fast5_rekindler 0.0.6

Collates information from BAM and POD5 files and generates FAST5 files for use in legacy tools such as tailfindr.

fast5_rekindler

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Documentation: https://adnaniazi.github.io/fast5_rekindler

Source Code: https://github.com/adnaniazi/fast5_rekindler

PyPI: https://pypi.org/project/fast5_rekindler/

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Collates information from BAM and POD5 files and generates FAST5 files for use in legacy tools such as tailfindr.

Installation

1. Create Python 3.10 or 3.11 environment.

conda create -n f5r python=3.11

2. Activate the environment.

conda activate f5r

3. Install FAST5 Rekindler.

pip install fast5_rekindler

Usage

FAST5 rekindler needs:

1. A BAM file with moves table in it.

You can generate it using Dorado:

dorado basecaller /path/to/basecalling/model \
  /pod5/dir/path \
  --recursive  \
  --emit-sam  \
  --emit-moves  \
  --device "cpu"  \ # or "cuda:all"
  --reference /path/to/alginment/reference > /path/to/calls.sam

2. Convert Doarado's output SAM file to a BAM file.

samtools view -bS /path/to/calls.sam > /path/to/calls.bam

3. Sort the BAM file.

samtools sort /path/to/calls.bam -o /path/to/sorted.calls.bam

4. Use FAST5 Rekindler to convert POD5 files to FAST5 files.

fast5_rekindler /path/to/sorted.calls.bam  \
  /path/to/pod5_dir \
  /path/to/output_dir \
  --num_processes 100

To invoke help for FAST5 Rekindler, just type:

fast5_rekindler --help

Development

• Clone this repository
• Requirements:
  • Poetry
  • Python 3.7+
• Create a virtual environment and install the dependencies

poetry install

• Activate the virtual environment

poetry shell

Testing

pytest

Documentation

The documentation is automatically generated from the content of the docs directory and from the docstrings of the public signatures of the source code. The documentation is updated and published as a Github project page automatically as part each release.

Releasing

Trigger the Draft release workflow (press Run workflow). This will update the changelog & version and create a GitHub release which is in Draft state.

Find the draft release from the GitHub releases and publish it. When a release is published, it'll trigger release workflow which creates PyPI release and deploys updated documentation.

Pre-commit

Pre-commit hooks run all the auto-formatters (e.g. black, isort), linters (e.g. mypy, flake8), and other quality checks to make sure the changeset is in good shape before a commit/push happens.

You can install the hooks with (runs for each commit):

pre-commit install

Or if you want them to run only for each push:

pre-commit install -t pre-push

Or if you want e.g. want to run all checks manually for all files:

pre-commit run --all-files