© 2018- Oxford Nanopore Technologies Ltd.

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 outperforms graph-based methods operating on basecalled data, and can be competitive with state-of-the-art signal-based methods whilst being much faster.

As input medaka accepts reads in either a .fasta or a .fastq file. It requires a draft assembly as a .fasta.

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


  • Requires only basecalled data (.fasta or .fastq).

  • Improved accurary over graph-based methods (e.g. Racon).

  • Methylation aggregation from Guppy .fast5 files.

  • 50X faster than Nanopolish (and can run on GPUs)..

  • Benchmarks are provided (see Benchmarks).

  • Includes extras for implementing and training bespoke correction networks.

  • Works on Linux (MacOS and Windows support is untested).

  • Open source (Mozilla Public License 2.0).

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

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.


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.

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