rifi

rifi analyses rifampicin time-series data from microarray or RNAseq to holistically identify transcription and decay associated processes in transcriptome data.

Key Features:

• Decay constant and delay fitting: Fits decay constants and the delay of the onset of decay for each probe or bin.
• Dynamic programming segmentation: Combines probes or bins of equal properties into segments by dynamic programming, independent of existing genome annotation.
• Transcript stability detection: Detects transcript segments of different stability or transcriptional events within one annotated gene.
• Processing site detection: Detects RNA processing sites from rifampicin time-series data.
• Transcription pausing site detection: Detects transcription pausing sites from rifampicin time-series data.
• Internal transcription start site detection: Detects internal transcription start sites in operons.
• Partial transcription termination detection: Detects sites of partial transcription termination in operons.
• Transcriptional interference identification: Identifies areas of likely transcriptional interference by the collision mechanism.
• Transcription velocity estimation: Provides an estimate of transcription velocity.
• Operon prediction: Integrates all data to give an estimate of continuous transcriptional units, i.e. operons.
• Comprehensive output generation: Produces comprehensive output tables and visualizations of the full genome result and individual fits for all probes or bins.

Scientific Applications:

• RNA half-life analysis: Performs classic decay constant and half-life analysis from rifampicin time-series transcriptome data.
• Operon structure elucidation: Estimates continuous transcriptional units to define operon structures without relying on existing genome annotation.
• Transcriptional event mapping: Maps transcriptional events including pausing, termination, and interference within and across annotated genes.

Methodology:

rifi fits decay constants and the delay of the onset of decay for each probe or bin, then combines probes or bins of equal properties into segments by dynamic programming independent of existing genome annotation.