GraphClust2

GraphClust2 clusters RNA sequences by combining nucleotide sequence and secondary-structure information to identify and annotate structured RNAs, conserved local structures in long non-coding RNAs, and RNA motifs from high-throughput sequencing and structure-probing datasets.

Key Features:

• Scalable Clustering: Handles large RNA datasets by clustering based on similarities in nucleotide sequence and secondary structure.
• Integration with Galaxy Framework: Implemented within the Galaxy framework to enable incorporation into computational workflows.
• Incorporation of Diverse Data Types: Integrates experimental and genomic data, including structure-probing datasets, into clustering analyses.
• Enhanced Annotation Performance: Clusters functional RNAs and identifies locally conserved structural candidates in long non-coding RNAs, highlighting phylogenetically conserved local structures.
• Bias Mitigation in Motif Discovery: Applies clustering to cross-linking immunoprecipitation experiments to mitigate biological and methodological biases during motif discovery.
• Identification of Functional Targets: Detects RNA targets bound by RNA-binding proteins such as Roquin-1, including conserved stem-loops within the 3' untranslated region of BCOR.

Scientific Applications:

• Structural RNA clustering and annotation: Supports annotation and discovery of structured RNAs using combined sequence and secondary-structure information from high-throughput sequencing and structure-probing data.
• Long non-coding RNA analysis: Identifies locally conserved structural candidates and phylogenetically conserved local structures in long non-coding RNAs.
• Motif discovery from CLIP data: Improves motif discovery from cross-linking immunoprecipitation experiments by reducing biological and methodological biases.
• RNA-binding protein target identification: Reveals functional RNA targets of proteins such as Roquin-1, exemplified by conserved stem-loops in the BCOR 3' untranslated region.

Methodology:

Clustering of RNAs based on nucleotide sequence and secondary-structure similarities; integration of structure-probing datasets and clustering of cross-linking immunoprecipitation experiments; identification of locally conserved structural candidates and assessment of phylogenetic conservation; implemented within the Galaxy framework.