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Van Deynze, K.

Publications and source records attributed to Van Deynze, K..

2 recordsLinked to original sources

A personalized multi-platform assessment of somatic mosaicism in the human frontal cortex

Somatic mutations in individual cells create genomic mosaicism, influencing genetic disorders and cancers. While clonal mutations in cancers are well-studied, rarer somatic variants in normal tissues remain poorly characterized. This study systematically evaluates detection methods using a personalized donor-specific assembly (DSA) from a neurotypical individuals dorsolateral prefrontal cortex assessed with Oxford Nanopore, NovaSeq, linked-read sequencing, Cas9-targeted long-read sequencing (TEnCATS), and single-neuron MALBAC amplification. The haplotype-resolved DSA improved cross-platform analysis, dramatically increasing phasing rates. Germline SNVs, structural variations (SVs), and transposable elements (TEs) were recalled with 99.4%-99.7% accuracy in bulk tissue, and phased haplotype analysis reduced false positives by 15.4%-75.1% for putative somatic candidates. Long-read single-neuron sequencing detected nine somatic SV candidates, demonstrating enhanced sensitivity for rare variants, while TEnCATS identified eight low-frequency somatic TE candidates. These findings highlight advanced methodologies for precise somatic variant detection, critical for understanding mosaicisms role in health and disease.

genomics↗

HiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

We present HiC-DC+, a software tool for Hi-C/Hi-ChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. HiC-DC+ integrates with popular preprocessing and visualization tools, includes TAD and A/B compartment callers, and outperformed existing tools in H3K27ac HiChIP benchmarking as validated by CRISPRi-FlowFISH. Differential HiC-DC+ analysis recovered global principles of 3D organization during cohesin perturbation and differentiation, including TAD aggregation, enhancer hubs, and promoter-enhancer loop dynamics.

bioinformatics↗