Command line arguments¶
We provide 2 main scripts to run m6A prediction as the following.
m6anet-dataprep¶
- Input
Output files from nanopolish eventalign
| Argument name | Required | Default value | Description |
|---|---|---|---|
| –eventalign=FILE | Yes | NA | Eventalign filepath, the output from nanopolish. |
| –out_dir=DIR | Yes | NA | Output directory. |
| –n_processes=NUM | No | 1 | Number of processes to run. |
| –chunk_size | No | 1000000 | chunksize argument for pandas read csv function on the eventalign input |
| –readcount_max=NUM | No | 1000 | Maximum read counts per gene. |
| –readcount_min=NUM | No | 1 | Minimum read counts per gene. |
| –index | No | True | To skip indexing the eventalign nanopolish output, can only be used if the index has been created before |
| –n_neighbors | No | 1 | The number of flanking positions to process |
- Output
| File name | File type | Description |
|---|---|---|
| eventalign.index | csv | File index indicating the position in the eventalign.txt file (the output of nanopolish eventalign) where the segmentation information of each read index is stored, allowing a random access. |
| data.json | json | Intensity level mean for each position. |
| data.index | csv | File index indicating the position in the data.json file where the intensity level means across positions of each gene is stored, allowing a random access. |
| data.readcount | csv | Summary of readcounts per gene. |
m6anet-run_inference¶
- Input
Output files from m6anet-dataprep.
| Argument name | Required | Default value | Description |
|---|---|---|---|
| –input_dir=DIR | Yes | NA | Input directory that contains data.json, data.index, and data.readcount from m6anet-dataprep |
| –out_dir=DIR | Yes | NA | Output directory for the inference results from m6anet |
| –model_config=FILE | No | prod_pooling.toml | Model architecture specifications. Please see examples in m6anet/model/configs/model_configs/prod_pooling.toml |
| –model_state_dict=FILE | No | prod_pooling_pr_auc.pt | Model weights to be used for inference. Please see examples in m6anet/model/model_states/ |
| –batch_size | No | 64 | Number of sites to be loaded each time for inference |
| –n_processes=NUM | No | 1 | Number of processes to run. |
| –num_iterations=NUM | No | 5 | Number of times m6anet iterates through each potential m6a sites. |
- Output
| File name | File type | Description |
|---|---|---|
| data.result.csv.gz | csv.gz | Result table in compressed form |
m6anet-train¶
| Argument name | Required | Default value | Description |
|---|---|---|---|
| –model_config=FILE | Yes | NA | Model architecture specifications. Please see examples in m6anet/model/configs/model_configs/prod_pooling.toml |
| –train_config=FILE | Yes | NA | Config file for training the model. Please see examples in m6anet/model/configs/training_configs/oversampled.toml |
| –save_dir=DIR | Yes | NA | Save directory to save the training results |
| –device=STR | No | cpu | Device to use for training the model. Set to cuda:cuda_id if using GPU |
| –lr=NUM | No | 4e-4 | Learning rate for the ADAM optimizer |
| –seed=NUM | No | 25 | Random seed for model training |
| –epochs=NUM | No | 50 | Number of epochs to train the model. |
| –num_workers=NUM | No | 1 | Number of processes to run. |
| –save_per_epoch=NUM | No | 10 | Number of recurring epoch to save the model |
| –weight_decay=NUM | No | 0 | Weight decay parameteter for the ADAM optimizer |
| –num_iterations=NUM | No | 5 | Number of times m6anet iterates through each potential m6a sites. |