sRNAfrag

sRNAfrag quantifies and analyzes fragmentation patterns of small RNAs to detect, characterize, and compare RNA fragments such as snoRNAs and mature microRNAs from sequencing data.

Key Features:

• Standardization and Interoperability: Provides a standardized approach for small RNA fragmentation quantification and integrates with tools such as ViennaRNA.
• Relational Database Outputs: Generates relational database tables capturing multi-mapping, fragment counts, and fragment stability metrics across cell types.
• Identification of Established Loci: Identifies established loci of mature microRNAs using sequencing data alone.
• Visualization and Sequence Alignment: Employs visualization techniques akin to multi-sequence alignments to rediscover 5' seed sequences of small RNAs.
• Evolutionary Analysis: Detects snoRNA fragment conservation events across species, reporting 1411 conserved events observed in two of four eukaryotic species.
• Novel Loci-Level Variance-Score: Computes a loci-level variance-score to quantify noise around sequencing peaks and, when applied to snoRNAs with dimension reduction, distinguishes breast cancer and neuroblastoma cell lines.

Scientific Applications:

• Fragmentation Profiling: Standardizes and quantifies small RNA fragmentation patterns across biotypes to compare fragment stability and abundance.
• Locus Discovery and Annotation: Detects and annotates mature microRNA loci from sequencing data.
• Evolutionary Conservation Studies: Enables identification of conserved snoRNA fragment motifs and fragmentation patterns across eukaryotic species (1411 events reported).
• Comparative Cell-Type Analysis: Compares multi-mapping and fragment stability across cell types to study cell-type-specific fragmentation.
• Biomarker and Classification Research: Uses loci-level variance-score and dimension reduction for distinguishing disease-related cell lines, exemplified by breast cancer versus neuroblastoma.

Methodology:

sRNAfrag processes sequencing data to generate relational database tables, integrates with ViennaRNA, applies visualization akin to multi-sequence alignments, computes a loci-level variance-score with subsequent dimension reduction, and analyzes database outputs to detect conservation events.