Bonita Model Set 1
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A PyTorch Basecaller for Oxford Nanopore Reads
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A PyTorch Basecaller for Oxford Nanopore Reads.
If a reference is provided in either .fasta or .mmi format then bonito will output in sam format.
The following pretrained models are available to download with bonito download.
All models can be downloaded with bonito download --models or if you just want the latest version then bonito download --models --latest -f.
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.
In addition to training a new model from scratch you can also easily fine tune one of the pretrained models.
If you are interested in method development and don't have you own set of reads then a pre-prepared set is provide.
All training calls use Automatic Mixed Precision to speed up training. To disable this, set the --no-amp flag to True.
Duplex calling takes template and complement reads and produces a single higher quality call.
The pairs.csv 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 pairs.csv.
(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.
A PyTorch Basecaller for Oxford Nanopore Reads
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Bonita Model Set 1
