RNA sequencing has revolutionized the way scientists study gene expression, helping researchers identify not just which genes are active but also which isoforms, alternative versions of RNA produced from the same gene, are being expressed. This is especially important because different isoforms can have very different roles in health and disease. However, accurately quantifying these isoforms has remained a challenge, particularly when relying on short-read sequencing. Short-read RNA sequencing technologies are widely used due to their high throughput and relatively low cost. But these reads are often too short to uniquely identify and assemble the full structure of complex isoforms, especially in genes with multiple exons or alternative splicing events. This can lead to quantification errors and misinterpretation of results. To address this issue, researchers from the University of Michigan have developed miniQuant, a computational method that significantly improves the accuracy of isoform quantification. Instead of relying solely on ...