RSLpred 2.0

RSLpred-2.0 prediction server

Configure a rice localization run

Choose one input method, select the prediction levels and model strategy, then run the analysis. Accession sequences are fetched automatically at submission.

Step 1

Provide protein input

Choose one input route. Your loaded sequence remains editable before submission.

Up to 10,000 sequences; each header must begin with >.

Step 2

Prediction options

Select Level(s)
Select Model Strategy

Publication

Please cite RSLpred2

Duhan, N., & Kaundal, R. (2025). RSLpred2: An Integrated Web Server for the Annotation of Rice Proteome Subcellular Localization Using Deep Learning. Rice, 18, 58.DOI: 10.1186/s12284-025-00767-7