Need help with bonito?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

205 Stars 66 Forks Other 518 Commits 57 Opened issues


A PyTorch Basecaller for Oxford Nanopore Reads

Services available


Need anything else?

Contributors list


PyPI version

A PyTorch Basecaller for Oxford Nanopore Reads.

$ pip install ont-bonito
$ bonito basecaller dna_r9.4.1 /data/reads > basecalls.fasta

If a reference is provided in either

format then bonito will output in
$ bonito basecaller dna_r9.4.1 --reference reference.mmi /data/reads > basecalls.sam

The default

package is built against CUDA 10.2 however a CUDA 11.1 build is available.
$ pip install -f ont-bonito-cuda111

Developer Quickstart

$ git clone  # or fork first and clone that
$ cd bonito
$ python3 -m venv venv3
$ source venv3/bin/activate
(venv3) $ pip install --upgrade pip
(venv3) $ pip install -r requirements.txt
(venv3) $ python develop
(venv3) $ bonito download --models --latest


The following pretrained models are available to download with

bonito download

| Model | Type | Bonito Version | | ------ | ------ |------ | |

[email protected]
[email protected]
| CRF-CTC RNN (fixed blank score) | v0.3.7 | |
[email protected]
[email protected]
| CRF-CTC RNN | v0.3.6 | |
[email protected]
| CRF-CTC RNN | v0.3.2 | |
[email protected]
| CRF-CTC RNN | v0.3.1 | |
[email protected]
| CRF-CTC RNN | v0.3.0 | |
[email protected]
| CTC CNN (Custom QuartzNet) | v0.2.0 | |
[email protected]
| CTC CNN (5x5 QuartzNet) | v0.1.2 |

All models can be downloaded with

bonito download --models
or if you just want the latest version then
bonito download --models --latest -f

Training your own model

To train a model using your own reads, first basecall the reads with the additional

flag and use the output directory as the input directory for training.
$ bonito basecaller dna_r9.4.1 --save-ctc --reference reference.mmi /data/reads > /data/training/ctc-data/basecalls.sam
$ bonito train --directory /data/training/ctc-data /data/training/model-dir

In addition to training a new model from scratch you can also easily fine tune one of the pretrained models.

bonito train --epochs 1 --lr 5e-4 --pretrained [email protected] --directory /data/training/ctc-data /data/training/fine-tuned-model

If you are interested in method development and don't have you own set of reads then a pre-prepared set is provide.

$ bonito download --training
$ bonito train /data/training/model-dir

All training calls use Automatic Mixed Precision to speed up training. To disable this, set the

flag to True.


Duplex calling takes template and complement reads and produces a single higher quality call.

$ bonito duplex dna_r9.4.1 /data/reads --pairs pairs.txt --reference ref.mmi > basecalls.sam


file is expected to contain pairs of read ids per line (seperated by a single space).

Follow on reads can also be automatically paired if an alignment summary file is provided instead of a

$ bonito duplex dna_r9.4.1 /data/reads --summary sequencing_summary.txt --reference ref.mmi > basecalls.sam


  • bonito view
    - view a model architecture for a given
    file and the number of parameters in the network.
  • bonito train
    - train a bonito model.
  • bonito convert
    - convert a hdf5 training file into a bonito format.
  • bonito evaluate
    - evaluate a model performance.
  • bonito download
    - download pretrained models and training datasets.
  • bonito basecaller
    - basecaller (


Licence and Copyright

(c) 2019 Oxford Nanopore Technologies Ltd.

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

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 use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.