wangjun258/signalp-6.0

Multi-class signal peptide prediction and structure decoding model.

SignalP 6.0

Signal peptide prediction model based on a Bert protein language model encoder and a conditional random field (CRF) decoder.

This is the development codebase. If you are looking for the prediction service, go to https://services.healthtech.dtu.dk/service.php?SignalP-6.0.

The installation instructions for the installable SignalP 6.0 prediction tool can be found here.

Install in editable mode with pip install -e ./ to experiment.

Data

The training dataset as well as the full dataset before homology partitioning are in data. The directory additionally contains the extended vocabulary of the ProtTrans BertTokenizer used.

Training

You can find the training script in scripts/train_model.py. The pytorch model is in src/signalp6/models. Please refer to the training script source for the meaning of all parameters.

A basic training command looks like this:

python3 scripts/train_model.py --data data/train_set.fasta --test_partition 0 --validation_partition 1 --output_dir testruns --experiment_name testrun1 --remove_top_layers 1 --kingdom_as_token --sp_region_labels --region_regularization_alpha 0.5 --constrain_crf --average_per_kingdom

Other things in package

• training_utils contains parts that were used to fit the model, e.g. dataloading and regularization.
• utils contains other utilities, such as functions to calculate metrics or region statistics.