DeepCLIP is a novel deep learning tool for finding binding-preferences of RNA-binding proteins.

Summary of DeepCLIP and its functionalities:
DeepCLIP is a neural network with shallow convolutional layers connected to a bidirectional LSTM layer.
DeepCLIP outputs binding profiles and overall scores for each input sequences.
Binding profiles show whether areas of sequences contain possible binding sites or whether they look like random genomic background.

On this webserver you can use our pretrained models to predict on your own sequences, or train your own model.
However, for optimal training, we highly recommend using CV runmode on your own server with a local DeepCLIP installation.
You can navigate using the menu at the top. New users are encouraged to read the DeepCLIP guide first.

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About

DeepCLIP was developed by Alexander GB Grønning and Thomas K Doktor, with additional code by Simon J Larsen.

Citation

If you use DeepCLIP in your work, we kindly ask you to cite the following paper:
Grønning AGB & Doktor TK, et al. DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning. Nucleic Acids Res. 2020;48(13):7099-7118.
doi:10.1093/nar/gkaa530

Disclaimer

While we provide pre-trained models and the ability to train your own, we take no responsibility for the results produced by this webserver.
Results are provided as is and with no guarantee of truth or validity.
Data uploaded to the server is stored securely and linked to a unique and anonymous job id. Anyone in possession of the job id may access the data uploaded.
Data on the server may be deleted without further notice and you should not rely on availability of models, including both pre-trained and user-trained models.

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