ToNER: A tool for identifying nucleotide enrichment signals in feature-enriched RNA-seq data.

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Existing biochemical methods enrich 5' ends of RNAs for prokaryotic RNA-seq protocols like dRNA-seq and Cappable-seq.
• Computational tools are required to statistically analyze enrichment data and accurately locate RNA 5' ends.
• Current statistical methods for dRNA-seq may not be optimal for Cappable-seq data due to differences in enrichment efficiency and data distribution.

Purpose of the Study:

• To develop a computational tool for statistical modeling of RNA 5' end enrichment from RNA-seq data.
• To accurately identify transcript 5' ends from Cappable-seq and dRNA-seq data.
• To compare the performance of the new tool against existing methods for transcript 5' end identification.

Main Methods:

• Developed the Transformation of Nucleotide Enrichment Ratios (ToNER) tool.
• ToNER employs statistical modeling, calculates nucleotide enrichment scores, and uses the Box-Cox procedure for normal distribution fitting.
• Utilizes meta-analysis across experimental replicates to enhance detection power and compares results with the TSSAR tool.

Main Results:

• ToNER successfully identifies transcript 5' ends from both Cappable-seq and dRNA-seq data.
• When applied to Cappable-seq replicates, ToNER detects more known transcript 5' ends compared to the TSSAR tool.
• ToNER identifies transcript 5' ends in regions that are not locally modeled by TSSAR, indicating improved sensitivity.

Conclusions:

• ToNER offers a robust statistical approach for identifying RNA 5' ends, particularly from Cappable-seq data and when integrating replicate information.
• The tool demonstrates potential for analyzing other RNA-seq datasets involving enrichment for different RNA structural features.
• ToNER is freely available for download, promoting its use in the research community.