mave2imap 1.0.0.0.5

This project is intended for infering and mapping interface hotspots based on results from MAVE (Multiplexed Assays of Variant Effects).

mave2imap

Table of contents

• mave2imap
• • Table of contents
  • Description
  • Install mave2imap conda environment
  • Testing
    • 1) Create a folder to download required data and run the test :construction:
    • 2) Run mave2imap pipeline for each targeted region. :computer:
    • 3) Analyze results using jupyter notebook(s). :mag_right:
  • Citing mave2imap
  • Copyright
    • Acknowledgements

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Description

This code is intended for 3D mapping of interface hotspots based on the most perturbed positions inferred from MAVE (Multiplexed Assays of Variant Effects) results. (See publication)

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Install mave2imap conda environment

$ conda env create -f https://raw.githubusercontent.com/synth-bio-evo/mave2imap/main/mave2imap.yml

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Testing

Requires about >= 64 Gb RAM to process the full dataset.
If you do not dispose of this amount of RAM you can create smaller .fastq.gz files by using the following command:

gunzip -cd <file>.fastq.gz | head -n 1600000 | gzip > <file_400k_reads>.fastq.gz

• Replace "<file>" by your filename
• It will extract and compress 1,6x10⁶ lines from "<file>.fastq.gz", corresponding to 4x10⁵ reads, and create "<file_400k_reads>.fastq.gz"

1) Create a folder to download required data and run the test :construction:

mkdir /tmp/test
cd /tmp/test

Data, notebooks used to produce the manuscript figures, results, and datasets are available at Zenodo (doi: 10.5281/zenodo.15690360; https://zenodo.org/records/15690360). Below you will find an exemple about how to use them.

If you have aria2c installed (faster):

aria2c -j 16 https://zenodo.org/records/15690361/files/Asf1B+IP3.tar.gz?download=1

else,

wget https://zenodo.org/records/15690361/files/Asf1B+IP3.tar.gz?download=1

Uncompress the .tar.gz file

tar -xvzf Asf1B+IP3.tar.gz

2) Run mave2imap pipeline for each targeted region. :computer:

Example:

cd Asf1B+IP3/Asf1_N-Ter
mave2imap -i Asf1_N-ter.ini
cd ../Asf1_C-Ter
mave2imap -i Asf1_C-ter.ini

This will produce the data required for analysis and visualization using the proposed jupyter notebook.

:microscope: The information available in the output file, "result_thresh3_2_2_compare_conditions.out", is probably the most relevant to a classical user.

3) Analyze results using jupyter notebook(s). :mag_right:

• Enter main folder and launch jupyter-lab

cd /tmp/test/Asf1B+IP3
jupyter-lab

Interface mapping (imap) and fitness assessment notebooks are available in the corresponding folders

• Open the notebook
• Choose mave2imap kernel
• If required edit the code according to your specific case (not required for the testing dataset)
• Click in "Run" (menu) => "Restart Kernel and Run All Cells"
  • If you are running the imap notebook, the most perturbed positions should be indicated below the last cell based on the defined threshold, and you should be able to visualized/manipulated the 3D interactive complex (most perturbed regions are indicated by reddish gradient).
  • If you are running the fitness notebook, by mutation results will be outputted to a CSV file.

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Citing mave2imap

"Publication is coming ..."

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Copyright

Copyright (c) 2025, Raphaël Guérois (CEA-Saclay/DRF/Joliot/I2BC/SB2SM/LBSR), Oscar H.P. Ramos (CEA-Saclay/DRF/Joliot/MTS/SIMoS/LICB/SBE)

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.11.