RIPper

RIPper quantifies and maps Repeat-Induced Point (RIP) mutations in fungal genomes, particularly Ascomycota, to assess RIP pathway activity and its effects on genome composition.

Key Features:

• Genome-wide detection and quantification: Automates detection and quantification of RIP mutations across whole genome assemblies.
• Sliding window analysis: Uses a sliding window approach for comprehensive, genome-wide scanning of RIP signals.
• Cytosine-to-thymine transition detection: Identifies the cytosine-to-thymine transition mutations characteristic of the RIP pathway.
• Region identification: Identifies genome regions with high frequencies of RIP, including gene regions and transposable elements.
• Fine-scale analysis and visualization: Enables fine-scale analysis and visualization of changes in RIP mutations across sequences of interest.
• RIP statistics generation: Produces comprehensive RIP statistics for entire genomes, including the proportion of the genome affected by RIP.
• Contextual impact assessment: Provides insights into occurrence, genetic context, and frequency of RIP mutations and associated reductions in GC content.
• Demonstrated on Neurospora crassa: Methodology and outputs have been demonstrated using the Neurospora crassa genome assembly.

Scientific Applications:

• Assessing RIP pathway activity: Quantifies RIP occurrence to evaluate activity of the fungal RIP defense mechanism in genomic regions.
• Genome composition analysis: Measures RIP-associated changes such as reduced GC content to study impacts on genome composition.
• Transposable element and gene targeting studies: Identifies transposable elements and gene regions targeted by RIP for evolutionary and functional analyses.
• Comparative genomics of fungi: Enables comparison of RIP burdens across fungal genomes, particularly within Ascomycota.
• Quantifying genome-wide RIP burden: Reports the proportion of the genome affected by RIP to support population- and species-level investigations.
• Validation on model genomes: Validates RIP detection approaches using assemblies such as Neurospora crassa.

Methodology:

Performs genome-wide and fine-scale analysis using a sliding window approach to detect and quantify cytosine-to-thymine transitions characteristic of RIP and to compute comprehensive RIP statistics.