ToNER

ToNER models nucleotide enrichment ratios to identify RNA 5' ends from enriched and unenriched RNA-seq libraries such as differential RNA-seq (dRNA-seq) and Cappable-seq.

Key Features:

• Enrichment Score Calculation: Calculates nucleotide enrichment scores to distinguish enriched and unenriched regions within RNA-seq data.
• Statistical Transformation: Applies the Box-Cox procedure to transform enrichment ratios toward a normal distribution for downstream statistical assessment.
• Meta-Analysis Capability: Combines data across experimental replicates to increase detection power for enriched sites.
• Comparative Performance: Demonstrated detection of more known transcript 5' ends than the TSSAR tool in comparative analyses of Cappable-seq and dRNA-seq datasets for Escherichia coli when results are combined across replicates.
• Robust Statistical Modeling: Uses a global robust modeling approach effective in regions where local modeling by tools like TSSAR is insufficient.

Scientific Applications:

• Identification of RNA 5' ends from Cappable-seq: Detects RNA 5' ends in Cappable-seq datasets using enrichment-based statistical analysis.
• Analysis of enrichment-based RNA-seq datasets: Applies to dRNA-seq and other enrichment protocols to identify enriched transcript features and RNA structural 5' ends.

Methodology:

Calculates nucleotide enrichment scores from RNA-seq data, applies the Box-Cox procedure to transform enrichment ratios toward normality, and performs meta-analysis across replicates to identify significant enrichment sites corresponding to RNA 5' ends.