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COnstraint-Based Reconstruction and EXascale Analysis

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This package provides constraint-based reconstruction and analysis tools for exa-scale metabolic modeling in Julia.

This repository contains the "legacy" version of COBREXA (version 1.x). COBREXA development is continuing at github.com/COBREXA with a much-improved version 2.x, and many other new packages. Unless you need version 1.x for compatibility reasons, we recommend switching to version 2!

How to get started

Prerequisites and requirements

Operating system: Use Linux (Debian, Ubuntu or centOS), MacOS, or Windows 10 as your operating system. COBREXA has been tested on these systems.
Julia language: In order to use COBREXA, you need to install Julia 1.0 or higher. Download and follow the installation instructions for Julia here.
Hardware requirements: COBREXA runs on any hardware that can run Julia, and can easily use resources from multiple computers interconnected on a network. For processing large datasets, you are required to ensure that the total amount of available RAM on all involved computers is larger than the data size.
Optimization solvers: COBREXA uses JuMP.jl to formulate optimization problems and is compatible with all JuMP supported solvers. However, to perform analysis at least one of these solvers needs to be installed on your machine. For a pure Julia implementation, you may use e.g. Tulip.jl, but other solvers (GLPK, Gurobi, ...) work just as well.

:bulb: If you are new to Julia, it is advisable to familiarize yourself with the environment first. Use the Julia documentation to solve various language-related issues, and the Julia package manager docs to solve installation-related difficulties. Of course, the Julia channel is another fast and easy way to find answers to Julia specific questions.

Quick start

COBREXA.jl documentation is available online (also for development version of the package).

You can install COBREXA from Julia repositories. Start julia, press ] to switch to the Packaging environment, and type:

add COBREXA@1

(Note: the above command installs the "1.x" version of COBREXA, which is what is described in this repository and the associated documentation. If you are not limited by compatibility and similar reasons, consider updating to COBREXA 2.)

You also need to install your favorite solver supported by JuMP.jl (such as Gurobi, Mosek, CPLEX, GLPK, Clarabel, etc., see a list here). For example, you can install Tulip.jl solver by typing:

add Tulip

Alternatively, you may use prebuilt Docker and Apptainer images.

If you are running COBREXA.jl for the first time, it is very likely that upon installing and importing the packages, your Julia installation will need to precompile their source code from the scratch. In fresh installations, the precompilation process should take less than 5 minutes.

When the packages are installed, switch back to the "normal" julia shell by pressing Backspace (the prompt should change color back to green). After that, you can download a SBML model from the internet and perform a flux balance analysis as follows:

using COBREXA   # loads the package
using Tulip     # loads the optimization solver

# download the model
download("http://bigg.ucsd.edu/static/models/e_coli_core.xml", "e_coli_core.xml")

# open the SBML file and load the contents
model = load_model("e_coli_core.xml")

# run a FBA
fluxes = flux_balance_analysis_dict(model, Tulip.Optimizer)

The variable fluxes will now contain a dictionary of the computed optimal flux of each reaction in the model:

Dict{String,Float64} with 95 entries:
  "R_EX_fum_e"    => 0.0
  "R_ACONTb"      => 6.00725
  "R_TPI"         => 7.47738
  "R_SUCOAS"      => -5.06438
  "R_GLNS"        => 0.223462
  "R_EX_pi_e"     => -3.2149
  "R_PPC"         => 2.50431
  "R_O2t"         => 21.7995
  "R_G6PDH2r"     => 4.95999
  "R_TALA"        => 1.49698
  ⋮               => ⋮

The main feature of COBREXA.jl is the ability to easily specify and process a huge number of analyses in parallel. You. You can have a look at a longer guide that describes the parallelization and screening functionality, or dive into the example analysis workflows.

Testing the installation

If you run a non-standard platform (e.g. a customized operating system), or if you added any modifications to the COBREXA source code, you may want to run the test suite to ensure that everything works as expected:

] test COBREXA

Prebuilt images

Docker image is available from the docker hub as lcsbbiocore/cobrexa.jl, and from GitHub container repository. Download and use them as usual with docker:

docker run -ti --rm lcsbbiocore/cobrexa.jl:latest

# or alternatively from ghcr.io
docker run -ti --rm ghcr.io/lcsb-biocore/docker/cobrexa.jl:latest

In the container, you should get a julia shell with the important packages already installed, and you may immediately continue the above tutorial from using COBREXA.

Apptainer (aka Singularity) images are available from GitHub container repository. To start one, run:

singularity run oras://ghcr.io/lcsb-biocore/apptainer/cobrexa.jl:latest

...which gives you a running Julia session with COBREXA.jl loaded.

If you require precise reproducibility, use a tag like v1.2.2 instead of latest (all releases since 1.2.2 are tagged this way).

Acknowledgements

COBREXA.jl is developed at the Luxembourg Centre for Systems Biomedicine of the University of Luxembourg (uni.lu/lcsb), cooperating with the Institute for Quantitative and Theoretical Biology at the Heinrich Heine University in Düsseldorf (qtb.hhu.de).

The development was supported by European Union's Horizon 2020 Programme under PerMedCoE project (permedcoe.eu) agreement no. 951773.

If you use COBREXA.jl and want to refer to it in your work, use the following citation format (also available as BibTeX in cobrexa.bib):

Miroslav Kratochvíl, Laurent Heirendt, St Elmo Wilken, Taneli Pusa, Sylvain Arreckx, Alberto Noronha, Marvin van Aalst, Venkata P Satagopam, Oliver Ebenhöh, Reinhard Schneider, Christophe Trefois, Wei Gu, COBREXA.jl: constraint-based reconstruction and exascale analysis, Bioinformatics, Volume 38, Issue 4, 15 February 2022, Pages 1171–1172, https://doi.org/10.1093/bioinformatics/btab782