Skip to content

docs: README with the documentation links and the logo for dark mode #193

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
edkerk merged 3 commits into from
Nov 1, 2026
Merged

docs: README with the documentation links and the logo for dark mode #193

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
617a426
docs: README with the documentation links and the RAVEN logo for dark…
edkerk Nov 1, 2026
647ecae
docs: use the Jaccard range of the validation study
edkerk Nov 1, 2026
9fc0088
docs: README wording
edkerk Nov 1, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
164 changes: 54 additions & 110 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,146 +1,90 @@
# raven-toolbox
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/CompMetEng/raven-toolbox/develop/img/raven-logo-white.png">
<img src="https://raw.githubusercontent.com/CompMetEng/raven-toolbox/develop/img/raven-logo.png" alt="RAVEN" width="400px">
</picture>

[![CI](https://github.com/CompMetEng/raven-toolbox/actions/workflows/ci.yml/badge.svg)](https://github.com/CompMetEng/raven-toolbox/actions/workflows/ci.yml)
[![PyPI](https://img.shields.io/pypi/v/raven-toolbox.svg)](https://pypi.org/project/raven-toolbox/)

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

`raven-toolbox` is the Python counterpart of the
[RAVEN Toolbox](https://github.com/CompMetEng/RAVEN) (MATLAB). It builds on
[**cobrapy**](https://github.com/opencobra/cobrapy) for everything cobrapy already does
well — simulation, standard analyses, SBML I/O, model manipulation — and adds the
functionality that is unique to RAVEN:

* **De novo reconstruction** from KEGG and protein homology (BLAST / DIAMOND).
* **Context-specific models** from omics data via **ftINIT**, with task-aware gap-filling,
the linear-merge MILP reduction, and metabolomics-informed reaction scoring.
* **Metabolic-task** validation (`check_tasks`, `find_task_essential_reactions`).
* **Gap-filling** — connectivity gap-filling against template models, plus LP/SWIFTCORE,
MILP, and topological strategies.
* **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** (ACHR, CHRR,
and the classic random-objective vertex method).
* **YAML I/O** following the cobra standard, plus geckopy's `ec-*` enzyme-constrained
fields, and **RAVEN-style Excel** export.

The status of every RAVEN function (ported, mapped to a cobra equivalent, or
explicitly not ported) is documented function-by-function in
**[RAVEN 3 and raven-toolbox](https://raven-docs.readthedocs.io/en/latest/raven3-vs-raven-toolbox.html)**.

## Design principle

The canonical in-memory object is always a [`cobra.Model`](https://cobrapy.readthedocs.io).
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.
[![PyPI](https://img.shields.io/pypi/v/raven-toolbox.svg?include_prereleases)](https://pypi.org/project/raven-toolbox/)
[![Documentation](https://readthedocs.org/projects/raven-docs/badge/?version=latest)](https://raven-docs.readthedocs.io/en/latest/)
[![GitHub Discussions](https://img.shields.io/github/discussions-search?query=repo%3ACompMetEng%2Fraven-toolbox&label=GitHub%20Discussions)](https://github.com/CompMetEng/raven-toolbox/discussions)

## Status
**[Documentation](https://raven-docs.readthedocs.io/en/latest/)** · [Installation](https://raven-docs.readthedocs.io/en/latest/installation/python.html) · [Guide](https://raven-docs.readthedocs.io/en/latest/guide/index.html) · [API reference](https://raven-docs.readthedocs.io/en/latest/api/index.html) · [Compare with RAVEN](https://raven-docs.readthedocs.io/en/latest/raven3-vs-raven-toolbox.html) · [Citations](https://raven-docs.readthedocs.io/en/latest/references.html)

**raven-toolbox 3.0.0b1** is out — the first beta toward 3.0, tracking the upcoming MATLAB
RAVEN 3.0 beta ([release notes](https://github.com/CompMetEng/raven-toolbox/releases/tag/v3.0.0b1),
[changelog](CHANGELOG.md)). It has been validated against MATLAB RAVEN on **Human-GEM** (5 Hart2015
cell-line models, Jaccard 0.975–0.980 — see
[the Human-GEM validation study](https://github.com/CompMetEng/raven-docs/blob/main/docs/parameter-tuning/studies/humangem-validation.md)
on raven-docs).
# raven-toolbox

Two deliberate scope decisions, not pending work:
Reconstruction, Analysis and Visualisation of Metabolic Networks in Python: the Python counterpart of the [RAVEN Toolbox](https://github.com/CompMetEng/RAVEN) (MATLAB). raven-toolbox builds on [cobrapy](https://github.com/opencobra/cobrapy) for simulation, standard analyses, SBML input and output, and model manipulation, and it adds the functionality that is specific to RAVEN:

* **Classic tINIT is not implemented — only ftINIT.** raven-toolbox is a new
implementation with no installed base of tINIT-built models to support; MATLAB RAVEN
keeps both algorithms for backwards compatibility, but ftINIT is the algorithm tINIT
was superseded by.
* **MetaCyc-based reconstruction** is not implemented and is flagged for removal from
MATLAB RAVEN as well — see [IMPROVEMENTS.md](IMPROVEMENTS.md) under `R-MetaCyc`.
* **Dynamic FBA** is not implemented — several maintained Python packages already cover
it ([`dfba`](https://pypi.org/project/dfba/), [`reframed`](https://pypi.org/project/reframed/),
[`mewpy`](https://pypi.org/project/mewpy/)).
- Reconstruction from KEGG and from protein homology (BLAST or DIAMOND).
- Context-specific models from omics data with ftINIT, with task-aware gap-filling, the linear-merge MILP reduction and scoring with metabolomics.
- Validation with metabolic tasks (`check_tasks`, `find_task_essential_reactions`).
- Gap-filling: connectivity gap-filling against template models, and LP, SWIFTCORE, MILP and topological strategies.
- Omics integration with Human Protein Atlas data (proteomics and RNA-seq).
- Prediction of sub-cellular localisation by MILP, with a partial-update mode and exchangeable evidence sources (DeepLoc 2, MULocDeep, COMPARTMENTS, UniProt).
- Comparison of several models, reporter metabolites, FSEOF, and flux sampling (ACHR, CHRR and the random-objective vertex method).
- YAML input and output that follows the cobra standard and adds the `ec-*` fields for enzyme-constrained models of [geckopy](https://github.com/CompMetEng/geckopy), and export to the RAVEN Excel format.

## Installation
[RAVEN 3 and raven-toolbox](https://raven-docs.readthedocs.io/en/latest/raven3-vs-raven-toolbox.html) gives the status of every RAVEN function: ported, mapped to a cobra equivalent, or not ported.

```bash
pip install raven-toolbox
```
## Design

The in-memory model is a [`cobra.Model`](https://cobrapy.readthedocs.io). raven-toolbox has no RAVEN struct and no adapter for one. Fields that cobra does not model, such as `rxnMiriams`, `metDeltaG` and `rxnConfidenceScores`, are kept in the `annotation` and `notes` dictionaries of cobra. This keeps raven-toolbox interoperable with the COBRA ecosystem.

## Status

That installs the latest **stable** release. PyPI pre-releases are opt-in, so get the
3.0.0b1 beta with:
raven-toolbox 3.x tracks the RAVEN 3 pre-releases and is a pre-release itself ([releases](https://github.com/CompMetEng/raven-toolbox/releases), [changelog](CHANGELOG.md)). On Human-GEM, the five cell-line models of Hart2015 extracted with ftINIT have a Jaccard index against MATLAB RAVEN of 0.964 to 0.982 without tasks and 0.981 to 0.988 with tasks ([Human-GEM validation](https://raven-docs.readthedocs.io/en/latest/parameter-tuning/studies/humangem-validation.html)).

Three functions of RAVEN are not implemented:

- Classic tINIT. raven-toolbox has no installed base of tINIT models, and ftINIT supersedes tINIT. MATLAB RAVEN keeps both for backward compatibility.
- MetaCyc-based reconstruction, which is flagged for removal from MATLAB RAVEN as well ([IMPROVEMENTS.md](IMPROVEMENTS.md), `R-MetaCyc`).
- Dynamic FBA, which maintained Python packages cover ([`dfba`](https://pypi.org/project/dfba/), [`reframed`](https://pypi.org/project/reframed/) and [`mewpy`](https://pypi.org/project/mewpy/)).

## Install

The 3.x releases are pre-releases, so pip needs `--pre`. Without it, pip installs the older stable release 0.4.0.

```bash
pip install --pre raven-toolbox
```

To work against the unreleased code on `develop` instead, install from git:
Python 3.11 or newer is required. The unreleased code on `develop` installs from git:

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

raven-toolbox requires Python ≥ 3.11. Genome-scale ftINIT MILPs currently require **Gurobi**
([details on solver portability](https://github.com/CompMetEng/raven-docs/blob/main/docs/parameter-tuning/studies/init-solver-benchmark.md)
on raven-docs); toy and unit-test work runs on the open-source GLPK.
Genome-scale ftINIT problems need Gurobi ([ftINIT on different solvers](https://raven-docs.readthedocs.io/en/latest/parameter-tuning/studies/init-solver-benchmark.html)), and small models and the unit tests run on the open-source GLPK. [Installation](https://raven-docs.readthedocs.io/en/latest/installation/python.html) describes the solver configuration.

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

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.
BLAST, DIAMOND and HMMER (and MAFFT and CD-HIT for building HMM libraries) are downloaded the first time a workflow needs them. A tool on the `PATH` takes precedence over a download, `raven-toolbox-binaries --set runtime` fetches the tools in advance, and `RAVEN_PYTHON_AUTOFETCH=0` disables downloads for air-gapped systems. [Download data and binaries](https://raven-docs.readthedocs.io/en/latest/installation/data-and-binaries.html) lists the tools per workflow, the cache locations and the platforms. On Windows, homology reconstruction and the KEGG species model run natively, and building the KEGG HMM libraries needs WSL2.

Which tools a workflow uses:
[SuppOrth](https://github.com/fma91/SuppOrth) orthologs can start a homology reconstruction. `hits_from_supporth` reads an existing `suppOrth.tsv` and needs nothing else. `run_supporth` needs the `suppOrth` command, which is not downloaded automatically and runs on Linux and macOS: `pip install 'git+https://github.com/fma91/SuppOrth.git@stand_alone'`, then `suppOrth install`.

| Workflow | Tools |
|---|---|
| Homology-based reconstruction | `blastp` + `makeblastdb`, or `diamond` |
| KEGG HMM query (`get_kegg_model_for_organism`) | `hmmsearch` |
| Building the KEGG HMM libraries (maintainers) | `hmmbuild`, `mafft`, `cd-hit` |
## Quick start

Optional:
```python
from raven_toolbox.io import read_yaml_model

- **Fetch them up front** instead of on first use:
```bash
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`.

**SuppOrth (optional):** homology reconstruction can also start from the consensus
orthologs of [SuppOrth](https://github.com/fma91/SuppOrth). `hits_from_supporth` reads an
existing `suppOrth.tsv` and needs nothing extra. `run_supporth` needs the `suppOrth`
command, which is not downloaded automatically and runs on Linux and macOS:
`pip install 'git+https://github.com/fma91/SuppOrth.git@stand_alone'`, then `suppOrth install`.

**Windows:** homology reconstruction and the KEGG species model work as-is. To
*build* the KEGG HMM libraries (needs MAFFT/CD-HIT), use **WSL2**.
model = read_yaml_model("yeast-GEM.yml") # a cobra.Model
model.objective = "r_2111" # growth
model.reactions.get_by_id("r_1714").lower_bound = -1.0 # glucose uptake
print(model.optimize().objective_value) # cobrapy
```

## Documentation
[Getting started](https://raven-docs.readthedocs.io/en/latest/guide/getting-started.html) continues from here.

[**raven-docs**](https://raven-docs.readthedocs.io/) is the documentation, and it
covers MATLAB RAVEN and raven-toolbox side by side: the same task shown in both
languages, with the examples run in both languages by the documentation's CI.
## Documentation and support

This repository carries no prose documentation of its own. The API reference on the
site is generated from the docstrings here at build time, so a docstring edit
reaches the site without anything being copied across. Contributor and maintainer
notes are in [CONTRIBUTING.md](CONTRIBUTING.md); the roadmap, the backlog and the
MATLAB parity record are in
[raven-gecko-parity](https://github.com/CompMetEng/raven-gecko-parity).
[raven-docs](https://raven-docs.readthedocs.io/en/latest/) documents MATLAB RAVEN and raven-toolbox side by side: each task is shown in both languages, and the CI of the documentation runs the examples in both. This repository has no prose documentation of its own. The API reference on the site is generated from the docstrings here when the site is built. [CONTRIBUTING.md](CONTRIBUTING.md) has the notes for contributors and maintainers, and [raven-gecko-parity](https://github.com/CompMetEng/raven-gecko-parity) holds the roadmap, the backlog and the MATLAB parity record. Use [GitHub Discussions](https://github.com/CompMetEng/raven-toolbox/discussions) for questions.

## Relationship to MATLAB RAVEN
## Cite us

`raven-toolbox` is an independent Python reimplementation of the
[RAVEN Toolbox](https://github.com/CompMetEng/RAVEN), released under the permissive
**MIT** license. If you use it in scientific work, please cite the RAVEN 2 paper:
raven-toolbox is an independent Python reimplementation of RAVEN. 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.
> 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. doi:[10.1371/journal.pcbi.1006541](https://doi.org/10.1371/journal.pcbi.1006541).

## License

Expand Down
Binary file added img/raven-logo-white.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file added img/raven-logo.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading