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Installation
fastCDS ships as a single package containing both a C++ binary (the index and map commands) and a Python wrapper (the fetch / plot commands, the Mapper API, and the DataFrame helpers). pip and bioconda both give you the whole thing.
pip install fastCDSThe wheel bundles the compiled binary (as fastCDS/_bin/fastCDS-core), so all four commands work immediately - nothing else to install:
fastCDS --version
fastCDS fetch listReady-to-use wheels cover Linux and macOS (Intel + Apple Silicon), Python 3.9+. Windows is not supported directly - there's no Windows build, so run fastCDS inside WSL (Windows Subsystem for Linux), where it installs and behaves exactly like on Linux.
conda install -c bioconda -c conda-forge fastCDS
# or, faster:
mamba install -c bioconda -c conda-forge fastCDSconda compiles the binary as part of the recipe, so all four commands land on your PATH just like the pip install.
pixi resolves from the same conda channels:
pixi add -c bioconda -c conda-forge fastCDS # in a project
pixi global install -c bioconda -c conda-forge fastCDS # as a global toolRequirements:
- C++17 toolchain (g++ >= 9, clang >= 10, or MSVC >= 2019)
- CMake >= 3.16
- OpenMP (optional)
git clone https://github.com/SotoLF/fastCDS.git
cd fastCDS
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j$(nproc)
pip install -e .The repo's bin/fastCDS wrapper finds build/fastCDS automatically, so you can run the four commands straight from the checkout.
A Dockerfile at the repo root builds an image with the binary and wrapper ready to go:
docker build -t fastCDS .
docker run --rm -v "$(pwd):/work" fastCDS \
map --index /work/human.idx --bed /work/queries.bed --out-dir /work/out --output all1 - How to install
2 - Building an index
(fastCDS index, fastCDS fetch)
3 - Mapping
(fastCDS map)
4 - Plotting
(fastCDS plot)
6 - Performance and benchmarking
7 - Reference