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Nucleic Acids Research Papers on Iberian Haplotypes, CellDART, Target-Directed MicroRNA Degradation

A team led by investigators in Spain presents a collection of structural variant haplotypes representing hundreds of individuals from the Iberian population. Using available short-read whole-genome sequence data for 785 participants in the Iberian GCAT "Genomes for Life" cohort, the researchers identified more than 30,300 single-nucleotide variants, some 5 million small insertions and deletions, and 89,178 structural variants spanning 50 or more nucleotide bases. With these data, they came up with a haplotype panel for imputing structural variants, along with smaller SNPs and indels — a resource used to search for associations in dozens of chronic conditions, uncovering a suspicious chromosome 3 Alu element SV in a rare neuromuscular condition. "Taken together," the authors write, "the availability of a high-quality haplotype panel, including a comprehensive fraction of structural variability, will significantly impact evolutionary and biomedical studies at different levels."

Seoul National University researchers describe a computational approach called CellDART, developed for teasing out cell type clues from spatial transcriptomic and single-cell RNA sequencing data. Along with analyses of data for cells in the prefrontal cortex of mouse and human brain samples, the team used the approach to retrace cell types in different parts of the human lung using available single-cell and spatial transcriptome data. "[T]he joint analysis of spatial and single-cell transcriptomic data elucidates the spatial cell composition and unveils the spatial heterogeneity of the cells," the authors report. "We utilized the proposed method to provide a resource for spatial mapping of the human lung cell atlas using the spatially resolved transcriptome of human lung tissue."

Finally, a team from Italy outlines a computational pipeline called TDMDfinder that was designed to detect target-directed microRNA degradation (TDMD) by RNA transcripts. The method "identifies 'high confidence' TDMD interactions in the human and mouse transcriptomes by combining sequence alignment and feature selection approaches," the investigators write. "Our predictions suggested that TDMD is widespread, with potentially every miRNA controlled by endogenous targets." After demonstrating that the approach could pick up experimentally verifiable TDMD interactions in mouse and human genomes, they used TDMDfinder to search for oncogenic TDMD events using multi-omic data generated across almost two dozen cancer types for the Cancer Genome Atlas project.

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