v0.8.1

• e33a860 Attention Is All You Need!
• 454324a v5.0.0 models and example training sets for DNA & RNA.
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  • example_data_dna_r10.4.1_v0
  • example_data_rna004_v0
• ed36968 new model configs.
• 37a0557 default alignment preset is now lr:hq.
• d4f6dd2 unpin bonito requirements.
• 40a9753 fast5 deprecation warning.
• 0ba190f fixed progress count when setting --max-reads.
• 6c8ecb5 batchnorm fusing for inference.
• 302b1ce bonito view now accepts a model directory or a config.
• a170b7c default scaling fixes.

v0.7.3

• 775ce75 v4.3 simplex models.
• 214bac6 RNA004 models.
• e87b05e RNA Remora fix.
• fc1d62a Add autocast to validation.

v0.7.2

• 0061534 Upgrade remora to 2.1.3.
• cd62fbb multi-channel input support.
• 7f79a08 Allow bonito to be run as a module.
• f403b40 Set v4 flag for bonito export.
• a9823b3 fix bonito convert nep-0034.

v0.7.1

Highlights

• 9113e24 v4.2.0 5kHz simplex models.
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• 8c96eb8 make sample_id optional for fast5 input.
• 3b4bcad ensure decoder runs on same device as nn model.
• 8fe1f61 fix training data downloading.
• 26d52d9 set default --valid-chunks to None.
• ebc32a0 fix models as list.

Thanks @chAwater for his collection of bug fixes in this release.

Installation

$ pip install ont-bonito

Note: For anything other than basecaller training or method development please use dorado.

v0.7.0

Highlights

• 66ee29a v4.1.0 simplex models.
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• 4cf3c6f torch 2.0 + updated requirements.
• 3bc338a fix use of TLEN.
• 21df7d5 v4.0.0 simplex models.
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Installation

Torch 2.0 (from pypi.org) is now built using CUDA 11.7 so the default installation of ont-bonito can be used for Turing/Ampere GPUs.

$ pip install ont-bonito

Note: For anything other than basecaller training or method development please use dorado.

v0.6.2

da7fe39 upgrade to pod5 0.0.41.
c45905c add milliseconds to start_time + convert to UTC.
199a3f0 Adds duration as du tag to BAM output.
717f414 fix bug in fast5 read id subset pre-processing.

v0.6.1

Bugfixes

• ebfc649 set TLEN in SAM.
• 0296226 fixed --save-ctc writing.
• c4da679 5fadc78 more robust signal trimming.

v0.6.0

Highlights

• f2a3a8e improved quantile based signal scaling algorithm.
• 552a5ce significant improvement in short read calling vs previous bonito versions.
• 5ceb6e1 qscore filtering.
• cfb7e06 new R10.4.1 E8.2 models (v3.5.2) for both 260bps and 400bps conditions.
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Bugfixes

• fa56de1 skip over any fast5 files that cause runtime errors.
• f0827d9 use stderr for all model download output to avoid issues with sequence output formats.
• 3c8294b upgraded koi with py3.7 support.

Misc

• Python 3.10 supported added.
• Read tags added for signal scaling midpoint, dispersion and version.
• 9f7614d support for exporting models to dorado.
• 90b6d19 add estimated total time to basecaller progress
• 8ba78ed export for guppy binary weights

Installation

$ pip install ont-bonito

By default pip will install torch which is build against CUDA 10.2. For CUDA 11.3 builds run:

$ pip install --extra-index-url https://download.pytorch.org/whl/cu113 ont-bonito

Note: packaging has been reworked and the ont-bonito-cuda111 and ont-bonito-cuda113 packages are now retired. The CUDA version of torch is handled exclusively with the use of pip install --extra-index-url now.

v0.5.3

Highlights

• c0f83f3 POD5 support - https://github.com/nanoporetech/pod5-file-format
• 27f8468 Kit 14 models dna_r10.4.1_e8.2_{fast, hac, sup}@v3.5.1 added.
• e6282a2 Upgrade to remora 1.1 (kit 14 mod bases).

Bugfixes

• 4585b74 fix for handling stitching of short reads (read < chunksize).
• 9a4f98a fix for overly confidant qscores in repeat regions.
• 3187198 scaling protection for short reads.

Misc

• d57a658 training validation times improved.

Installation

The default version on PyTorch in PyPI supports Volta and below (SM70) and can be installed like so -

$ pip install ont-bonito

For newer GPUs (Turing, Ampere) please use -

$ pip install -f https://download.pytorch.org/whl/torch_stable.html ont-bonito-cuda113

v0.5.1

Highlights

• There is no longer a requirement for a CUDA toolkit on the target system, which significantly improves the ease of installation¹.
• BAM spec 0.0.2 (+move table, numbers of samples, trimming information).

Features

• 241e622 record the move table into the SAM/BAM.
• a6a3ed2 ont-koi replaces seqdist + cupy¹.

Bugfixes

• c8417b7 handle datatimes with subsecond resolution.
• 6f23467 fix the mappy preset.
• 737d9a2 better management of mappy's memory usage.
• 2bbd711 remora 0.1.2 - fixes bonito/remora hanging #216.
• 6e91a9d sensible minimum scaling factor - fixes #209.

Misc

• Upgrade to the latest Mappy.
• Python3.6 support dropped (EOL).

Installation

The default version on PyTorch in PyPI supports Volta and below (SM70) and can be installed like so -

$ pip install ont-bonito

For newer GPUs (Turing, Ampere) please use -

$ pip install -f https://download.pytorch.org/whl/torch_stable.html ont-bonito-cuda113