Bonito

Bonito is an open source research basecaller for Oxford Nanopore reads.

For anything other than basecaller training or method development please use dorado.

$ pip install --upgrade pip
$ pip install ont-bonito
$ bonito basecaller dna_r10.4.1_e8.2_400bps_hac@v5.0.0 /data/reads > basecalls.bam

Bonito supports writing aligned/unaligned {fastq, sam, bam, cram}.

$ bonito basecaller dna_r10.4.1_e8.2_400bps_hac@v5.0.0 --reference reference.mmi /data/reads > basecalls.bam

Bonito will download and cache the basecalling model automatically on first use but all models can be downloaded with -

$ bonito download --models --show  # show all available models
$ bonito download --models         # download all available models

Modified Bases

For modified-base calling with ont-supported mods please use dorado For development of modified base calling models please see remora.

Training your own model

For detailed information on the training process, please see the Training Documentation.

Developer Quickstart

$ git clone https://github.com/nanoporetech/bonito.git  # or fork first and clone that
$ cd bonito
$ python3 -m venv venv3
$ source venv3/bin/activate
(venv3) $ pip install --upgrade pip
(venv3) $ pip install -e .[cu128] --extra-index-url https://download.pytorch.org/whl/cu128

The ont-bonito[cu128] and ont-bonito[cu130] optional dependencies can be used, along with the corresponding --extra-index-url, to ensure the PyTorch package matches the local CUDA setup.

Interface

bonito view - view a model architecture for a given .toml file and the number of parameters in the network.
bonito train - train a bonito model.
bonito evaluate - evaluate a model performance.
bonito download - download pretrained models and training datasets.
bonito basecaller - basecaller (.pod5 -> .bam).

References

Licence and Copyright

Bonito is distributed under the terms of the Oxford Nanopore Technologies, Ltd. Public License, v. 1.0. If a copy of the License was not distributed with this file, You can obtain one at http://nanoporetech.com

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.

Citation

@software{bonito,
  title = {Bonito: A PyTorch Basecaller for Oxford Nanopore Reads},
  author = {{Chris Seymour, Oxford Nanopore Technologies Ltd.}},
  year = {2019},
  url = {https://github.com/nanoporetech/bonito},
  note = {Oxford Nanopore Technologies, Ltd. Public License, v. 1.0},
  abstract = {Bonito is an open source research basecaller for Oxford Nanopore reads. It provides a flexible platform for training and developing basecalling models using PyTorch.}
}

Metadata

Release files for ont-bonito 1.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for ont-bonito 1.1.0
File	Interpreter	ABI	Platform	Reset
ont_bonito-1.1.0-py3-none-any.whl 62.0 kB Feb 20, 2026	Python 3	none	any	Details

Release files / ont_bonito-1.1.0-py3-none-any.whl

Download URL	ont_bonito-1.1.0-py3-none-any.whl
Size	62.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`7e6244e5b66d64e7dd000c5710de52b336ce81324ce4eb52433887d1a4470e23`
BLAKE2b-256 checksum How to use checksums	`1532e0bbd26127e014eb0589a1a260489f84d4f1d4aae7061435332299683a10`
Upload date	Feb 20, 2026
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Uploaded via	`twine/6.2.0 CPython/3.13.12`