MSRCall: A Multi-scale Deep Neural Network to Basecall Oxford Nanopore Sequences
Yang-Ming Yeh and Yi-Chang Lu
You can put your own test reads in fast5 format in the dataset folder and modify the path in script run_0_preprocee_testsets.sh
In our paper, we used dataset from Ryan Wick et. al. that can be downloaded from this website:
https://doi.org/10.26180/5c5a5fa08bbee
Our code is tested on cuda 10.0, cudnn 7.6
Please install ctcdecode from:
https://github.com/parlance/ctcdecode.git
Install required python packages:
pip install -r requirement.txt
optional software: minimap2
bash ./run_0_preprocee_testsets.sh
The preprocessed .npy files are put in the preprocess_test directory.
python call.py -model exp_backup/MSRCall/MSRCall.chkpt -records_dir preprocessed_test/Acinetobacter_pittii_16_377_0801/ -output MSRCall_out
You can change the test data by replacing Acinetobacter_pittii_16_377_0801 with your own filename.
Basecalled results are stored in the MSRCall_out folder.
You can reproduce the training process by:
python MSRCall_train.py -save_model ${modelName} -as ${train_set_dir}/signals/ -al ${train_set_dir}/labels/ -es ${val_set_dir}/signals/ -el ${val_set_dir}/labels/
As for the training/validation set generation, we follow the same procedures used in SACall. Please refer to scripts for training set in SACall and scripts for validation set in SACall.
Wick, R. R., Judd, L. M., & Holt, K. E. (2019). Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome biology, 20(1), 1-10.
Huang, N., Nie, F., Ni, P., Luo, F., & Wang, J. (2022). SACall: A Neural Network Basecaller for Oxford Nanopore Sequencing Data Based on Self-Attention Mechanism. IEEE/ACM transactions on computational biology and bioinformatics, 19(1), 614–623.
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