Welcome to RONN v3.2!

Please note that this version is not actively maintained and will be superseded by a brand-new predictor soon.
Therefore, bugs and string handling have not been fixed in this version and so, please follow these key instructions to obtain a proper prediction:

1) Please provide only one input sequence!
2) Please use a FASTA-header. After the header, insert a newline ('Return' key).
3) The rest of the sequence should not have any newline characters.

RONN will try its best to handle inconsistencies in the input, but I cannot guarantee a valid prediction in these cases. Furthermore, it is probably better to wait for the new predictor if you need multiple sequences handled quickly. Alternatively, please email us using the link below if you need large-scale predictions done with the new algorithm.

On that note, RONN is much slower than the upcoming predictor, so this version is mainly for posterity without any optimisations or improvements (save for a new data set that was curated in 2012, which provides a modest improvement in prediction).

You should see the two-column output below the text box after clicking "Send Sequence", which can easily be copy-pasted into a text document for further analysis/graphing.

The graph that is produced as part of the output can be zoomed and exported using the little drop-down button at the top right of the graph, but please note that currently, the various image download options require contacting an external server. The safest option if you do not want to transmit your input sequence, therefore, is to print to PDF. This option will also maintain your zoom settings.

We are slowly rolling out our own image export server which should be available soon.

As always, please email us with any questions or comments, or if you need standalone GNU/Linux binaries for your local setup.

Finally, if you used RONN, please cite:
Yang, ZR, Thomson, R, McNeil, P, and Esnouf, RM (2005). RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. (Link to full text)