medaka 0.8.1

Neural network sequence error correction.

Medaka

medaka is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. It outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods, whilst being much faster.

© 2018 Oxford Nanopore Technologies Ltd.

Features

• Requires only basecalled data. (.fasta or .fastq)
• Improved accurary over graph-based methods (e.g. Racon).
• 50X faster than Nanopolish (and can run on GPUs).
• Benchmarks are provided here.
• Includes extras for implementing and training bespoke correction networks.
• Works on Linux and MacOS.
• Open source (Mozilla Public License 2.0).

Tools to enable the creation of draft assemblies can be found in a sister project pomoxis.

Documentation can be found at https://nanoporetech.github.io/medaka/.

Installation

Medaka can be installed in one of several ways.

Installation with conda

Perhaps the simplest way to start using medaka on both Linux and MacOS is through conda; medaka is available via the bioconda channel:

conda install -c bioconda medaka

Installation with pip

For those who prefer python's native pacakage manager, medaka is also available on pypi and can be installed using pip:

pip install medaka

On Linux platforms this will install a precompiled binary, on MacOS (and other) platforms this will fetch and compile a source distribution.

We recommend using medaka within a virtual environment, viz.:

virtualenv medaka --python=python3 --prompt "(medaka) "
. medaka/bin/activate
pip install medaka

Using this method requires the user to provide several binaries:

• samtools,
• minimap2,
• tabix, and
• bgzip

and place these within the PATH. samtools/bgzip/tabix version 1.9 and minimap2 version 2.11 are recommended as these are those used in development of medaka.

Installation from source

Medaka can be installed from its source quite easily on most systems.

Before installing medaka it may be required to install some prerequisite libraries, best installed by a package manager. On Ubuntu theses are:

• gcc
• zlib1g-dev
• libbz2-dev
• liblzma-dev
• libffi-dev
• libncurses5-dev
• libcurl4-gnutls-dev
• libssl-dev
• curl
• make
• wget
• python3-all-dev
• python-virtualenv

A Makefile is provided to fetch, compile and install all direct dependencies into a python virtual environment. To set-up the environment run:

git clone https://github.com/nanoporetech/medaka.git
cd medaka
make install
. ./venv/bin/activate

Using this method both samtools and minimap2 are built from source and need not be provided by the user.

Using a GPU

All installation methods will allow medaka to be used with CPU resource only. To enable the use of GPU resource it is necessary to install the tensorflow-gpu package. Unfortunately depending on your python version it may be necessary to modify the requirements of the medaka package for it to run without complaining. Using the source code from github a working GPU-powered medaka can be configured with:

git clone https://github.com/nanoporetech/medaka.git
cd medaka
sed -i 's/tensorflow/tensorflow-gpu/' requirements.txt
make install

However, note that The tensorflow-gpu GPU package is compiled against a specific version of the NVIDIA CUDA library; users are directed to the tensorflow installation pages for further information.

Usage

medaka can be run using its default settings through the medaka_consensus program. An assembly in .fasta format and basecalls in .fasta or .fastq formats are required. The program uses both samtools and minimap2. If medaka has been installed using the from-source method these will be present within the medaka environment, otherwise they will need to be provided by the user.

source ${MEDAKA}  # i.e. medaka/venv/bin/activate
NPROC=$(nproc)
BASECALLS=basecalls.fa
DRAFT=draft_assm/assm_final.fa
OUTDIR=medaka_consensus
medaka_consensus -i ${BASECALLS} -d ${DRAFT} -o ${OUTDIR} -t ${NPROC} -m r941_min_high

The variables BASECALLS, DRAFT, and OUTDIR in the above should be set appropriately. When medaka_consensus has finished running, the consensus will be saved to ${OUTDIR}/consensus.fasta.

Models

It is crucially important to specify the correct model, -m in the above, according to the basecaller used. Allowed values can be found by running medaka tools list\_models.

For guppy v3.0.3 models are named similarly to their basecalling counterparts with a "fast" and "high accuracy" model, for example r941_min_fast and r941_min_high. The medaka models are equal in speed regardless of basecaller speed/accuracy.

For guppy versions >=2.1.3 where the flip-flop algorithm has been used, users should select the highest numbered model equal to or less than the guppy version used for basecalling. There are two models here: r941_flip213 and r941_flip235

A final model r941_trans is available where a basecaller with the transducer algorithm has been used (Albacore or Guppy<2.1.3).

Origin of the draft sequence

Medaka has been trained to correct draft sequences processed through racon, specifically racon run four times iteratively with:

racon -m 8 -x -6 -g -8 -w 500 ...

Processing a draft sequence from alternative sources (e.g. the output of canu or wtdbg2) may lead to poorer results even when the draft is of a superior quality than that obtained from racon.

The walkthrough outlines one recommended workflow rapid construction of a draft for input into medaka. A second approach would be to run canu followed by racon applied twice iteratively before entry into medaka.

Acknowledgements

We thank Joanna Pineda and Jared Simpson for providing htslib code samples which aided greatly development of the optimised feature generation code, and for testing the version 0.4 release candidates.

Help

Licence and Copyright

© 2018 Oxford Nanopore Technologies Ltd.

medaka is distributed under the terms of the Mozilla Public License 2.0.

Research Release

Research releases are provided as technology demonstrators to provide early access to features or stimulate Community development of tools. Support for this software will be minimal and is only provided directly by the developers. Feature requests, improvements, and discussions are welcome and can be implemented by forking and pull requests. However much as we would like to rectify every issue and piece of feedback users may have, the developers may have limited resource for support of this software. Research releases may be unstable and subject to rapid iteration by Oxford Nanopore Technologies.