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.

1. python 3.7.1
2. java 14.0
3. numpy 1.16.0
4. pandas 0.23.4

The folder “Tool” already contains all the source code for predicting functional microexons.

1. Download databases from https://drive.google.com/file/d/1VOBYv1MO4XUvxy7xg32phKOO6KgjFQs0/view?usp=sharing;
2. Assign correct databases path in Tool/configuration.txt;
3. 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.
4. You can get prediction results in Tool/results.txt using following command

python Predict.py

1. This program applies to microexons with length less than 30bps and being an integer of 3;
2. Thispositions of inputed microexons are based on the GRCH37/hg19；
3. This positions of inputed microexons are based on 0.