First and foremost, thanks for you interest in using our computational framework to study FV-deficiency.
A bioinformatics and machine learning framework designed to study FV-deficiency of the Factor V (code: 7KVE), although in the final paper we used a relaxed version of the structure, whose structure you can find here. The paper is available here.
This repository aims to show how the pipeline shown in this figure was built.
The source code provided in this repository can be executed using the environment which will be constructed with the next steps. Initially lets build a image based on the Dockerfile recipe available here. Execute the following comand in the root of the repository:
docker-compose build
Then build a container based on the image created in the previous step. Execute the following comand in the root of the repository:
docker-compose up
Example of expected result:
Before entering the container is needed to find its id:
docker container ps
Example of expected result:
The id of this container is: 76f3d9ee0a39. To enter the container one can execute the following code:
docker container exec -it 76f3d9ee0a39 bash
Example of expected result:
Obs:
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The outputs created while inside the container stays in the container after stopped. Feel free to map a volume in the
docker-compose.ymlin order to have the files in your local machine. -
It is also possible to customize your own local machine to execute the code. The markdown files found in supplementary info folder available here may be usefull to you. The hardest part to configure is explained in detail here.
The features dataset contains structural, graph and conservation information of the pdb while the target was constructed using the clinical data as specified in the article available here.
The structural features were made using the Chimera software and the parse_chimera_features.py script, available here. To understand input and output of this step check the supplementary information available here.
The graph features were created using the Rinerator program with the produce_graph_features.py script, available here. To understand input and output of this step check the supplementary information available here.
The FV_mutations.csv dataset available here is the final version used after processing as specified in the article.
Ferreira-Martins, A.J., Castaldoni, R., Alencar, B.M. et al. Full-scale network analysis reveals properties of the FV protein structure organization. Scientific Reports 13, 9546 (2023). https://doi.org/10.1038/s41598-023-36528-z
If you have comments, suggestions or found any issues in our code, please contact us.
André Juan Ferreira Martins:
- Email: andre.jfmdm@gmail.com
Rodrigo Cabrera Castaldoni:
- Email: castaldoniro@gmail.com
Tiago Jose da Silva Lopes:
- Email: tiago-jose@ncchd.go.jp
On behalf of all of the authors, we hope that this computational framework is useful for your research.