High-Accuracy RNA Integrity Definition for Unbiased Transcriptome Comparisons with INDEGRA
Alice CleyneniD, Agin RavindraniD, A.J. SethiiD, B. R. Prashantha KumariD, Tanya Javaid, Shafi MahmudiD, Katrina Woodward, Helaine Graziele Santos VieiraiD, Minna‐Liisa ÄnköiD, Robert J. WeatherittiD, Eduardo EyrasiD, Stephane S. RobiniD, Nikolay E. ShirokikhiD
Abstract
RNA sample integrity variability introduces biases and obscures natural RNA degradation, posing a significant challenge in transcriptomics. To address this, we developed the Direct Transcriptome Integrity (DTI) measure, a universal and robust RNA integrity metric based on nanopore sequencing. By accurately modeling RNA fragmentation, DTI provides a reliable assessment of sample quality. Integrated into the INDEGRA package (freely available at https://github.com/Arnaroo/INDEGRA), we provide tools to correct false discoveries and enable precise differential expression and RNA degradation analyses, even for challenging sample types. INDEGRA software can be used to accurately measure RNA DTI stability metric, isolate biological component of RNA degradation from technical biases, compare biological RNA stability transcriptome-wide and suppress false degradation-induced differential gene expression hits to allow broad comparisons across samples of different quality DTI offers a straightforward and accurate method for assessing RNA degradation, characterizing both overall sample integrity and transcript-specific degradation rates using direct RNA sequencing (DRS) data. Calculated through INDEGRA, DTI reveals inter- and intra-transcript variability in degradation, while INDEGRA separates RNA degradation from mapping inaccuracies, and connects degradation profiles to RNA fragmentation rates. By leveraging INDEGRA, researchers can minimize false differential transcript abundance findings caused by variations in overall sample integrity, while preserving genuine transcript-specific differences in stability and degradation. INDEGRA supports integration with widely used differential transcript abundance tools like DESeq2, limma-voom, and edgeR, enabling seamless analysis pipelines. INDEGRA enhances the accuracy and reliability of RNA quantification in high-throughput data and simplifies comparisons across diverse transcriptomic datasets, including those derived from different tissues, species, or experimental protocols.
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