Publications by Year: 2026

Somatic variants are increasingly recognized as contributors to diverse non-cancer, developmental, and aging-related disorders. However, most tools for detecting somatic single-nucleotide variants (sSNVs) were designed for DNA sequencing and primarily tailored to cancer datasets, leaving a critical gap in harnessing the rich potential of RNA-seq for sSNV identification, particularly in non-cancer tissues with low mutation rates. Here, we introduce RNA-MosaicHunter, a novel bioinformatic tool for accurate sSNV detection from bulk RNA-seq. In two benchmarking datasets, it demonstrated high precision (94.7% in TCGA and 99.3% in a cell-line mixture) with sensitivities of 53.4% and 38.9%, respectively, in the default mode that maximizes precision. We then applied RNA-MosaicHunter to profile 827 RNA-seq samples in three tissue types from the Genotype Tissue Expression project (GTEx), where it outperformed previous methods in capturing mutational characteristics associated with normal aging. We further utilized RNA-MosaicHunter to analyze RNA-seq data from 382 Alzheimer’s disease (AD) brain samples and 480 age-matched controls and revealed a significantly higher burden of sSNVs in AD cerebral cortex, suggesting the potential contribution of sSNVs to AD pathogenesis. RNA-MosaicHunter enables accurate profiling and characterization of sSNVs from RNA-seq data, advancing the understanding of the role of somatic variants across diverse tissues and diseases.

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on Major Histocompatibility Complex class I (MHC-I) molecules, collectively termed peptide-MHC complexes (pMHC), enabling detection of foreign or mutated proteins. With the rising importance of immunotherapies targeting neoantigens in cancers, the ability to accurately predict which peptides will bind to the diverse population of MHC alleles is critically important. Current computational methods for pMHC-I prediction fall broadly into sequence-based methods, which rely heavily on large training datasets, and structure-based methods that leverage structural modeling and energetics of pMHC binding. While sequence-based methods have been popularly used, their performance is dependent on the size and quality of training data. On the other hands, while structure-based approaches can generalize better across diverse MHC alleles, they traditionally depend on identifying a single global minimum energy conformation, an assumption that often fails due to the inherent binding promiscuity of MHC-I molecules. To address these limitations, we developed a STRUMP-I (STRUcture-based pMHC Prediction (for class I)), a novel pMHC binding prediction tool that directly leverages a broad set of force-field-derived energy terms as machine-learning features. STRUMP-I achieves performance comparable to state-of-the-art sequence-based models while significantly outperforming them on MHC alleles with limited representation in training data. Furthermore, STRUMP-I demonstrates strong synergy when integrated with sequence-based methods, notably enhancing prediction precision. The robustness and generalizability of STRUMP-I were confirmed by evaluating its predictive performance on independent, previously unseen datasets, including an experimentally validated cancer neoantigen dataset. This combined approach advances our capability to reliably identify clinically relevant neoantigen targets. The source code and trained models are available at https://github.com/yoonjoolab/STRUMP-I.

Background Changes in RNA splicing over the course of evolution have profoundly diversified the functional landscape of the human genome. While DNA sequences proximal to intron-exon junctions are known to be critical for RNA splicing, the impact of distal intronic sequences remains underexplored. Emerging evidence suggests that inverted pairs of intronic Alu elements can promote exon skipping by forming RNA stem-loop structures. However, their prevalence and influence throughout evolution remain unknown.

Results Here, we present a systematic analysis of inverted Alu pairs across the human genome to assess their impact on exon skipping through predicted RNA stem-loop formation and their relevance to hominoid evolution. We found that inverted Alu pairs, particularly pairs of AluY-AluSx1 and AluSz-AluSx, are enriched in the flanking regions of skippable exons genome-wide and are predicted to form stable stem-loop structures. Exons defined by weak 3′ acceptor and strong 5′ donor splice sites appear especially prone to this skipping mechanism. Through comparative genome analysis across nine primate species, we identified 67,126 hominoid-specific Alu insertions, primarily from AluY and AluS subfamilies, which form inverted pairs enriched across skippable exons in genes of ubiquitination-related pathways. Experimental validation of exon skipping among several hominoid-specific inverted Alu pairs further reinforced their potential evolutionary significance.

Conclusion This work extends our current knowledge of the roles of RNA secondary structure formed by inverted Alu pairs and details a newly emerging mechanism through which transposable elements have contributed to genomic innovation across hominoid evolution at the transcriptomic level.

Alzheimer's disease (AD) is an age-associated neurodegenerative disorder characterized by progressive neuronal loss and pathological accumulation of the misfolded proteins amyloid-β and tau1,2. Neuroinflammation mediated by microglia and brain-resident macrophages plays a crucial role in AD pathogenesis1-5, though the mechanisms by which age, genes, and other risk factors interact remain largely unknown. Somatic mutations accumulate with age and lead to clonal expansion of many cell types, contributing to cancer and many non-cancer diseases6,7. Here we studied somatic mutation in normal aged and AD brains by three orthogonal methods and in three independent AD cohorts. Analysis of bulk RNA sequencing data from 866 samples from different brain regions revealed significantly higher ( two-fold) overall burdens of somatic single-nucleotide variants (sSNVs) in AD brains compared to age-matched controls. Molecular-barcoded deep (>1000X) gene panel sequencing of 311 prefrontal cortex samples showed enrichment of sSNVs and somatic insertions and deletions (sIndels) in cancer driver genes in AD brain compared to control, with recurrent, and often multiple, mutations in genes implicated in clonal hematopoiesis (CH)8,9. Pathogenic sSNVs were enriched in CSF1R+ microglia of AD brains, and the high proportion of microglia (up to 40%) carrying some sSNVs in cancer driver genes suggests mutation-driven microglial clonal expansion (MiCE). Analysis of single-nucleus RNA sequencing (snRNAseq) from temporal neocortex of 62 additional AD cases and controls exhibited nominally increased mosaic chromosomal alterations (mCAs) associated with CH10,11. Microglia carrying mCA showed upregulated pro-inflammatory genes, resembling the transcriptomic features of disease-associated microglia (DAM) in AD. Our results suggest that somatic driver mutations in microglia are common with normal aging but further enriched in AD brain, driving MiCE with inflammatory and DAM signatures. Our findings provide the first insights into microglial clonal dynamics in AD and identify potential new approaches to AD diagnosis and therapy.