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Neural network sequence error correction.

Project description

Oxford Nanopore Technologies logo


install with bioconda

medaka is a tool to create consensus sequences and variant calls from nanopore sequencing data. This task is performed using neural networks applied a pileup of individual sequencing reads against a draft assembly. It provides state-of-the-art results outperforming sequence-graph based methods and signal-based methods, whilst also being faster.

© 2018- Oxford Nanopore Technologies Ltd.


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

For creating draft assemblies we recommend Flye.


Medaka can be installed in one of several ways.

Installation with pip

Official binary releases of medaka are 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 --upgrade pip
pip install medaka

Using this method requires the user to provide several binaries:

and place these within the PATH. samtools/bgzip/tabix version 1.14 and minimap2 version 2.17 are recommended as these are those used in development of medaka. (Newer versions are almost certainly fine).

Installation with conda

The bioconda medaka packages are no longer supported by Oxford Nanopore Technologies.

For those who prefer the conda package manager, medaka is available via the bioconda channel:

conda create -n medaka -c conda-forge -c bioconda medaka

The bioconda releases lag behind the source code and PyPI releases.

Installation from source

This method is useful for macOS M1 devices as it will assist in building dependencies which will fail with the other methods above.

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:

bzip2 g++ zlib1g-dev libbz2-dev liblzma-dev libffi-dev libncurses5-dev
libcurl4-gnutls-dev libssl-dev curl make cmake wget python3-all-dev

In addition it is required to install and set up git LFS before cloning the repository.

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

# Note: certain files are stored in git-lfs,,
#       which must therefore be installed first.
git clone
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

Since version 1.1.0 medaka uses Tensorflow 2, prior versions used Tensorflow 1. For medaka 1.1.0 and higher installation from source or using pip can make immediate use of GPUs. However, note that the tensorflow package is compiled against specific versions of the NVIDIA CUDA and cuDNN libraries; users are directed to the tensorflow installation pages for further information. cuDNN can be obtained from the cuDNN Archive, whilst CUDA from the CUDA Toolkit Archive.

For medaka prior to version 1.1.0, to enable the use of GPU resource it is necessary to install the tensorflow-gpu package. Using the source code from github a working GPU-powered medaka can be configured with:

# Note: certain files are stored in git-lfs,,
#       which must therefore be installed first.
git clone
cd medaka
sed -i 's/tensorflow/tensorflow-gpu/' requirements.txt
make install

GPU Usage notes

Depending on your GPU, medaka may show out of memory errors when running. To avoid these the inference batch size can be reduced from the default value by setting the -b option when running medaka_consensus. A value -b 100 is suitable for 11Gb GPUs.

For users with RTX series GPUs it may be required to additionally set an environment variable to have medaka run without failure:


In this situation a further reduction in batch size may be required.

Using Docker

The source code repository contains a Dockerfile which can be used to create a GPU compatible Docker container image with the appropriate CUDA and cuDNN library versions for running medaka. The image is built on top of images provided by NVIDIA designed to run with the NVIDIA Container Toolkit. With the toolkit setup on your host computer the following command can be used to run the latest version of medaka:

docker run --rm --gpus 0 ontresearch/medaka:latest medaka --help

(The --gpus option can be amended as appropriate for your environment). Versioned tags are also available.


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
medaka_consensus -i ${BASECALLS} -d ${DRAFT} -o ${OUTDIR} -t ${NPROC} -m r941_min_high_g303

The variables BASECALLS, DRAFT, and OUTDIR in the above should be set appropriately. For the selection of the model (-m r941_min_high_g303 in the example above) see the Model section following.

When medaka_consensus has finished running, the consensus will be saved to ${OUTDIR}/consensus.fasta.

Bacterial (ploidy-1) variant calling

Variant calling for monoploid samples is enabled through the medaka_haploid_variant workflow:

medaka_haploid_variant -i <reads.fastq> -r <ref.fasta>

which requires the reads as a .fasta or .fastq and a reference sequence as a .fasta file.

Diploid variant calling

The diploid variant calling workflow medaka_variant that was historically implemented within the medaka package has been surpassed in accuracy and compute performance by other methods, it has therefore been deprecated. Our current recommendation for performing this task is to use Clair3 either directly or through the Oxford Nanopore Technologies provided Nextflow implementation available through EPI2ME Labs.


For best results it is 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.

Recent basecallers

Recent basecaller versions annotate their output with their model version. In such cases medaka can inspect the files and attempt to select an appropriate model for itself. This typically works best in the case of BAM output from basecallers. It will work also for FASTQ input provided the FASTQ has been created from basecaller output using:

samtools fastq -T '*' dorado.bam | gzip -c > dorado.fastq.gz

The command medaka consensus will attempt to automatically determine a correct model by inspecting its BAM input file. The helper scripts medaka_consensus and medaka_haploid_variant will make similar attempts from their FASTQ input.

To inspect files for yourself, the command:

medaka tools resolve_model --auto_model <consensus/variant> <input.bam/input.fastq>

will print the model that automatic model selection will use.

For older basecallers and when automatic selection is unsuccessful

If the name of the basecaller model used is known, but has been lost from the input files, the basecaller model can been provided to medaka directly. It must however be appended with either :consensus or :variant according to whether the user wishing to use the consensus or variant calling medaka model. For example:

medaka consensus input.bam output.hdf \
    --model dna_r10.4.1_e8.2_400bps_hac@v4.1.0:variant

will use the medaka variant calling model appropriate for use with the basecaller model named dna_r10.4.1_e8.2_400bps_hac@v4.1.0.

Medaka models are named to indicate i) the pore type, ii) the sequencing device (MinION or PromethION), iii) the basecaller variant, and iv) the basecaller version, with the format:

{pore}_{device}_{caller variant}_{caller version}

For example the model named r941_min_fast_g303 should be used with data from MinION (or GridION) R9.4.1 flowcells using the fast Guppy basecaller version 3.0.3. By contrast the model r941_prom_hac_g303 should be used with PromethION data and the high accuracy basecaller (termed "hac" in Guppy configuration files). Where a version of Guppy has been used without an exactly corresponding medaka model, the medaka model with the highest version equal to or less than the guppy version should be selected.

Improving parallelism

The medaka_consensus program is good for simple datasets but perhaps not optimal for running large datasets at scale. A higher level of parallelism can be achieved by running independently the component steps of medaka_consensus. The program performs three tasks:

  1. alignment of reads to input assembly (via mini_align which is a thin veil over minimap2)
  2. running of consensus algorithm across assembly regions (medaka consensus, note no underscore!)
  3. aggregation of the results of 2. to create consensus sequences (medaka stitch)

The three steps are discrete, and can be split apart and run independently. In most cases, Step 2. is the bottleneck and can be trivially parallelized. The medaka consensus program can be supplied a --regions argument which will restrict its action to particular assembly sequences from the .bam file output in Step 1. Therefore individual jobs can be run for batches of assembly sequences simultaneously. In the final step, medaka stitch can take as input one or more of the .hdf files output by Step 2.

So in summary something like this is possible:

# align reads to assembly
mini_align -i basecalls.fasta -r assembly.fasta -P -m \
    -p calls_to_draft.bam -t <threads>
# run lots of jobs like this, change model as appropriate
mkdir results
medaka consensus calls_to_draft.bam results/contigs1-4.hdf \
    --model r941_min_fast_g303 --batch 200 --threads 8 \
    --region contig1 contig2 contig3 contig4
# wait for jobs, then collate results
medaka stitch results/*.hdf polished.assembly.fasta

It is not recommended to specify a value of --threads greater than 2 for medaka consensus since the compute scaling efficiency is poor beyond this. Note also that medaka consensus may been seen to use resources equivalent to <threads> + 4 as an additional 4 threads are used for reading and preparing input data.

Origin of the draft sequence

Medaka has been trained to correct draft sequences output from the Flye assembler.

Processing a draft sequence from alternative sources (e.g. the output of canu or wtdbg2) may lead to different results.

Historical correction models in medaka were trained to correct draft sequences output from the canu assembler with racon applied either once, or four times iteratively. For contemporary models this is not the case and medaka should be used directly on the output of Flye.


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.

We thank Devin Drown for working through use of medaka with his RTX 2080 GPU.


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.

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