RNA degradation fundamentally challenges transcriptomic accuracy, yet current methods rely on simplistic abundance-based read allocation that perpetuates quantification biases. We present INDEGRA (Integrity and DEGradation of RNA Analysis), a revolutionary approach that leverages RNA instability as a feature rather than a limitation to achieve unprecedented accuracy in transcript quantification.
INDEGRA introduces three paradigm-shifting innovations. First, we model RNA degradation as a Bernoulli fragmentation process, enabling precise calculation of per-transcript degradation rates from long-read data. This creates the Direct Transcript Integrity (DTI) metric—a universal, isoform-resolved measure superior to RIN that works for any RNA type, including samples lacking ribosomal RNA. Second, we implement degradation profile-based read reallocation, where ambiguously-mapped reads are assigned based on which allocation best improves degradation model fit, not mere abundance. This fundamentally different approach achieves the highest accuracy in read allocation reported to date, applicable to both direct RNA and cDNA sequencing. Third, we employ Bayesian deconvolution to separate biological degradation from technical handling bias, revealing true in vivo RNA stability even across samples of vastly different quality.
Our rigorous validation using synthetic spike-ins with known ground truth demonstrates INDEGRA's superior performance: removing up to 60% of false positive differential expression hits while maintaining sensitivity, dramatically outperforming existing tools. In real datasets spanning multiple species and tissues, INDEGRA corrected hundreds of erroneous differential expression calls, substantially altering gene ontology outcomes. Critically, by removing technical degradation bias, we uncovered conserved and tissue-specific RNA stability patterns across evolution, identifying transcripts with invariant stability and others with context-dependent turnover.
INDEGRA represents a fundamental advance in RNA quantification methodology. By transforming degradation from a confounding factor into an informative signal for accurate read allocation, we enable truly unbiased transcriptomic comparisons across any samples regardless of quality differences. Our high-performance, open-source implementation seamlessly integrates with standard workflows, providing researchers immediate access to dramatically improved quantification accuracy. This work establishes degradation-aware analysis as essential for modern transcriptomics, with immediate applications spanning basic biology to clinical diagnostics where sample quality varies.