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https://github.com/pangenome/odgi

Optimized Dynamic Genome/Graph Implementation: understanding pangenome graphs
https://github.com/pangenome/odgi

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Optimized Dynamic Genome/Graph Implementation: understanding pangenome graphs

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README 

        
# odgi



[![build and test](https://github.com/pangenome/odgi/actions/workflows/build_and_test_on_push.yml/badge.svg)](https://github.com/pangenome/odgi/actions/workflows/build_and_test_on_push.yml)

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/odgi/README.html)



## optimized dynamic genome/graph implementation (odgi)



`odgi` provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.



Careful encoding of graph entities allows `odgi` to efficiently compute and transform [pangenomes](https://pangenome.github.io/) with minimal overheads.  `odgi` implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.



The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes.

Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small.

This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.



The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.



## installation



### building from source



`odgi` requires a C++ version of 9.3 or higher. You can check your version via:



``` bash

gcc --version

g++ --version

```



`odgi` pulls in a host of source repositories as dependencies. It may be necessary to install several system-level libraries to build `odgi`. On `Ubuntu 20.04`, these can be installed using `apt`:

```

sudo apt install build-essential cmake python3-distutils python3-dev libjemalloc-dev

```



After installing the required dependencies, clone the `odgi` git repository recursively because of the many submodules

and build with:



```

git clone --recursive https://github.com/pangenome/odgi.git

cd odgi

cmake -H. -Bbuild && cmake --build build -- -j 3

```



To build a static executable, use:



```

cmake -DBUILD_STATIC=1 -H. -Bbuild && cmake --build build -- -j 3

```



You'll need to set this flag to 0 or remove and rebuild your build directory if you want to unset this build behavior and get a dynamic binary again.

Static builds are unlikely to be supported on OSX, and require appropriate static libraries on linux.



For more information on optimisations, debugging and GNU Guix builds, see [INSTALL.md](./INSTALL.md) and [CMakeLists.txt](./CMakeLists.txt).



### Nix build



If you have `nix`, build and installation in your profile are as simple as:



```

nix-build && nix-env -i ./result

```



#### Notes for distribution



If you need to avoid machine-specific optimizations, use the `CMAKE_BUILD_TYPE=Generic` build type:



```shell

cmake -H. -Bbuild -DCMAKE_BUILD_TYPE=Generic && cmake --build build -- -j 3

```



#### Notes on dependencies



On `Arch Linux`, the `jemalloc` dependency can be installed with:



```

sudo pacman -S jemalloc     # arch linux

```



### Bioconda



`odgi` recipes for Bioconda are available at https://bioconda.github.io/recipes/odgi/README.html. To install the latest version using `Conda` please execute:



``` bash

conda install -c bioconda odgi

```



### Docker



To simplify installation and versioning, we have an automated GitHub action that pushes the current docker build to [dockerhub](https://hub.docker.com/r/pangenome/odgi).

To use it, pull the docker image:



```shell

docker pull pangenome/odgi

```



Then, you can run `odgi` with:



```shell

docker run odgi

```



### Guix



An alternative way to manage `odgi`'s dependencies is by using the `GNU GUIX` package manager. We use Guix to develop, test and deploy odgi on our systems.

For more information see [INSTALL](./INSTALL.md).



## documentation



`odgi` includes a variety of tools for analyzing and manipulating large pangenome graphs.

Read the full documentation at [https://odgi.readthedocs.io/](https://odgi.readthedocs.io/).



## multiqc



Since v1.11 [MultiQC](https://multiqc.info/) has an [ODGI module](https://multiqc.info/docs/#odgi). This module can only

work with output from `odgi stats`! For more details take a look at the documentation at [odgi.readthedocs.io/multiqc](https://odgi.readthedocs.io/en/latest/rst/multiqc.html).



## Citation

**Andrea Guarracino\*, Simon Heumos\*, Sven Nahnsen, Pjotr Prins, Erik Garrison**. [ODGI: understanding pangenome graphs](https://doi.org/10.1093/bioinformatics/btac308), Bioinformatics, 2022\

**\*Shared first authorship**



## funding sources



`odgi` has been funded through a variety of mechanisms, including a Wellcome Sanger PhD fellowship and diverse NIH and NSF grants (listed in our paper), as well as funding from the State of Tennessee.

Of particular note is the [contribution of NLnet to the development of a differential privacy model](https://nlnet.nl/project/VariationGraph/), ["privvg"](https://privvg.github.io/), which supported significant maintenance and development effort in the `odgi` toolkit.



## tests



Unittests from `vg` have been ported here and are used to validate the behavior of the algorithm.

They can be run via `odgi test` which is invoked by



```

ctest .

```



## API



`odgi::graph_t` is a `MutablePathDeletableHandleGraph` in the generic variation graph [handle graph](https://github.com/vgteam/libhandlegraph) hierarchical API model.

As such, it is possible to add, delete, and modify nodes, edges, and paths through the graph.

Wherever possible, destructive operations on the graph maintain path validity.



## versioning

Each time `odgi` is build, the current version is inferred via `git describe --always --tags`. Assuming, [version.cpp](./src/version.cpp)

is up to date, `odgi version` will not only print out the current tagged version, but its release codename, too.



## new release (developers only)



- Create a new release [on GitHub](https://github.com/pangenome/odgi/releases/new)

  - `Choose a tag`: v0.X.Y

  - Fill the `Release title`: ODGI v0.X.Y - Miao

  - Fill the `Describe this release` section

  - Tick `This is a pre-release`

  - Click `Publish release`

- Produce a buildable source tarball, containing code for `odgi` and all submodules, and upload it to the release.

    - Execute the following instructions:

    ```shell

    mkdir source-tarball

    cd source-tarball

    git clone --recursive https://github.com/pangenome/odgi

    cd odgi

    git fetch --tags origin

    LATEST_TAG="$(git describe --tags `git rev-list --tags --max-count=1`)"

    git checkout "${LATEST_TAG}"

    git submodule update --init --recursive

    mkdir include

    bash scripts/generate_git_version.sh include

    sed 's/execute_process(COMMAND bash/#execute_process(COMMAND bash/g' CMakeLists.txt -i

    rm -Rf .git

    find deps -name ".git" -exec rm -Rf "{}" \;

    cd ..

    mv odgi "odgi-${LATEST_TAG}"

    tar -czf "odgi-${LATEST_TAG}.tar.gz" "odgi-${LATEST_TAG}"

    rm -Rf "odgi-${LATEST_TAG}"

    ```

  - Open the (pre-)release created earlier

  - Upload the `odgi-v0.X.Y.tar.gz` file

  - Remove the tick on `This is a pre-release`

  - Click `Publish release` (this will trigger the update [on bioconda](http://bioconda.github.io/recipes/odgi/README.html))



[//]: # (This section is important for developers only. Each time we make a new release, we invoke [prepare_release.sh](./scripts/prepare_release.sh) (`cd` into folder [scripts](./scripts) first!) with a new release version and codename. [version.cpp](./src/version.cpp) is updated and the documentation version is bumped up.)



## presentations



[@AndreaGuarracino](https://github.com/andreaguarracino) and [@subwaystation](https://github.com/subwaystation) presented `odgi` at the German Bioinformatics Conference 2021: [ODGI - scalable tools for pangenome graphs](https://docs.google.com/presentation/d/1d52kaiOqeH4db4LyMHn7YNjv-mBKvhY2t2zQMNvzgno/edit#slide=id.p).



## name



`odgi` is a play on the [Italian word "oggi" (/ˈɔd.dʒi/)](https://en.wiktionary.org/wiki/oggi), which means "today".

As of 2019, a standard refrain in genomics is that genome graphs will be useful in _x_ years.

But, if we make them efficient and scalable, they will be useful today.