Probing-directed identification of novel structured RNAs

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Regulatory RNA elements control cellular processes and often depend on their structure.
• Current methods for predicting RNA structure primarily use DNA sequence analysis, thermodynamic stability, and evolutionary data.
• RNA probing experiments offer an alternative source of structural information by identifying accessible RNA positions.

Purpose of the Study:

• To investigate how RNA probing data can be integrated into RNA structure prediction algorithms.
• To enhance the RNASurface energy model with experimental probing data for improved structured RNA detection.
• To identify novel structured RNA elements, particularly non-coding RNAs (ncRNAs), in the human transcriptome.

Main Methods:

• Developed a general framework to integrate RNA probing data into the RNASurface energy model.
• Applied the enhanced RNASurface approach to genome-wide analysis of the human transcriptome.
• Utilized experimental data, specifically PARS (Poly(A)-selected RNA Structure) data, for analysis.

Main Results:

• The integration of experimental probing data significantly improves the discrimination of ncRNAs from other transcripts.
• Genome-wide analysis using the enhanced RNASurface identified previously undetectable structured RNA segments.
• Some of the newly identified segments show evidence of biological functionality.

Conclusions:

• RNA probing data can substantially contribute to the detection of structured RNAs.
• The RNASurface tool, augmented with probing data, is effective for discovering novel functional RNA elements.
• This approach advances the field of RNA structure prediction and functional genomics.