As RNA sequencing technology continues to advance, scientists are generating massive datasets that capture how genes are expressed in different tissues and disease conditions. However, these datasets often include variables such as age, sex, ethnicity, and clinical history, all of which can influence results and make data interpretation challenging.

Researchers from Johns Hopkins University have developed advanced tools to address this problem. Their programs, MntJULiP and Jutils, are designed to detect and visualize RNA splicing differences in data that contain multiple confounding factors. Splicing refers to the way cells edit RNA transcripts before they are translated into proteins, and small changes in this process can have major biological consequences.

MntJULiP uses a sophisticated Bayesian model that adjusts for covariates, meaning it can separate the effects of variables like age and sex from true biological differences in splicing. This adjustment greatly reduces false positives and improves accuracy compared to other methods. Meanwhile, Jutils allows scientists to easily visualize the data through heatmaps, Venn diagrams, and principal component analysis (PCA) maps.

Differential splicing analyses of GTEx brain RNA-seq data with MntJULiP and Jutils

Jutils heatmaps of DSR events from the comparison between the frontal cortex female (“F”) and male (“M”) sample groups, without (left) and with (right) “age at death” as covariate. PSI intron values estimated with MntJULiP and uploaded into Jutils were plotted, with rows (events) and columns (samples) clustered using the “weighted” method and “cityblock” similarity metric. The covariate-adjusted heatmap shows a distinct supercluster marked with boxes.

The team demonstrated the power of their tools by analyzing RNA sequencing data from the GTEx brain tissue collection. Their findings showed that as age differences increase, so do the variations in RNA splicing patterns in the brain’s frontal cortex. They also identified a subgroup of individuals showing unique splicing behaviors across different ages, suggesting that aging may drive specific molecular changes in brain function.

Together, MntJULiP and Jutils represent a major step forward in RNA analysis, offering researchers greater precision and deeper insight into how genetic regulation changes with age and other biological factors.

Availability – The tools MntJULiP and Jutils are available from https://github.com/splicebox/MntJULiP and https://github.com/splicebox/Jutils, respectively.