Rmats Workflow

The workflow combines exon-junction reads with exon-body reads to estimate how strongly a splicing event is included in each sample. Junction evidence captures reads spanning transcript-processing boundaries, while exon-body counts contribute additional quantitative support. These measurements allow inclusion levels to be compared between biological conditions rather than evaluating transcript changes from a single read category alone.

Biological replicates provide multiple measurements of splicing within each condition, allowing the model to account for variation among samples when comparing groups. This is important because an apparent difference in exon inclusion may reflect sample-to-sample variation rather than a condition-associated change. Replicate-aware analysis therefore strengthens interpretation of differential splicing results.

The workflow evaluates five major classes of alternative-splicing events, including exon skipping and alternative splice-site selection. Considering multiple event classes broadens the analysis beyond one transcript feature and helps reveal different ways that RNA processing changes between conditions. The resulting event-level comparisons can identify distinct patterns of condition-specific transcript regulation.

Inclusion levels summarize the relative representation of a splicing event in the analyzed RNA-sequencing data. The workflow compares these measurements across biological conditions using the event-specific read evidence and replicate structure. Differences in the resulting values indicate that transcript processing may change between groups, providing an event-level view of differential alternative splicing.

The workflow is designed for replicate RNA-sequencing data organized into biological conditions for comparison. Its analysis uses exon-junction and exon-body read counts derived from those samples, then evaluates splicing events across the groups. A practical workflow therefore begins with replicate sequencing datasets representing the conditions whose transcript-processing patterns are being studied.

Results can identify alternative-splicing events whose inclusion levels differ between biological conditions and can describe the event class associated with each change. These findings provide candidate condition-specific isoform changes and regulatory events for further investigation. Researchers can use the event-level output to prioritize transcript-processing differences for biological interpretation or mechanistic follow-up.

The workflow is useful when researchers need to examine how transcript processing changes during development, disease, or cellular responses. By identifying condition-specific splicing and candidate regulatory events, it connects RNA-sequencing comparisons with questions about gene regulation and transcriptome diversity. Its findings can guide later mechanistic studies of how particular processing changes affect biology.