Biomedical subjects

Minji Kim

Publications and source records attributed to Minji Kim.

4 recordsLinked to original sources

PubMed2026

Dietary Laver Intake and Risk of Incident Abdominal Obesity Among Korean Adults: An 18-Year Prospective Cohort Study.

Background/Objectives: Abdominal obesity is a major risk factor for metabolic disorders, including type 2 diabetes and cardiovascular disease. Laver (Porphyra spp.), a seaweed commonly consumed in Korea, contains dietary fiber and bioactive compounds that may help reduce abdominal fat accumulation. This study aimed to investigate the association between laver intake and the incidence of abdominal obesity in Korean adults. Methods: Data from 5457 adults aged 40-69 years who participated in the Korean Genome and Epidemiology Study were analyzed. Laver intake was assessed at baseline using a validated 103-item food frequency questionnaire. Abdominal obesity was defined as waist circumference of ≥90 cm in men and ≥85 cm in women. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: During 18 years of follow-up, 1202 men (39.4%) and 1117 women (46.4%) developed abdominal obesity. A higher laver intake was significantly associated with a lower risk of abdominal obesity in both sexes. Compared with participants in the lowest category of laver intake, those in the highest category had a 28% lower risk among men (HR: 0.72; 95% CI: 0.58-0.89; p for trend = 0.0099) and a 33% lower risk among women (HR: 0.67; 95% CI: 0.54-0.84; p for trend = 0.0005). These associations remained significant after additional adjustment for overall diet quality. Conclusions: Higher habitual laver intake was associated with a lower risk of incident abdominal obesity in Korean adults. Further prospective and intervention studies are needed to confirm these findings.

Humans↗

PubMed2025

Chrom-Sig: de-noising 1D genomic profiles by signal processing methods.

MOTIVATION: Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq, CUT&Tag, and CUT&RUN that generate coverage files for transcription factor binding, as well as ATAC-seq that yield coverage files for chromatin accessibility. Due to the inherent technical noise present in the experimental protocols, researchers need statistically rigorous and computationally efficient methods to extract true biological signal from a mixture of signal and noise. However, existing approaches are often computationally demanding or require input or spike-in controls. RESULTS: We developed Chrom-Sig, a Python package to quickly de-noise 1D genomic coverage tracks by computing the empirical null distribution without prior assumptions or experimental controls. When tested on 19 ChIP-seq, CUT&RUN, ATAC-seq, and snATAC-seq datasets, Chrom-Sig can effectively decompose the data into signal and noise components. Notably, Chrom-Sig performs de-noising and peak calling in 1-2 h using around 20 GB of memory. The de-noised signal corroborates with biologically meaningful results: CTCF CUT&RUN data retained a high percentage of peaks overlapping CTCF binding motifs, while ATAC-seq and RNA Polymerase II data were enriched in enhancers and promoters. We envision Chrom-Sig to be a versatile and general tool for current and future genomic technologies. AVAILABILITY AND IMPLEMENTATION: Chrom-Sig is publicly available on GitHub (https://github.com/minjikimlab/chromsig) and Zenodo (doi: 10.5281/zenodo.17488772) under the MIT licence.

Genomics↗

PubMed2025

Designing smart spatial omics experiments with S2Omics.

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

digital pathology↗

PubMed2025

MIA-Jet: Multi-scale Identification Algorithm of Chromatin Jets.

The mammalian genome is organized into large-scale chromosome territories, compartments, domains, and at the smallest scale, chromatin loops and stripes. The newest element is a chromatin jet, a diffused line perpendicular to the main diagonal in the Hi-C contact map, which was reported in quiescent mammalian lymphocytes supporting a two-sided symmetric cohesin loop extrusion model. A similar structure is observed in Repli-HiC data, where relatively thin and straight chromatin fountains indicate coupling of DNA replication forks. However, the precise biological implications of these jet-like structures are unknown due to the limitations in computational methods. We developed MIA-Jet, a multi-scale ridge detection algorithm that can accurately detect jets of variable lengths, widths, and angles. When tested on Hi-C, Repli-HiC, ChIA-PET, ChIA-Drop, and Micro-C data in mouse, human, roundworm, and zebrafish cells, MIA-Jet outperformed existing methods. In human cells, jets were enriched in cohesin loading sites and early replication initiation zones. Applying MIA-Jet to Hi-C data generated from protein-degraded cells revealed that jets are dependent on cohesin but not YY1, and jet signals are strengthened after depleting WAPL. We envision MIA-Jet to be broadly applicable to any 3D genome mapping data, thereby providing new insights into the functional roles of chromatin jets.

3D genome mapping↗