RODAN

A fully convolutional architecture for basecalling nanopore RNA sequencing data

Generated Taiyaki RNA data: https://doi.org/10.5281/zenodo.4556884

RNA training and validation data: https://doi.org/10.5281/zenodo.4556950

RNA test data: https://doi.org/10.5281/zenodo.4557004

Requirements

• Python 3
• torch >= 1.4.0 <= 1.8.0
• numpy
• h5py
• ont-fast5-api
• fast-ctc-decode
• pyyaml
• tensorboard
• pytorch-ranger (only for training)

Installation

Create a python virtual environment.

python3 -m venv virtualenv
source virtualenv/bin/activate
git clone https://github.com/biodlab/RODAN.git
cd RODAN
pip install -r requirements.txt

MODEL_DP_REPLACE

python model_dp_replace.py -c rna_replace.config -n runs -l > output_dp_replace.txt

MODEL_DP_INSERT

python model_dp_insert.py -c rna_insert.config -n runs -l > output_dp_insert.txt

Basecalling

To basecall (must be run from root directory):

./basecall.py /path/to/fast5files > outfile.fasta or

python basecall.py /path/to/fast5files > outfile.fasta

Basecall will recursively search in the specified directory for all fast5 files which can be single or multi fast5 files.

Test data

Five samples of human RNA fast5 data is provided in test-data.tgz.

minimap2 Alignment

minimap2 --secondary=no -ax map-ont -t 32 --cs genomefile fastafile > file.sam

Accuracy

python accuray.py /path/to/samfile /path/to/genomefile