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Optimized sequence graph implementations for graph genomics

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libbdsg

Optimized bidirected sequence graph implementations for graph genomics

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About

The main purpose of libbdsg is to provide high performance implementations of sequence graphs for graph-based pangenomics applications. The repository contains two graph implementations with different performance tradeoffs:

  • HashGraph: prioritizes speed
  • PackedGraph: prioritizes low memory usage

Previously, a third implementation, ODGI, was provided, but that implementation is now part of its own odgi project.

All of these graph objects implement a common interface defined by libhandlegraph, so they can be used interchangeably and swapped easily.

Additionally, libbdsg provides a few "overlays", which are applied to the graph implementations in order to expand their functionality. The expanded functionality is also described generically using libhandlegraph interfaces.

Programming languages

libbdsg is written in C++. Using the instructions below, it is also possible to generate Python bindings to the underlying C++ library. The Python API is documented here. The documentation also includes a tutorial that serves as a useful introduction to libhandlegraph and libbdsg concepts.

Citation

A journal article that discusses the implementation and functionality of libbdsg is available under the following citation:

Eizenga, JM, Novak, AM, Kobayashi, E, Villani, F, Cisar, C, Heumos, S, Hickey, G, Colonna, V, Paten, B, Garrison, E. (2020) Efficient dynamic variation graphs. Bioinformatics. doi:10.1093/bioinformatics/btaa640.

The peer-reviewed article was drafted in this GitHub respository, and a preprint is avilable here.

Installation

There are several ways to install libbdsg.

From pip (Python bindings only)

If you only want the Python bindings (bdsg module), you can install via pip:

pip install bdsg

With cmake (C++ library and Python bindings)

Full CMake-based installation instructions, including tips on dependency installation, are available in the documentation. A basic guide is provided here.

When obtaining the source repo, make sure to clone with --recursive to get all the submodules:

git clone --recursive https://github.com/vgteam/libbdsg.git
cd libbdsg

With CMake, we are able to build Python bindings that use pybind11. However, we only support out-of-source builds from a directory named build, and we still put the built artifacts in lib in the main project directory.

To run a CMake-based build:

mkdir build
cd build
cmake ..
make -j 8

If the build fails, the Python bindings may be out of date with respect to the source files. See PYBIND_README.md for instructions on updating them. You may also need to install Doxygen. If you cannot install Doxygen, you can bypass the Doxygen portion of the build with cmake .. -DRUN_DOXYGEN=OFF.

Building Documentation

The documentation for libbdsg is built using Sphinx, and will invoke the CMake-based build process if not already run. To build it, from the main project directory:

# Install Sphinx
virtualenv --python python3 venv
. venv/bin/activate
pip3 install -r bdsg/docs/requirements.txt

# Build the documentation
make docs

The documentation can then be found at docs/_build/html/index.html.

With make (library only)

Dependencies

libbdsg has a few external dependencies:

The build process with make assumes that these libraries and their headers have been installed in a place on the system where the compiler can find them (e.g. in CPLUS_INCLUDE_PATH).

Easy make installation

The libbdsg-easy repository provides a simple method to coordinate these dependencies for a make build using git submodules.

Building

The following commands will create the libbdsg.a library in the lib directory.

git clone https://github.com/vgteam/libbdsg.git
cd libbdsg
make -j 8

To install system-wide (in /usr/local/):

make install

Or to install in an alternate location:

INSTALL_PREFIX=/other/path/ make install

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Optimized sequence graph implementations for graph genomics

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