GithubHelp home page GithubHelp logo

sablokgaurav / atlasxanalyses Goto Github PK

View Code? Open in Web Editor NEW

This project forked from epfl-lcsb/atlasxanalyses

0.0 0.0 0.0 19 KB

The data and scripts contained in this repository allow the user to reproduce the figures and analyses of the article "ATLASx: a computational map for the exploration of biochemical space", doi: https://doi.org/10.1101/2021.02.17.431583

License: Apache License 2.0

Python 99.73% Makefile 0.27%

atlasxanalyses's Introduction

ATLASx Analysis tools

The data and scripts contained in this repository allow the user to reproduce the figures in the ATLASx manuscript from the original data files. Reference to the article (doi): https://doi.org/10.1101/2021.02.17.431583

1) Installation

The installation can be completed in less than 10 minutes, including installation of dependencies and fetching the data from the git repository.

Requirements

  • python 2.7 or higher (Tested and recommended: python 3.9.6)
  • pip

The code is adapted for python 3, but it can also be executed in python 2.s

Runtimes are indicated for each script and were determined normal desktop computer (macOS).

Download repository

$ git clone https://github.com/EPFL-LCSB/ATLASxAnalyses

To install the required dependencies:

$ cd ATLASxAnalysis $ make

Note

Data files are stored using git large file storage (lfs). The make file will install git lfs automatically. However, if lfs was not installed previously, the repository has to be updated after installation:

$ git pull

This is needed to retrieve the data files from the repository after installation.

2) Reproduce Network Analysis

$ cd NetworkAnalysis/Source

Plot component distribution for database scopes

$ python3 get_component_distribution.py #Runtime: 97s

By default, the database scopes will be plotted. For a resolution by data source, add data_sources as an argument to the above command:

$ python3 get_component_distribution.py data_sources #Runtime: 11s

CSV file conversion

CVS files of networks are quite practical for visualisation, e.g. in the Gephi software. To convert the gpickle files for database scopes to CSV, run the following:

$ python3 print_csv_from_gpickle.py #Runtime: 52s

As above, for single data sources use:

$ python3 print_csv_from_gpickle.py data_sources #Runtime: 50s

The output is automatically written to a new folder called ATLASxAnalyses_output created in the same directory as the repository.

The data files used in the repository is the same as the one used in the manuscript, which has been downloaded on 8 November 2020. For updated network files, please contact the authors of the paper directly.

3) Reproduce MetaCyc pathway coverage plot

The data and scripts contained in this repository allow the user to reproduce the Figure 3: "Pathway search comparison to dataset of pathways extracted from MetaCyc" of the ATLASx manuscript from the original data files and the Supplementary Figures S3 and S4: "Pathway search comparison to dataset of pathways extracted from MetaCyc for BNICE.ch-curated reactions" and "Distribution of the length (i.e., number of reaction steps) of reconstructed MetaCyc pathways."

$ cd MetaCycPWanalysis

Perform the pathway search for MetaCyc, MetaCyc BNICE-curated, ATLASx and ATLASx BNICE-curated (running time: 5 min for edges coverage, approx. 2.5 days on 10 cores for pathway search)

$ python3 check_coverage_and_rank_pathways.py

Running this script will produce 5 output files within the output folder:

  • networkEdgesCoverage.csv
  • pw_ranking_chemATLAS_hp.csv
  • pw_ranking_chemATLAS.csv
  • pw_ranking_MetaCyc_hp.csv
  • pw_ranking_MetaCyc.csv

As the script takes long time to be executed we recommend to use the existing files that are stored in "output_generated_for_article" folder for plotting (copy them to output folder):

To generate the plots run:

$ python3 plot_edges_coverage.py

$ python3 plot_pathway_rank.py

The 2 scripts will generate the figures used in the publication within the "plots" folder: MetaCycVsATLASx.png - figure 3A of the manuscript MetaCycVsATLASxBNICE.png - Supplementary Figure 3A pathways_rank_chemATLAS_hp.png - figure 3B of the manuscript pathways_rank_chemATLAS.png - Supplementary Figure 3B pathways_rank_MetaCyc_hp.png - figure 3B of the manuscript pathways_rank_MetaCyc.png - Supplementary Figure 3B plotMetacycPathwayLengthDistributionAll_all.png - main part of the Supplementary Figure 4 plotMetacycPathwayLengthDistributionAll_crop.png - cropped in part of the Supplementary Figure 4

atlasxanalyses's People

Contributors

jasminhafner avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.