raven-toolbox

Reconstruction, Analysis and Visualisation of Metabolic Networks — in Python.

raven-toolbox is the Python counterpart of the RAVEN Toolbox 2 (MATLAB). It builds on cobrapy for everything cobrapy already does well (simulation, standard analyses, SBML I/O, model manipulation) and adds the functionality that's unique to RAVEN:

• De novo reconstruction from KEGG and protein homology (BLAST / DIAMOND).
• Context-specific models from omics data via tINIT / ftINIT, with task-aware gap-filling and the linear-merge MILP reduction.
• Metabolic-task validation (check_tasks, fitTasks).
• Connectivity gap-filling against template models.
• Omics integration — Human Protein Atlas (proteomics + RNA-seq) ingestion.
• Sub-cellular localisation prediction by MILP, with partial-update mode and pluggable evidence sources (DeepLoc 2, MULocDeep, COMPARTMENTS, UniProt, …).
• N-model comparison; reporter metabolites; FSEOF; flux sampling.
• YAML I/O following the cobra standard, plus geckopy's ec-* enzyme-constrained fields. SIF export. RAVEN-style Excel export.

The status of every RAVEN function (ported, cheatsheet-mapped to cobra, or explicitly not ported) is documented function-by-function in docs/raven_migration.md.

Design principle

The canonical in-memory object is always a cobra.Model. There is no parallel RAVEN struct, no ravenCobraWrapper-style adapter. RAVEN-specific fields that cobra doesn't model natively (rxnMiriams, metDeltaG, rxnConfidenceScores, …) live in cobra's annotation / notes dictionaries. This avoids duplicating cobra's data model and keeps raven-toolbox interoperable with the wider COBRA ecosystem.

Status

raven-toolbox has been validated against MATLAB RAVEN on Human-GEM (5 Hart2015 cell-line models, Jaccard 0.975–0.980 — see docs/humangem_validation.md). The functional scope of the original RAVEN toolbox is covered with three principled omissions, all deliberately out of scope rather than pending work:

• MetaCyc-based reconstruction is not implemented and is flagged for removal from MATLAB RAVEN as well — see IMPROVEMENTS.md under R-MetaCyc.
• Dynamic FBA is not implemented — several maintained Python packages already cover it (dfba, reframed, mewpy).
• Metabolomics-based scoring in tINIT / ftINIT is not implemented — passing a non-empty metabolomics argument raises NotImplementedError.

Installation (development)

git clone https://github.com/SysBioChalmers/raven-toolbox
cd raven-toolbox
pip install -e ".[dev]"

raven-toolbox requires Python ≥ 3.11. Genome-scale (f)tINIT MILPs currently require Gurobi (details on solver portability); toy and unit-test work runs on the open-source GLPK.

External command-line tools (BLAST, DIAMOND, HMMER, MAFFT, CD-HIT)

Some workflows call external tools. For most users there is nothing to do — raven-toolbox downloads each tool it needs automatically the first time it's used.

Which tools a workflow uses:

Optional:

• Fetch them up front instead of on first use:

  raven-toolbox-binaries --set runtime   # blastp, makeblastdb, diamond, hmmsearch
  raven-toolbox-binaries --set build     # hmmbuild, mafft, cd-hit
  

• Use your own install — if a tool is on your PATH it's used instead of a download, e.g. conda install -c bioconda blast diamond hmmer mafft cd-hit.
• Disable automatic downloads (air-gapped / conda-only setups): set RAVEN_PYTHON_AUTOFETCH=0.

Windows: homology reconstruction and the KEGG species model work as-is. To build the KEGG HMM libraries (needs MAFFT/CD-HIT), use WSL2.

Documentation

The documentation is built with Sphinx (MyST Markdown); the source lives in docs/ — see docs/README.md for the layout and local-build instructions. (A hosted ReadTheDocs site is not yet published.)

Relationship to MATLAB RAVEN

raven-toolbox is an independent Python reimplementation of the RAVEN Toolbox 2, released under the permissive MIT license. If you use it in scientific work, please cite the RAVEN 2 paper:

Wang H, Marcišauskas S, Sánchez BJ, Domenzain I, Hermansson D, Agren R, Nielsen J, Kerkhoven EJ. (2018) RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput Biol 14(10): e1006541.

License

MIT