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The Quadtree is a gradient-boosted decision tree model used to predict guanine quadruplexes in DNA sequences. It's developed on top of the LightGBM python library. Each sequence base is encoded based on a given encoding prescription. The model was trained to be used with a sliding window and analyses the whole sequence. Machine learning model can be used as python script or thru preview websitequadtree.vercel.app

quadtree    └─ web -> preview website source code    └─ python          └─ model -> lightgbm model params          └─ train -> example files how training was performed          └─ quadtree.py -> predictor

• lightgbm==3.3.2
• numpy==1.21.2

Before using install the requirements:

  pip install -r requirements.txt

fromquadtreeimportQuadtreemodel=Quadtree()

• sequence as a string (maximum length is not limited)
• threshold (recommended values is 0.2)
• quadnet model file path

result=quadtree.analyse(sequence='ATTAATACTTTTAACAATTGTAGTATATAAAAAAGGGAGTAACC...',model_path='/path/to/quadnet_model.txt',',score_threshold=0.1)

Results are then returned in given form which can be loaded into pandas DataFrame.

importpandasaspddf=pd.DataFrame(result)

These parameter were used to train lightgbm model

• Patrik Kaura -Main developer -patrikkaura

This project is licensed under the MIT License - see theLICENSE file for details. # quadtree