RASP v2.0: an updated atlas for RNA structure probing data

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• RNA molecules are crucial for biological processes, performing functions through complex structures.
• Understanding RNA structure is key to deciphering its biological roles.

Purpose of the Study:

• To present RASP v2.0, an updated database for RNA structure probing data.
• To enhance online structural analysis functionalities for RNA studies.

Main Methods:

• Expanded the RNA structure dataset from 156 to 438 entries, including transcriptome-wide, target-specific, and RNA-RNA interaction datasets.
• Implemented a deep learning model to impute missing structural signals in 59 datasets, improving data quality for low-abundance RNAs.
• Deployed three new online analysis modules: missing structure score imputation, RNA secondary/tertiary structure prediction, and RNA binding protein (RBP) binding prediction.

Main Results:

• RASP v2.0 now contains 438 RNA structure datasets, covering 24 species.
• Deep learning imputation significantly improved data quality for low-coverage transcriptome-wide datasets.
• New analysis modules offer advanced capabilities for RNA structure research.

Conclusions:

• RASP v2.0 provides a more comprehensive resource for RNA structure data.
• The enhanced functionalities facilitate the exploration of RNA structure-function relationships.
• RASP v2.0 is a valuable tool for researchers studying diverse biological processes involving RNA.