snoGPS

snoGPS identifies H/ACA small nucleolar RNA (snoRNA) genes within genomic sequences and predicts their pseudouridine-guiding interactions in ribosomal RNA (rRNA).

Key Features:

• Deterministic Screening Algorithm: snoGPS employs a deterministic screening algorithm to search genomic sequences for potential H/ACA snoRNA candidates.
• Probabilistic Scoring Model: The tool integrates a probabilistic scoring model to rank and score candidate H/ACA snoRNAs.
• Prediction of snoRNA–rRNA Base-pairings: snoGPS predicts base-pairing interactions between snoRNAs and rRNA to identify candidate pseudouridylation guides.
• Experimental Verification in Saccharomyces cerevisiae: In S. cerevisiae, snoGPS identified six novel RNA transcripts as candidate snoRNAs (five verified to guide pseudouridine formation), and predicted 14 new snoRNA–rRNA base-pairings of which 12 were confirmed by gene disruption experiments.
• Structural-Feature Focus: The method explicitly targets unique structural features of H/ACA snoRNAs that distinguish them from protein-coding genes and tRNAs.

Scientific Applications:

• Pseudouridylation Mapping: snoGPS has been used to link 41 of the 44 known pseudouridine modifications in S. cerevisiae rRNA to verified snoRNAs.
• Novel snoRNA Discovery: The tool provides candidate H/ACA snoRNAs for discovery and validation, expanding the catalog of guides that direct rRNA pseudouridylation.

Methodology:

Application of a deterministic screening algorithm combined with a probabilistic scoring model to detect genomic regions with the structural features characteristic of H/ACA snoRNAs.