/octopus

Bayesian haplotype-based mutation calling

Primary LanguageC++MIT LicenseMIT

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Octopus is a mapping-based variant caller that implements several calling models within a unified haplotype-aware framework. Octopus takes inspiration from particle filtering by constructing a tree of haplotypes and dynamically pruning and extending the tree based on haplotype posterior probabilities in a sequential manner. This allows octopus to implicitly consider all possible haplotypes at a given loci in reasonable time.

There are currently five calling models implemented:

  • individual: call germline variants in a single healthy individual.
  • population: jointly call germline variants in small cohorts.
  • cancer: call germline and somatic mutations tumour samples.
  • trio: call germline and de novo mutations in a parent-offspring trio.
  • polyclone: call variants in samples with an unknown mixture of haploid clones, such a bacteria or viral samples.
  • cell: call variants in a set of single cell samples from the same individual.

Octopus is currently able to call SNVs, small-medium sized indels, small complex rearrangements, and micro-inversions.

Quick start

Install Octopus (dependencies will be installed into octopus/build):

$ git clone -b develop https://github.com/luntergroup/octopus.git
$ octopus/scripts/install.py --install-dependencies --download-forests
$ echo 'export PATH='$(pwd)'/octopus/bin:$PATH' >> ~/.bash_profile
$ source ~/.bash_profile

Call some variants:

$ FOREST="$(pwd)/octopus/resources/forests/germline.v0.5.2-beta.forest"
$ octopus -R hs37d5.fa -I NA12878.bam -T 1 to MT -o NA12878.octopus.vcf.gz --forest $FOREST --threads 8

Consult the command line reference for descriptions of these options.

Requirements

  • A C++14 compiler with SSE2 support
  • A C++14 standard library implementation
  • Git 2.5 or greater
  • Boost 1.65 or greater
  • htslib 1.4 or greater
  • GMP 5.1.0 or greater
  • CMake 3.9 or greater
  • Optional:
    • Python3 or greater

Obtaining requirements on OS X

On OS X, Clang is recommended. All requirements can be installed using the package manager Homebrew:

$ brew update
$ brew install git
$ brew install --with-clang llvm
$ brew install boost
$ brew install gmp
$ brew install cmake
$ brew tap homebrew/science # required for htslib
$ brew install htslib
$ brew install python3

Note if you already have any of these packages installed via Homebrew on your system the command will fail, but you can update to the latest version using brew upgrade.

Obtaining requirements on Ubuntu

Depending on your Ubuntu distribution, some requirements can be installed with apt-get. It may be preferable to use GCC as this will simplify installing Boost:

$ sudo add-apt-repository ppa:ubuntu-toolchain-r/test
$ sudo apt-get update && sudo apt-get upgrade
$ sudo apt-get install gcc-7
$ sudo apt-get install libgmp3-dev
$ sudo apt-get install git-all
$ sudo apt-get install python3

The other packages will need to be installed manually:

  • CMake installation instructions are given here.
  • Htslib installation instructions are given here. Note you may need to install autoconf (sudo apt-get install autoconf).
  • Instructions on installing Boost are given here.

These instructions are replicated in the user documentation (Appendix).

Installation

Octopus can be built and installed on most Unix based systems (Linux, OS X). Windows has not been tested, but should be compatible.

Conda package

Octopus is available pre-built for Linux as part of Bioconda. To install in an isolated environment:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b -p venv
venv/bin/conda install -c conda-forge -c bioconda octopus
venv/bin/octopus -h

A package will also be available for OSX once conda-forge and bioconda move to newer versions of gcc and boost.

Quick installation with Python3

First clone the git repository in your preferred directory:

$ git clone -b master https://github.com/luntergroup/octopus.git && cd octopus

The easiest way to install octopus from source is with the Python3 install script. If your default compiler satisfies the minimum requirements just execute:

$ ./scripts/install.py

otherwise explicitly specify the compiler to use:

$ ./scripts/install.py --cxx_compiler /path/to/cpp/compiler # or just the compiler name if on your PATH

For example, if the requirement instructions above were used:

$ ./scripts/install.py --cxx_compiler clang++-4.0

On some systems, you may also need to specify a C compiler which is the same version as your C++ compiler, otherwise you'll get lots of link errors. This can be done with the --c_compiler option:

$ ./scripts/install.py -cxx g++-7 -c gcc-7

By default this installs to /bin relative to where you installed octopus. To install to a different location (e.g. /usr/local/bin) use:

$ ./scripts/install.py --prefix /user/local/bin

If anything goes wrong with the build process and you need to start again, be sure to add the command --clean.

Installing with CMake

If Python3 isn't available, the binaries can be installed manually with CMake:

$ git clone -b master https://github.com/luntergroup/octopus.git
$ cd octopus/build
$ cmake .. && make install

To install to different location (e.g. /usr/local/bin) use:

$ cmake -DCMAKE_INSTALL_PREFIX=/usr/local/bin ..

CMake will try to find a suitable compiler on your system, if you'd like you use a specific compiler use the -D option, for example:

$ cmake -D CMAKE_C_COMPILER=clang-4.0 -D CMAKE_CXX_COMPILER=clang++-4.0 ..

You can check installation was successful by executing the command:

$ octopus -h

Running Tests

Octopus currently has limited unit tests (more are planned!). To install and run them, use the Python3 install script in the test directory:

$ test/install.py

Examples

Here are some common use-cases to get started. These examples are by no means exhaustive, please consult the documentation for explanations of all options, algorithms, and further examples. For more in depth examples, refer to the case studies.

Note by default octopus will output all calls in VCF format to standard output, in order to write calls to a file (.vcf, .vcf.gz, and .bcf are supported), use the command line option --output (-o).

Calling germline variants in an individual

This is the simplest case, if the file NA12878.bam contains a single sample, octopus will default to its individual calling model:

$ octopus --reference hs37d5.fa --reads NA12878.bam

or less verbosely:

$ octopus -R hs37d5.fa -I NA12878.bam

By default, octopus automatically detects and calls all samples contained in the input read files. To call a subset of these samples, use the --samples (-S) option:

$ octopus -R hs37d5.fa -I multi-sample.bam -S NA12878

Targeted calling

By default, octopus will call all regions specified in the reference index. In order to restrict calling to a subset of regions, either provide a list of zero-indexed regions in the format chr:start-end (--regions; -T), or a file containing a list of regions in either standard format or BED format (--regions-file; -t):

$ octopus -R hs37d5.fa -I NA12878.bam -T 1 2:30,000,000- 3:10,000,000-20,000,000
$ octopus -R hs37d5.fa -I NA12878.bam -t regions.bed

Conversely a set of regions to exclude can be given explictely (--skip-regions;-K), or with a file (--skip-regions-file; -k):

$ octopus -R hs37d5.fa -I NA12878.bam -K 1 2:30,000,000- 3:10,000,000-20,000,000
$ octopus -R hs37d5.fa -I NA12878.bam -k skip-regions.bed

Calling de novo mutations in a trio

To call germline and de novo mutations in a trio, either specify both maternal (--maternal-sample; -M) and paternal (--paternal-sample; -F) samples:

$ octopus -R hs37d5.fa -I NA12878.bam NA12891.bam NA12892.bam -M NA12892 -F NA12891

or provide a PED file which defines the trio:

$ octopus -R hs37d5.fa -I NA12878.bam NA12891.bam NA12892.bam --pedigree ceu_trio.ped

Calling somatic mutations in tumours

To call germline and somatic mutations in a paired tumour-normal sample, just specify which sample is the normal (--normal-sample; -N):

$ octopus -R hs37d5.fa -I normal.bam tumour.bam --normal-sample NORMAL

It is also possible to genotype multiple tumours from the same individual jointly:

$ octopus -R hs37d5.fa -I normal.bam tumourA.bam tumourB.bam --normal-sample NORMAL

If a normal sample is not present the cancer calling model must be invoked explicitly:

$ octopus -R hs37d5.fa -I tumour1.bam tumour2.bam -C cancer

Be aware that without a normal sample, somatic mutation classification power is significantly reduced.

Joint variant calling (experimental)

Multiple samples from the same population, without pedigree information, can be called jointly:

$ octopus -R hs37d5.fa -I NA12878.bam NA12891.bam NA12892.bam

Joint calling samples may increase calling power, especially for low coverage sequencing.

Calling variants in mixed haploid samples (experimental)

If your sample contains an unknown mix of haploid clones (e.g. some bacteria or viral samples), use the polyclone calling model:

$ octopus -R H37Rv.fa -I mycobacterium_tuberculosis.bam -C polyclone

This model will automatically detect the number of subclones in your sample (up to the maximum given by --max-clones).

Calling variants in single cell samples (experimental)

Single cell samples can be called with the cell calling model. Allelic dropout and cell phylogeny are considered by the model to improve variant calls.

$ octopus -R H37Rv.fa -I cellA.bam cellB.bam cellC.bam -C cell

HLA genotyping

To call phased HLA genotypes, increase the default lagging level:

$ octopus -R hs37d5.fa -I NA12878.bam -t hla-regions.bed --lagging-level AGGRESSIVE

Multithreaded calling

Octopus has built in multithreading capabilities, just add the --threads command:

$ octopus -R hs37d5.fa -I NA12878.bam --threads

This will let octopus automatically decide how many threads to use, and is the recommended approach as octopus can dynamically juggle thread usage at an algorithm level. However, a strict upper limit on the number of threads can also be used:

$ octopus -R hs37d5.fa -I NA12878.bam --threads 4

Fast calling

By default, octopus is geared towards more accurate variant calling which requires the use of complex (slow) algorithms. However, to achieve faster runtimes (at the cost of decreased calling accuracy) many of these features can be disabled. There are two helper commands that setup octopus for faster variant calling, --fast and --very-fast, e.g.:

$ octopus -R hs37d5.fa -I NA12878.bam --fast

Note this does not turn on multithreading or increase buffer sizes.

Making evidence BAMs

Octopus can generate 'evidence' BAMs for single sample calling. To generate a single BAM file containing realigned reads supporting called variants use the --bamout option:

$ octopus -R hs37d5.fa -I NA12878.bam -o octopus.vcf --bamout octopus.bam

To generate split BAM files (one for each called haplotype) use the --bamout option, but specify only the file prefix:

$ octopus -R hs37d5.fa -I NA12878.bam -o octopus.vcf --bamout octopus

Octopus will generate BAM files (octopus1.bam, octopus2.bam, ...) for the number of haplotypes in the sample. Note that although each split BAM is haploid, the variants in each are only phased according to the phase sets called in the output VCF.

Output format

Octopus outputs variants using a simple but rich VCF format (see user documentation for full details). For example, two overlapping deletions are represented like:

#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	NA12878
1	102738191	.	ATTATTTAT	A,*	.	.	.	GT	1|2
1	102738191	.	ATTATTTATTTAT	A	.	.	.	GT	.|1

in contrast to how such a site would usually be represented, either:

#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	NA12878
1	102738191	.	ATTATTTAT	A	.	.	.	GT	1/0
1	102738191	.	ATTATTTATTTAT	A	.	.	.	GT	1/0

which is inconsistent as the reference is deduced in each record, or:

#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	NA12878
1	102738191	.	ATTATTTATTTAT	ATTAT,A	.	.	.	GT	1/2

which is at least consistent, but rapidly becomes unmanageable as the length and number of overlapping variants increases.

Octopus's representation is both succinct and consistent. The * allele denotes an upstream deletion, while the . in the genotype of the second record indicates the allele is missing due to a previous event. As the records are phased, the called haplotypes can be unambiguously reconstructed when the VCF file is read sequentially.

However, some existing tools will not recognise this format. For example, RTG Tools does not fully support this representation. Therefore, octopus has an option to also produce calls using a more typical VCF format (like the first of the two examples). To request this, use the --legacy command line option. This option is only available when outputting calls to a file (i.e. not stdout).

Documentation

Complete user and developer documentation is available in the doc directory.

Support

Please report any bugs or feature requests to the octopus issue tracker.

Contributing

Contributions are very welcome, but please first review the contribution guidelines.

Authors

Daniel Cooke and Gerton Lunter

Citing

Please cite the preprint: A unified haplotype-based method for accurate and comprehensive variant calling.

License

Please refer to LICENSE.