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TALE

Transformer-based protein function Annotation with joint sequence-Label Embedding

TALE Architecture

Joint Feature-Label Embedding

Input feature: sequence data (using transformer)

Output label: hierarchical nodes on directed graphs

Dependencies

  • TensorFlow >=1.13
  • For TALE+ (TALE+Diamond), please download Diamond and put the executable file into TALE/diamond/

For users

If you want to use TALE+ for prediction, prepare your sequence file in the fasta format and go to src/ and run:

python predict.py --input_seq $path_to_your_fasta_file --ontology on --outputpath $path_to_your_output_file

where on=mf,bp,cc for MFO,BPO and CCO, respectively.

For developers

Training and test data:

  • Data/Gene_Ontology/EXP_Swiss_Prot/train_seq_mf: The training sequence file for MFO
  • Data/Gene_Ontology/EXP_Swiss_Prot/train_label_mf: The training label file for MFO
  • Data/Gene_Ontology/EXP_Swiss_Prot/test_seq_mf: The test sequence file for MFO
  • Data/Gene_Ontology/EXP_Swiss_Prot/test_label_mf: The test label file for MFO
  • Data/Gene_Ontology/EXP_Swiss_Prot/mf_go_1.pickle: The ontology file for MFO

Data formats:

Sequence

The sequence file is a list, where each element is a directory having the following information:

  • 'ID': The ID of the sequence in Swiss-Prot
  • 'ac': The acession number of the sequence in Swiss-Prot
  • 'date': The date of the sequence released in Swiss-Prot
  • 'seq': The amino acid sequence
  • 'GO': The GO annotations of the sequence

Label

  • The label file is a list, where each element is a list containing the indexes of labels (GO terms).

Ontology

The ontology file is a directory, where each key is a GO term (e.g. 'GO:0030234') in the ontology. Each value is also a directory containing the information for that key:

  • 'name': The name of the GO term
  • 'ind': The index of this GO term
  • 'father': The parent GO terms
  • 'child': The children GO terms

Training:

In order to train the model, under src/, run:

python train.py --batch_size 32 --epochs 100 --lr 1e-3 --save_path ./log/ --ontology mf --data_path ../data/Gene_Ontology/EXP_Swiss_Prot/ --regular_lambda 0

The above example is to train a model with 32 batch size, 100 epochs, 1e-3 learning rate, MFO ontology, 0 lambda value, with training data path at '../data/Gene_Ontology/EXP_Swiss_Prot/' and save the trained model in './log/'.

Trained models:

The trained models are in 'trained_models/'. (e.g. model1_mf* is the model1 on MFO)

Citation

TBD

Contact:

Yang Shen: [email protected]

Yue Cao: [email protected]