Clara Genomics Analysis

Overview

Clara Genomics Analysis is a GPU-accelerated library for biological sequence analysis. This section provides a brief overview of the different components of ClaraGenomicsAnalysis. For more detailed API documentation please refer to the documentation.

cudapoa

The cudapoa package provides a GPU-accelerated implementation of the Partial Order Alignment algorithm. It is heavily influenced by SPOA and in many cases can be considered a GPU-accelerated replacement. Features include:

1. Generation of consensus sequences
2. Generation of multi-sequence alignments (MSAs)

cudaaligner

The cudaaligner package provides GPU-accelerated global alignment.

cudamapper

Note cudamapper is still in pre-alpha stage and should be considered experimental.

The cudamapper package provides minimizer-based GPU-accelerated approximate mapping. cudamapper outputs mappings in the PAF format and is currently optimised for all-vs-all long read (ONT, Pacific Biosciences) sequences.

To run all-vs all overlaps use the following command:

cudamapper in.fasta in.fasta

A query fasta can be mapped to a reference as follows:

cudamapper query.fasta target.fasta

cudamapper usage information

To access more information about running cudamapper, run cudamapper --help.

Clone Clara Genomics Analysis

Latest released version

This will clone the repo to the master branch, which contains code for latest released version and hot-fixes.

git clone --recursive -b master git@github.com:clara-genomics/ClaraGenomicsAnalysis.git

Latest development version

This will clone the repo to the default branch, which is set to be the latest development branch. This branch is subject to change frequently as features and bug fixes are pushed.

git clone --recursive git@github.com:clara-genomics/ClaraGenomicsAnalysis.git

System Requirements

Minimum requirements -

1. Ubuntu 16.04 or Ubuntu 18.04
2. CUDA 9.0+ (official instructions for installing CUDA are available here)
3. gcc/g++ 5.4.0+
4. Python 3.6.7+
5. htslib 1.9+

Clara Genomics Analysis Setup

Build

To build Clara Genomics Analysis -

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install
make -j install

Install

To install the SDK -

make install

Package generation

Package generation puts the libraries, headers and binaries built by the make command above into a .deb/.rpm for portability and easy installation. The package generation itself doesn't guarantee any cross-platform compatibility.

It is recommended that a separate build and packaging be performed for each distribution and CUDA version that needs to be supported.

The type of package (deb vs rpm) is determined automatically based on the platform the code is being run on. To generate a package for the SDK -

make package

Enable Unit Tests

To enable unit tests, add -Dcga_enable_tests=ON to the cmake command in the build step.

This builds GTest based unit tests for all applicable modules, and installs them under ${CMAKE_INSTALL_PREFIX}/tests. These tests are standalone binaries and can be executed directly. e.g.

cd $INSTALL_DIR
./tests/cudapoatests

Enable Benchmarks

To enable benchmarks, add -Dcga_enable_benchmarks=ON to the cmake command in the build step.

This builds Google Benchmark based microbenchmarks for applicable modules. The built benchmarks are installed under ${CMAKE_INSTALL_PREFIX}/benchmarks/<module> and can be run directly.

e.g.

#INSTALL_DIR/benchmarks/cudapoa/multibatch

A description of each of the benchmarks is present in a README under the module's benchmark folder.

Enable Doc Generation

To enable document generation for Clara Genomics Analysis, please install Doxygen on your system. OnceDoxygen has been installed, run the following to build documents.

make docs

Docs are also generated as part of the default all target when Doxygen is available on the system.

To disable documentation generation add -Dcga_generate_docs=OFF to the cmake command in the build step.

Code Formatting

C++ / CUDA

Clara Genomics Analysis makes use of clang-format to format it's source and header files. To make use of auto-formatting, clang-format would have to be installed from the LLVM package (for latest builds, best to refer to http://releases.llvm.org/download.html).

Once clang-format has been installed, make sure the binary is in your path.

To add a folder to the auto-formatting list, use the macro cga_enable_auto_formatting(FOLDER). This will add all cpp source/header files to the formatting list.

To auto-format, run the following in your build directory.

make format

To check if files are correct formatted, run the following in your build directory.

make check-format

Python

Clara Genomics Analysis follows the PEP-8 style guidelines for all its Python code. The automated CI system for Clara Genomics Analysis run flake8 to check the style.

To run style check manually, simply run the following from the top level folder.

pip install -r ci/checks/python-style-requirements.txt
./pyclaragenomics/style_check

Running CI Tests Locally

Please note, your git repository will be mounted to the container, any untracked files will be removed from it. Before executing the CI locally, stash or add them to the index.

Requirements:

1. docker (https://docs.docker.com/install/linux/docker-ce/ubuntu/)
2. nvidia-docker (https://github.com/NVIDIA/nvidia-docker)
3. nvidia-container-runtime (https://github.com/NVIDIA/nvidia-container-runtime)

Run the following command to execute the CI build steps inside a container locally:

bash ci/local/build.sh -r <ClaraGenomicsAnalysis repo path>

ci/local/build.sh script was adapted from rapidsai/cudf

The default docker image is clara-genomics-base:cuda10.0-ubuntu16.04-gcc5-py3.7. Other images from gpuci/clara-genomics-base repository can be used instead, by using -i argument

bash ci/local/build.sh -r <ClaraGenomicsAnalysis repo path> -i gpuci/clara-genomics-base:cuda10.0-ubuntu18.04-gcc7-py3.6