A fork of Bonito for simplified FAST5 files that contain only raw current signal (other metadata fields omitted). The modified pipeline can be used to generate the move table in other nanopore reads with personalized basecall models.
Bonito is an open source research basecaller for Oxford Nanopore reads.
For anything other than basecaller training or method development please use dorado.
For detailed information on the training process, please see the Training Documentation.
$ git clone https://github.com/Runsheng/bonito.git # clone the fork so the package can be installed
$ cd bonito
$ python3 -m venv venv3
$ source venv3/bin/activate
(venv3) $ pip install --upgrade pip
(venv3) $ pip install -e .[cu118] --extra-index-url https://download.pytorch.org/whl/cu118The ont-bonito[cu118] and ont-bonito[cu121] optional dependencies can be used, along
with the corresponding --extra-index-url, to ensure the PyTorch package matches the
local CUDA setup.
bonito view- view a model architecture for a given.tomlfile 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 (.fast5->.bam).
- Sequence Modeling With CTC
- Quartznet: Deep Automatic Speech Recognition With 1D Time-Channel Separable Convolutions
- Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing
(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 http://nanoporetech.com
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.
@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.}
}