Development¶
As well as providing the useful functionality of creating consensus calls from
basecall data, medaka
demonstrates a framework for both training and
inference. The code exploits the keras deep learning
library.
A simple method presented to inspire further ideas is to align input sequences and a truth sequence to a common baseline (e.g. reads and a reference to a draft assembly), and extract ‘feature vectors’ for input into a neural network classifier.
API reference¶
The medaka library comprising feature generation, data labelling, and batching code is detailed below.
- medaka package
- Subpackages
- Submodules
- medaka.align module
- medaka.common module
- medaka.datastore module
- medaka.executor module
- medaka.features module
- medaka.keras_ext module
- medaka.labels module
- medaka.medaka module
- medaka.medaka_counts module
- medaka.models module
- medaka.options module
- medaka.prediction module
- medaka.rle module
- medaka.smolecule module
- medaka.stitch module
- medaka.training module
- medaka.variant module
- medaka.vcf module
- medaka.wrappers module
- Module contents
- Subpackages