Package: ont-bonito-cuda113
BonitoA PyTorch Basecaller for Oxford Nanopore Reads.$pipinstall--upgradepip
$pipinstallont-bonito
$bonitobasecallerdna_r10.4_e8.1_sup@v3.4/data/reads>basecalls.bamBonito supports writing aligned/unaligned{fastq, sam, bam, cram}.$bonitobasecallerdna_r10.4_e8.1_sup@v3.4--referencereference.mmi/data/reads>basecalls.bamBonito will download and cache the basecalling model automatically on first use but all models can be downloaded with -$bonitodownload--models--show# show all available models$bonitodownload--models# download all available modelsThe defaultont-bonitopackage is built against CUDA 10.2 however CUDA 11.1 and 11.3 builds are available.$pipinstall-fhttps://download.pytorch.org/whl/torch_stable.htmlont-bonito-cuda111Modified BasesModified base calling is handled byRemora.$bonitobasecallerdna_r10.4_e8.1_sup@v3.4/data/reads--modified-bases5mC--referenceref.mmi>basecalls_with_mods.bamSee available modified base models with theremora model list_pretrainedcommand.Training your own modelTo train a model using your own reads, first basecall the reads with the additional--save-ctcflag and use the output directory as the input directory for training.$bonitobasecallerdna_r9.4.1--save-ctc--referencereference.mmi/data/reads>/data/training/ctc-data/basecalls.sam
$bonitotrain--directory/data/training/ctc-data/data/training/model-dirIn addition to training a new model from scratch you can also easily fine tune one of the pretrained models.bonitotrain--epochs1--lr5e-4--pretraineddna_r10.4_e8.1_sup@v3.4--directory/data/training/ctc-data/data/training/fine-tuned-modelIf you are interested in method development and don't have you own set of reads then a pre-prepared set is provide.$bonitodownload--training
$bonitotrain/data/training/model-dirAll training calls use Automatic Mixed Precision to speed up training. To disable this, set the--no-ampflag to True.Developer Quickstart$gitclonehttps://github.com/nanoporetech/bonito.git# or fork first and clone that$cdbonito
$python3-mvenvvenv3
$sourcevenv3/bin/activate(venv3)$pipinstall--upgradepip(venv3)$pipinstall-rrequirements.txt(venv3)$pythonsetup.pydevelopInterfacebonito 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).ReferencesSequence Modeling With CTCQuartznet: Deep Automatic Speech Recognition With 1D Time-Channel Separable ConvolutionsPair consensus decoding improves accuracy of neural network basecallers for nanopore sequencingLicence 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 athttp://nanoporetech.comResearch ReleaseResearch 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.