meRanTK

meRanTK performs computational analysis of high-throughput RNA cytosine methylation data to detect and quantify posttranscriptional cytosine methylation in poly(A) RNA transcriptomes.

Key Features:

• Specialized RNA methylation analysis: Supports analysis of RNA-BSseq and Aza-IP datasets for transcriptome-wide cytosine methylation profiling.
• Splice-aware bisulfite read mapping: Maps bisulfite-treated sequencing reads with splice-aware alignment to accommodate transcript structures.
• Methylation calling at single-base resolution: Calls methylated cytosines across the transcriptome at single-base pair resolution.
• Differential methylation statistics: Implements statistical methods to identify differentially methylated cytosines for single- and multi-replicate experiments.
• Standard-compliant output formats: Produces results in standard-compliant formats for integration with downstream workflows.

Scientific Applications:

• Transcriptome-wide cytosine methylation profiling: Enables detection and quantification of posttranscriptional cytosine methylation in poly(A) RNA.
• Functional studies of RNA modifications: Supports investigation of the regulatory roles of RNA cytosine methylation in gene expression and disease contexts.
• Experimental dataset analysis: Processes RNA-BSseq and Aza-IP experimental data for methylation mapping and comparison across conditions.

Methodology:

Performs splice-aware mapping of bisulfite-treated sequencing reads, methylation calling at single-base pair resolution, and statistical identification of differentially methylated cytosines for single- and multi-replicate experiments.