UniFrac is the de facto repository for high-performance phylogenetic diversity calculations. The methods in this repository are based on an implementation of the Strided State UniFrac algorithm which is faster, and uses less memory than Fast UniFrac. Strided State UniFrac supports Unweighted UniFrac, Weighted UniFrac, Generalized UniFrac, Variance Adjusted UniFrac and meta UniFrac, in both double and single precision (fp32). This repository also includes Stacked Faith (manuscript in preparation), a method for calculating Faith's PD that is faster and uses less memory than the Fast UniFrac-based reference implementation.
This repository produces standalone exutables and a C API exposed via a shared library which can be linked against by any programming language.
A detailed description of the Strided State UniFrac algorithm can be found in McDonald et al. 2018 Nature Methods. Please note that this package implements multiple UniFrac variants, which may have their own citation. Details can be found in the help output from the command line interface in the citations section, and is included immediately below:
ssu
For UniFrac, please see:
McDonald et al. Nature Methods 2018; DOI: 10.1038/s41592-018-0187-8
Lozupone and Knight Appl Environ Microbiol 2005; DOI: 10.1128/AEM.71.12.8228-8235.2005
Lozupone et al. Appl Environ Microbiol 2007; DOI: 10.1128/AEM.01996-06
Hamady et al. ISME 2010; DOI: 10.1038/ismej.2009.97
Lozupone et al. ISME 2011; DOI: 10.1038/ismej.2010.133
For Generalized UniFrac, please see:
Chen et al. Bioinformatics 2012; DOI: 10.1093/bioinformatics/bts342
For Variance Adjusted UniFrac, please see:
Chang et al. BMC Bioinformatics 2011; DOI: 10.1186/1471-2105-12-118
For GPU-accelerated UniFrac, please see:
Sfiligoi et al. PEARC'20; DOI: 10.1145/3311790.3399614
faithpd
For Faith's PD, please see:
Faith Biological Conservation 1992; DOI: 10.1016/0006-3207(92)91201-3
At this time, there is one primary way to install the library, through bioconda. It is also possible to clone the repository and install the C++ API with make.
Compilation has been performed on both LLVM 10.0.0 (OS X >= 10.12) or GCC 9 (Centos >= 7) and HDF5 >= 1.8.17.
Installation time should be a few minutes at most.
An example of installing UniFrac, and using it with CPUs as well as GPUs, can be be found on Google Colabs.
This binaries can be installed via a combination of conda-forge and bioconda:
conda create --name unifrac -c conda-forge -c bioconda unifrac-binaries
conda activate unifrac
To install, first the binary needs to be compiled. This assumes that the HDF5 toolchain and libraries are available. More information about how to setup the stack can be found here.
Assuming h5c++ is in your path, the following should work:
make api && make main
#optionally
make install
Note: if you are using conda we recommend installing HDF5 and relate compiler using the
conda-forge channel, for example:
conda create --name unifrac -c conda-forge gxx_linux-64 hdf5 mkl-include lz4 hdf5-static libcblas liblapacke make
conda activate unifrac
For GPU-enabled code, you will need the NVIDIA HPC SDK compiler. A helper script will download it, install it and setup the necessary environment:
scripts/install_hpc_sdk.sh
source setup_nv_h5.sh
Unifrac uses OpenMP to make use of multiple CPU cores. By default, Unifrac will use all the cores that are available on the system. To restrict the number of cores used, set:
export OMP_NUM_THREADS=nthreads
On Linux platforms, Unifrac will run on a GPU, if one is found. To disable GPU offload, and thus force CPU-only execution, one can set:
export UNIFRAC_USE_GPU=N
To check which code path is used (Unifrac will print it to standard output at runtime), set:
export UNIFRAC_GPU_INFO=Y
Finally, Unifrac will only use one GPU at a time. If more than one GPU is present, one can select the one to use by setting:
export ACC_DEVICE_NUM=gpunum
Note that there is no GPU support for MacOS.
Below are a few light examples of different ways to use this library.
The methods can be used directly through the command line after install:
$ which ssu
/Users/<username>/miniconda3/envs/unifrac/bin/ssu
$ ssu --help
usage: ssu -i <biom> -o <out.dm> -m [METHOD] -t <newick> [-n threads] [-a alpha] [-f] [--vaw]
[--mode [MODE]] [--start starting-stripe] [--stop stopping-stripe] [--partial-pattern <glob>]
[--n-partials number_of_partitions] [--report-bare] [--format|-r out-mode]
-i The input BIOM table.
-t The input phylogeny in newick.
-m The method, [unweighted | weighted_normalized | weighted_unnormalized | generalized |
unweighted_fp32 | weighted_normalized_fp32 | weighted_unnormalized_fp32 | generalized_fp32].
-o The output distance matrix.
-n [OPTIONAL] The number of threads, default is 1.
-a [OPTIONAL] Generalized UniFrac alpha, default is 1.
-f [OPTIONAL] Bypass tips, reduces compute by about 50%.
--vaw [OPTIONAL] Variance adjusted, default is to not adjust for variance.
--mode [OPTIONAL] Mode of operation:
one-off : [DEFAULT] compute UniFrac.
partial : Compute UniFrac over a subset of stripes.
partial-report : Start and stop suggestions for partial compute.
merge-partial : Merge partial UniFrac results.
--start [OPTIONAL] If mode==partial, the starting stripe.
--stop [OPTIONAL] If mode==partial, the stopping stripe.
--partial-pattern [OPTIONAL] If mode==merge-partial, a glob pattern for partial outputs to merge.
--n-partials [OPTIONAL] If mode==partial-report, the number of partitions to compute.
--report-bare [OPTIONAL] If mode==partial-report, produce barebones output.
--format|-r [OPTIONAL] Output format:
ascii : [DEFAULT] Original ASCII format.
hfd5 : HFD5 format. May be fp32 or fp64, depending on method.
hdf5_fp32 : HFD5 format, using fp32 precision.
hdf5_fp64 : HFD5 format, using fp64 precision.
--pcoa [OPTIONAL] Number of PCoA dimensions to compute (default: 10, do not compute if 0)
--diskbuf [OPTIONAL] Use a disk buffer to reduce memory footprint. Provide path to a fast partition (ideally NVMe).
Citations:
For UniFrac, please see:
McDonald et al. Nature Methods 2018; DOI: 10.1038/s41592-018-0187-8
Lozupone and Knight Appl Environ Microbiol 2005; DOI: 10.1128/AEM.71.12.8228-8235.2005
Lozupone et al. Appl Environ Microbiol 2007; DOI: 10.1128/AEM.01996-06
Hamady et al. ISME 2010; DOI: 10.1038/ismej.2009.97
Lozupone et al. ISME 2011; DOI: 10.1038/ismej.2010.133
For Generalized UniFrac, please see:
Chen et al. Bioinformatics 2012; DOI: 10.1093/bioinformatics/bts342
For Variance Adjusted UniFrac, please see:
Chang et al. BMC Bioinformatics 2011; DOI: 10.1186/1471-2105-12-118
$ which faithpd
/Users/<username>/miniconda3/envs/unifrac/bin/faithpd
$ faithpd --help
usage: faithpd -i <biom> -t <newick> -o <out.txt>
-i The input BIOM table.
-t The input phylogeny in newick.
-o The output series.
Citations:
For Faith's PD, please see:
Faith Biological Conservation 1992; DOI: 10.1016/0006-3207(92)91201-3
In addition to the above methods to access UniFrac, it is also possible to link against the shared library. The C API is described in src/api.hpp, and examples of linking against this API can be found in examples/.
A small test .biom and .tre can be found in src/. An example with expected output is below, and should execute in 10s of milliseconds:
$ ssu -i src/test.biom -t src/test.tre -m unweighted -o test.out
$ cat test.out
Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
Sample1 0 0.2 0.5714285714285714 0.6 0.5 0.2
Sample2 0.2 0 0.4285714285714285 0.6666666666666666 0.6 0.3333333333333333
Sample3 0.5714285714285714 0.4285714285714285 0 0.7142857142857143 0.8571428571428571 0.4285714285714285
Sample4 0.6 0.6666666666666666 0.7142857142857143 0 0.3333333333333333 0.4
Sample5 0.5 0.6 0.8571428571428571 0.3333333333333333 0 0.6
Sample6 0.2 0.3333333333333333 0.4285714285714285 0.4 0.6 0