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 one sequencing technology, miniQuant intelligently combines the strengths of short reads, which offer depth and coverage, and long reads, which provide better isoform resolution due to their ability to span entire transcripts.

MiniQuant-H improves the accurate quantification of gene isoform

a, Schematic illustration of miniQuant to estimate gene isoform abundance. Left, miniQuant leverages the strengths of long and short reads. Middle, miniQuant-L and miniQuant-H are developed for two data scenarios. The miniQuant-H integrates the likelihood of both long and short reads with gene- and data-specific weight α_c, which is determined by a machine learning model with data and gene structure features (for example, K-value) as input. Right, gene isoform abundances estimated from the hybrid likelihood using the EM algorithm. b, Comparison of the median MARD by miniQuant-H with uniform (from 0 to 1, purple) and gene-specific (red) weight across combinations of short-read and long-read sequencing depths (n = 9 combinations) under GENCODE annotation. c, Lines represent median MARD by miniQuant-H with increasing short-read sequencing depths under GENCODE annotation. Long reads across three sequencing depths with three protocols (cDNA-PacBio, cDNA-ONT and dRNA-ONT) are used. Red arrows, recommended combinations of sequencing depths. d, Comparison of the median MARD by five short-read tools, eight long-read tools and miniQuant-H across three short-read and three long-read sequencing depths under GENCODE annotation. e, Barplot showing the mean MARD of ERCC (left) and SIRV (right) spike-in transcripts of SIRV-set4 from the LRGASP consortium (n = 6 biological replicates). Bars within the dashed line indicate the de novo sample-specific annotation identified by Cufflinks and StringTie. Asterisk, technological issues specified in Supplementary Note 6. f, Dot chart represents the median MARD by miniQuant-L and miniQuant-H with different expression levels under GENCODE (left) and sample-specific (right) annotations. g, Dot chart showing the median MARD by short-read-based (left), long-read-based (right) and miniQuant-H tools within different K-value groups. Asterisk, LIQA does not quantify genes with single isoform. LR, long reads; SR, short-read pairs (2 × 150 bp). M, million.

One of the key innovations of miniQuant is its gene- and dataset-specific approach. Not all genes benefit equally from long-read data, and sequencing resources are often limited. miniQuant analyzes each gene to determine where quantification is most prone to error and where long reads can offer the most value. It then combines the two data types in an optimal way, producing more reliable and accurate isoform measurements.

The team validated their approach with rigorous testing, including mathematical proofs, simulations, and experimental validation. They applied miniQuant to more than 17,000 public RNA sequencing datasets from major consortia such as GTEx (Genotype-Tissue Expression), TCGA (The Cancer Genome Atlas), and ENCODE (Encyclopedia of DNA Elements), demonstrating its broad applicability across biological systems.

Importantly, miniQuant also revealed previously undetectable isoform switches during the differentiation of human embryonic stem cells into pharyngeal endoderm and primordial germ cell-like cells. These insights could open new doors in developmental biology and regenerative medicine, offering a better understanding of how gene expression shifts at critical stages of cell fate decisions.

In summary, miniQuant provides a powerful new tool for transcriptome analysis by making RNA sequencing data more accurate and informative. It helps overcome one of the long-standing limitations in isoform quantification, making it especially valuable for researchers working with complex gene structures or studying developmental and disease processes.

Availability – MiniQuant is available at https://github.com/Augroup/miniQuant.