SPOT-RNA-2D: Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred base-pairing and evolutionary-derived coupling.

Our resources are limited. If you wish to run several batches per day please make use of the downloadable package or contact the administrator directly.

Submit

E-mail address (required):
Target (optional):
Input your RNA Sequences: Maximum: 500 nts for SPOT-RNA-2D and 10,000 for SPOT-RNA-2D-Single, Only one RNA sequence at a time
Predictor:

Example outputs:

Example-1: 3NPN (Chain-A)      Example-2: 6P2H (Chain-A)

Standalone Program to Run Locally:

SPOT-RNA-2D-local

Datasets:

Sequences      Secondary structure labels      Distance-based contact-labels      Predictions

Reference:

Jaswinder Singh, Kuldip Paliwal, Thomas Litfin, Jaspreet Singh, and Yaoqi Zhou. “Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.” under-review (2022).