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.
  • dna_r10.4.1_e8.2_400bps_fast@v4.2.0
  • dna_r10.4.1_e8.2_400bps_hac@v4.2.0
  • dna_r10.4.1_e8.2_400bps_sup@v4.2.0
• 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.
  • dna_r10.4.1_e8.2_260bps_fast@v4.1.0
  • dna_r10.4.1_e8.2_260bps_hac@v4.1.0
  • dna_r10.4.1_e8.2_260bps_sup@v4.1.0
  • dna_r10.4.1_e8.2_400bps_fast@v4.1.0
  • dna_r10.4.1_e8.2_400bps_hac@v4.1.0
  • dna_r10.4.1_e8.2_400bps_sup@v4.1.0
• 4cf3c6f torch 2.0 + updated requirements.
• 3bc338a fix use of TLEN.
• 21df7d5 v4.0.0 simplex models.
  • dna_r10.4.1_e8.2_260bps_fast@v4.0.0
  • dna_r10.4.1_e8.2_260bps_hac@v4.0.0
  • dna_r10.4.1_e8.2_260bps_sup@v4.0.0
  • dna_r10.4.1_e8.2_400bps_fast@v4.0.0
  • dna_r10.4.1_e8.2_400bps_hac@v4.0.0
  • dna_r10.4.1_e8.2_400bps_sup@v4.0.0

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.
  • dna_r10.4.1_e8.2_260bps_fast@v3.5.2
  • dna_r10.4.1_e8.2_260bps_hac@v3.5.2
  • dna_r10.4.1_e8.2_260bps_sup@v3.5.2
  • dna_r10.4.1_e8.2_400bps_fast@v3.5.2
  • dna_r10.4.1_e8.2_400bps_hac@v3.5.2
  • dna_r10.4.1_e8.2_400bps_sup@v3.5.2

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

v0.5.0

Highlights

• Modified basecalling via Remora.
• Aligned/unaligned SAM/BAM/CRAM output support with read groups (draft spec).
• Fast/HAC/SUP models for R9.4.1 E8, R9.4.1 E8.1 and R10.4 E8.1.
• Model performance for SUP & HAC models are now inline with Guppy.
• Fully calibrated qstring/qscores for all models.
• Automatic model downloading.

Modified Basecalling

Methylation/modified base calling can now be enabled with a single flag --modified-bases.

$ bonito basecaller dna_r9.4.1_e8_hac@v3.3 reads --modified-bases 5mC --ref ref.mmi | samtools sort -o out.bam -
$ samtools index out.bam
$ modbam2bed -a 0.2 -b 0.8 --cpg -r chr20 -m 5mC -e ref.fa out.bam > results_5mC.bed

Models

All model identifiers include the model version, ambiguous unversioned models are no longer provided.

Condition	Fast	High Accuracy	Super Accuracy
R9.4.1 E8	dna_r9.4.1_e8_fast@v3.4	dna_r9.4.1_e8_hac@v3.3	dna_r9.4.1_e8_sup@v3.3
R9.4.1 E8.1	dna_r9.4.1_e8.1_fast@v3.4	dna_r9.4.1_e8.1_hac@v3.3	dna_r9.4.1_e8.1_sup@v3.3
R10.4 E8.1	dna_r10.4_e8.1_fast@v3.4	dna_r10.4_e8.1_hac@v3.4	dna_r10.4_e8.1_sup@v3.4

Available models can be listed with bonito download --models --list.

v3.4 models are newly released whereas v3.3 models have been available previously, however, all models have newly tuned configs. Fast models are now higher accuracy 128 wide models.

Models configs have been tuned for performance and the batch sizes have been selected to use approximately 11GB of GPU memory. If you have a GPU with less than this please reduce the batch size with --batchsize when base calling.

Misc

• CUDA 11.3 builds added.
• Updated dependency highlights: pytorch==1.10, mappy=2.23.
• Duplex calling superseded by significantly higher performance inplmention in Guppy 6.0.
• Basecaller default parameters can now be set in the model config.toml under the [basecaller] section
• Command line parameters will now override config.toml settings.
• SAM tags included when output .fastq (SAM/BAM/CRAM is recommended however).

Full Changelog: v0.4.0...v0.5.0