Raven

Raven is a de novo genome assembler for long uncorrected reads.

Usage

To build raven executable run the following commands:

git clone https://github.com/lbcb-sci/raven && cd raven
cmake -S ./ -B./build -DRAVEN_BUILD_EXE=1 -DCMAKE_BUILD_TYPE=Release
cmake --build build

For faster build times optionally use ninja and enable threading in cmake. Eg.

git clone https://github.com/lbcb-sci/raven && cd raven
cmake -S ./ -B ./build -DRAVEN_BUILD_EXE=1 -DCMAKE_BUILD_TYPE=Release -G Ninja
cmake --build build -j 4

To install the raven executable after build run:

cmake --install ./build

To install python bindings run the following:

pip install git+git://github.com/lbcb-sci/raven.git@master

Python example can be found at PythonLib/example.py

usage: raven [options ...] <sequences> [<sequences> ...]

  # default output is to stdout in FASTA format
  <sequences>
    input file in FASTA/FASTQ format (can be compressed with gzip)

  options:
    -k, --kmer-len <int>
      default: 15
      length of minimizers used to find overlaps
    -w, --window-len <int>
      default: 5
      length of sliding window from which minimizers are sampled
    -f, --frequency <double>
      default: 0.001
      threshold for ignoring most frequent minimizers
    -i, --identity <double>
      default: 0
      threshold for overlap between two reads in order to construct an edge between them
      if set to zero, this functionality is disabled
    -o, --kMaxNumOverlaps <long unsigned int>
      default: 32
      maximum number of overlaps that will be taken during FindOverlapsAndCreatePiles stage
    -p, --polishing-rounds <int>
      default: 2
      number of times racon is invoked
    -m, --match <int>
      default: 3
      score for matching bases
    -n, --mismatch <int>
      default: -5
      score for mismatching bases
    -g, --gap <int>
      default: -4
      gap penalty (must be negative)
    -u, --min-unitig-size <int>
      default: 9999
      minimal unitig size
    --graphical-fragment-assembly <string>
      prints the assembly graph in GFA format
    --resume
      resume previous run from last checkpoint
    --disable-checkpoints
      disable checkpoint file creation
    -t, --threads <int>
      default: 1
      number of threads
    --version
      prints the version number
    -h, --help
      prints the usage

  only available when built with CUDA:
    -c, --cuda-poa-batches <int>
      default: 0
      number of batches for CUDA accelerated polishing
    -b, --cuda-banded-alignment
      use banding approximation for polishing on GPU
      (only applicable when -c is used)
    -a, --cuda-alignment-batches <int>
      default: 0
      number of batches for CUDA accelerated alignment

To use raven library component in your project, add the following to your cmake file:

include(FetchContent)

FetchContent_Declare(
        raven
        GIT_REPOSITORY https://github.com/lbcb-sci/raven
        GIT_TAG v1.8.1)

FetchContent_GetProperties(raven)
if (NOT raven_POPULATED)
    FetchContent_Populate(raven)
    add_subdirectory(
            ${raven_SOURCE_DIR}
            ${raven_BINARY_DIR}
            EXCLUDE_FROM_ALL)
endif ()

target_link_libraries(<YourTarget> <PRIVATE|PUBLIC|INTERFACE> raven)

Build options

• RAVEN_BUILD_TESTS: build unit tests
• RAVEN_BUILD_PYTHON: builds python module
• RAVEN_BUILD_SHARED_LIBS: build raven lib and it's dependencies as shared libraries
• RAVEN_BUILD_EXE: build raven executable
• racon_enable_cuda: build with NVIDIA CUDA support

Dependencies

• gcc 7.5+ | clang 8.0+
• cmake 3.11+
• zlib 1.2.8+

Hidden

• pybind11
• lbcb-sci/racon/tree/library 3.0.2
• rvaser/bioparser 3.0.13
• (raven_test) google/googletest 1.10.0

Other options

NOTE: not updated for 1.8 release

Brew

Install Linuxbrew and run the following command:

brew install brewsci/bio/raven-assembler

Conda

Install conda and run the following command:

conda install -c bioconda raven-assembler

Acknowledgment

This work has been supported in part by the Genome Institute of Singapore (A*STAR), by the Croatian Science Foundation under projects Algorithms for genome sequence analysis (UIP-11-2013-7353) and Single genome and metagenome assembly (IP-2018-01-5886), and in part by the European Regional Development Fund under grant KK.01.1.1.01.0009 (DATACROSS).