dash.plot.ly/dash-html-components
• Bulma.css Documentation: https://bulma.io/documentation/
• Create and share beautiful screenshots: https://carbon.now.sh/

Thanks for all these resources!

#️⃣ Contributing & Feedback

I would be very happy to see any feedback and contributions on the repository.

I will keep continuing to append new interactive graphs and figures as much as I can.

Furkan Torun — furkanmtorun@gmail.com

Twitter: @furkanmtorun

Website: furkanmtorun.github.io

Academics: Google Scholar Profile