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https://github.com/nanoporetech/bonito
A PyTorch Basecaller for Oxford Nanopore Reads
https://github.com/nanoporetech/bonito
basecalling nanopore pytorch
Last synced: 3 months ago
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A PyTorch Basecaller for Oxford Nanopore Reads
- Host: GitHub
- URL: https://github.com/nanoporetech/bonito
- Owner: nanoporetech
- License: other
- Created: 2019-11-19T13:46:22.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-06-04T11:55:44.000Z (5 months ago)
- Last Synced: 2024-07-22T03:03:17.004Z (4 months ago)
- Topics: basecalling, nanopore, pytorch
- Language: Python
- Homepage: https://nanoporetech.com/
- Size: 742 KB
- Stars: 384
- Watchers: 36
- Forks: 118
- Open Issues: 92
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-nanopore - Bonito - [Python] - A PyTorch Basecaller for Oxford Nanopore Reads (research, not production basecaller) (Software packages / Basecalling)
README
# Bonito
[](https://badge.fury.io/py/ont-bonito)
[](https://img.shields.io/badge/python-3.8-brightgreen.svg)
[](https://img.shields.io/badge/python-3.9-brightgreen.svg)
[](https://img.shields.io/badge/python-3.10-brightgreen.svg)
[](https://img.shields.io/badge/python-3.11-brightgreen.svg)
[](https://img.shields.io/badge/cuda-11.8-blue.svg)
Bonito is an open source research basecaller for Oxford Nanopore reads.
For anything other than basecaller training or method development please use [dorado](https://github.com/nanoporetech/dorado).
```bash
$ pip install --upgrade pip
$ pip install ont-bonito
$ bonito basecaller [email protected] /data/reads > basecalls.bam
```
Bonito supports writing aligned/unaligned `{fastq, sam, bam, cram}`.
```bash
$ bonito basecaller [email protected] --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 -
``` bash
$ bonito download --models --show # show all available models
$ bonito download --models # download all available models
```
## Transformer Models
The `bonito.transformer` package requires
[flash-attn](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features).
This must be manually installed as the `flash-attn` packaging system prevents it from being listed as a normal dependency.
Setting `CUDA_HOME` to the relevant library directory will help avoid CUDA version mismatches between packages.
## Modified Bases
Modified base calling is handled by [Remora](https://github.com/nanoporetech/remora).
```bash
$ bonito basecaller [email protected] /data/reads --modified-bases 5mC --reference ref.mmi > basecalls_with_mods.bam
```
See available modified base models with the ``remora model list_pretrained`` command.
## Training your own model
To train a model using your own reads, first basecall the reads with the additional `--save-ctc` flag and use the output directory as the input directory for training.
```bash
$ bonito basecaller [email protected] --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.
```bash
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.
```bash
$ 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 `--no-amp` flag to True.
## Developer Quickstart
```bash
$ 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 .[cu118] --extra-index-url https://download.pytorch.org/whl/cu118
```
The `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.
## 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 *(`.fast5` -> `.bam`)*.
### References
- [Sequence Modeling With CTC](https://distill.pub/2017/ctc/)
- [Quartznet: Deep Automatic Speech Recognition With 1D Time-Channel Separable Convolutions](https://arxiv.org/pdf/1910.10261.pdf)
- [Pair consensus decoding improves accuracy of neural network basecallers for nanopore sequencing](https://www.biorxiv.org/content/10.1101/2020.02.25.956771v1.full.pdf)
### 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
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.}
}
```