Bonito

A convolutional basecaller inspired by QuartzNet.

Features

Raw signal input.
Simple 5 state output {BLANK, A, C, G, T}.
CTC training.
Small Python codebase.

Installation

$ pip install ont-bonito

Scripts

bonito view - view a model architecture for a given .toml file and the number of parameters in the network.
bonito tune - tune network hyperparameters.
bonito train - train a bonito model.
bonito evaluate - evaluate a model performance on a chunk basis.
bonito basecaller - basecaller (.fast5 -> .fasta).

Basecalling

(venv3) $ bonito basecaller dna_r9.4.1 /data/reads > basecalls.fasta

If you have a turing or volta GPU the --half flag can be uses to increase performance.

Training a model

(venv3) $ # download the training data and train a model with the default settings
(venv3) $ ./scripts/get-training-data
(venv3) $ bonito train ./data/model-dir ./config/quartznet5x5.toml
(venv3) $ 
(venv3) $ # train on the first gpu, use mixed precision, larger batch size and 1,000,000 chunks
(venv3) $ export CUDA_VISIBLE_DEVICES=0
(venv3) $ bonito train ./data/model-dir ./config/quartznet5x5.toml --amp --batch 64 --chunks 1000000

Automatic mixed precision can be used to speed up training by passing the --amp flag (however apex needs to be installed manually).

Developer Quickstart

$ git clone https://github.com/nanoporetech/bonito.git
$ cd bonito
$ python3 -m venv venv3
$ source venv3/bin/activate
(venv3) $ pip install --upgrade pip
(venv3) $ pip install -r requirements.txt
(venv3) $ python setup.py develop

The pretrained models can be downloaded by running ./scripts/get-models.

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.

kedartatwawadi/bonito