Microexons are a particular kind of exons with length of less than 30 nucleotides. More than 60% of annotated human microexons were found to have high levels of sequence conservation, indicating of potential functions. The tool was developed to predict functional microexons using TCA(transfer component analysis) and KNN(K-Nearest Neighbor) with k=5. Please refer to our paper Prediction of Functional Microexons with Transferring Learning for more details.
- ExtractedFeatures: Feature extraction source codes, we had packaged these programs as a jar in tool/feature_abstract/ExtractedFeature.jar;
- ModelingAndEvaluating: Including all experimental data, modeling and evaluating codes;
- Tool: Available complete tool.
If you only use this tool to predict Functional Microexons, we also provided a long-term online service.
- python 3.7.1
- java 14.0
- numpy 1.16.0
- pandas 0.23.4
The folder “Tool” already contains all the source code for predicting functional microexons.
- Download databases from https://drive.google.com/file/d/1VOBYv1MO4XUvxy7xg32phKOO6KgjFQs0/view?usp=sharing;
- Assign correct databases path in Tool/configuration.txt;
- Input your variants (one position based on 0 of microexon per row, example: chr11:233910:233928 that is a microexon with 18bps) in Tool/input.txt.
- You can get prediction results in Tool/results.txt using following command
python Predict.py- This program applies to microexons with length less than 30bps and being an integer of 3;
- Thispositions of inputed microexons are based on the GRCH37/hg19；
- This positions of inputed microexons are based on 0.