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 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.
odgi requires a C++ version of 9.3 or higher. You can check your version via:
gcc --version
g++ --versionodgi 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 and CMakeLists.txt.
If you need to avoid machine-specific optimizations, use the CMAKE_BUILD_TYPE=Generic build type:
cmake -H. -Bbuild -DCMAKE_BUILD_TYPE=Generic && cmake --build build -- -j 3On Arch Linux, the jemalloc dependency can be installed with:
sudo pacman -S jemalloc # arch linux
odgi recipes for Bioconda are available at https://bioconda.github.io/recipes/odgi/README.html. To install the latest version using Conda please execute:
conda install -c bioconda odgiTo simplify installation and versioning, we have an automated GitHub action that pushes the current docker build to dockerhub. To use it, pull the docker image:
docker pull pangenome/odgiThen, you can run odgi with:
docker run odgiAn 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.
odgi includes a variety of tools for analyzing and manipulating large pangenome graphs.
Read the full documentation at https://odgi.readthedocs.io/.
Since v1.11 MultiQC has an ODGI module. This module can only
work with output from odgi stats! For more details take a look at the documentation at odgi.readthedocs.io/multiqc.
Andrea Guarracino*, Simon Heumos*, Sven Nahnsen, Pjotr Prins, Erik Garrison. ODGI: understanding pangenome graphs, Bioinformatics, 2022
*Shared first authorship
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, "privvg", which supported significant maintenance and development effort in the odgi toolkit.
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 .
odgi::graph_t is a MutablePathDeletableHandleGraph in the generic variation graph handle graph 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.
Each time odgi is build, the current version is inferred via git describe --always --tags. Assuming, version.cpp
is up to date, odgi version will not only print out the current tagged version, but its release codename, too.
- Create a new release on GitHub
Choose a tag: v0.X.Y- Fill the
Release title: ODGI v0.X.Y - Miao - Fill the
Describe this releasesection - Tick
This is a pre-release - Click
Publish release
- Produce a buildable source tarball, containing code for
odgiand all submodules, and upload it to the release.- Execute the following instructions:
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.gzfile - Remove the tick on
This is a pre-release - Click
Publish release(this will trigger the update on bioconda)
@AndreaGuarracino and @subwaystation presented odgi at the German Bioinformatics Conference 2021: ODGI - scalable tools for pangenome graphs.
odgi is a play on the Italian word "oggi" (/ˈɔd.dʒi/), 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.