LoopPredictor employs an assembly of multi-omics features to learn active long-range enhancer mediated looping characteristics. Users need only to provide multi-omics datasets as input, alongside our adaptive training model, into LoopPredictor to generate a list of predicted loops ranked by confidence, and comprehensively annotated. It can be used to predict enhancer mediated loops in a genome-wide fashion across different cell lines, and applicable to different model organisms.

Tang, L., Hill, M. C., Wang, J., Wang, J., Martin, J. F., & Li, M. (2020). Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model. Genome research, 30(12), 1835-1845.

LoopPredictor test case:

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Please paste/upload interested regions for the prediction (optional) more details see tutorial

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