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https://github.com/welch-lab/SquiggleNet
https://github.com/welch-lab/SquiggleNet
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/welch-lab/SquiggleNet
- Owner: welch-lab
- License: mit
- Created: 2021-01-04T03:52:00.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-10-29T16:53:58.000Z (over 1 year ago)
- Last Synced: 2024-04-23T00:13:09.310Z (2 months ago)
- Language: Python
- Size: 888 KB
- Stars: 19
- Watchers: 6
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Lists
- awesome-nanopore - SquiggleNet - [Python] - [Real-Time, Direct Classification of Nanopore Signals with SquiggleNet](https://www.biorxiv.org/content/10.1101/2021.01.15.426907v2) (Software packages / Adaptive sampling / ReadUntil Real-time mapping / signal mapping)
README
# SquiggleNet
SquiggleNet is a 1D ResNet based model to classify Oxford Nanopore raw electrical signals as target or non-target for Read-Until sequence enrichment or depletion.
## Package Requirements
- Python >= 3.7
- Pytorch
- Scipy
- Numpy
- Glob
- Click
- Ont_fast5_api
## Inference
Use a pretrained model (two are available in the models folder, or you could use your own pretrained model) to make inference on fast5 files. To incorporate SquiggleNet into a read-until pipeline, simply run the model on the fast5 files returned by the MinKNOW API, then check the classification results to issue eject commands for the reads classified as non-target.
Options:
```python
-m, --model PATH The pretrained model path and name
-i, --infile PATH The input fast5 folder path
-o, --outfile PATH The output result folder path
-b, --batch INTEGER Batch size
-c, --cutoff INTEGER Cutoff the first c signals
--help Show this message and exit.
```
Example:
```bash
python inference.py -m models/model_B4t2_3000_tot32.ckpt -i myfolder/fast5 -o outfolder
```
## Training
### Preprocessing
To accelerate the training process, the training dataset needs to be preprocessed into a well-formatted torch tensor. This requires ground truth labels for each read ID, input as two lists of read IDs respectively. The ground truth labels could be obtained through various ways, such as barcode labeling during sample preparation or running basecalled samples against Minimap2 or other alignment algorithms (though this method could introduce systematic errors caused by the alignment algorithm of your choice).
Example:
```bash
python preprocess.py -gp groundtruth_pos.txt -gn groundtruth_neg.txt -i myfolder/fast5 -o outfolder
```
Options:
```python
-gp, --gtPos TEXT Ground truth list of positive read IDs
-gn, --gtNeg TEXT Ground trueh list of negative read IDs
-i, --inpath TEXT The input fast5 directory path
-o, --outpath TEXT The output pytorch tensor directory path
-b, --batch INTEGER Batch size, default 10000
-c, --cutoff INTEGER Cutoff the first c signals
--help Show this message and exit.
```
### Training
Run the trainer using the preprocessed dataset. Program auto-detects device and uses GPU whenever available. The option of fine tuning from a pre-trained checkpoint is also available.
Example:
```bash
python trainer.py -tt outfolder/pos_train.pt -nt outfolder/neg_train.pt -tv outfolder/pos_val.pt -nv outfolder/neg_val.pt -o trainedModel.ckpt
```
```python
-tt, --tTrain PATH The path of target sequence training set
-tv, --tVal PATH The path of target sequence validation set
-nt, --nTrain PATH The path of non-target sequence training set
-nv, --nVal PATH The path of non-target sequence validation set
-o, --outpath TEXT The output path and name for the best trained
model
-i, --interm PATH The path and name for model checkpoint (optional)
-b, --batch INTEGER Batch size, default 1000
-e, --epoch INTEGER Number of epoches, default 20
-l, --learningrate FLOAT Learning rate, default 1e-3
--help Show this message and exit.
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. For further questions, please contact [email protected].
## License
[MIT](https://choosealicense.com/licenses/mit/)
[](https://zenodo.org/badge/latestdoi/326563198)