Highly accurate Korean draft genomes reveal structural variation highlighting human telomere evolution.

Given the presence of highly repetitive genomic regions such as subtelomeric regions, understanding human genomic evolution remains challenging. Recently, long-read sequencing technology has facilitated the identification of complex genetic variants, including structural variants (SVs), at the single-nucleotide level. Here, we resolved SVs and their underlying DNA damage-repair mechanisms in subtelomeric regions, which are among the most uncharted genomic regions.

KTED: a comprehensive web-based database for transposable elements in the Korean genome.

Summary: Transposable elements (TEs), commonly referred to as "mobile elements," constitute DNA segments capable of relocating within a genome. Initially disregarded as "junk DNA" devoid of specific functionality, it has become evident that TEs have diverse influences on an organism's biology and health. The impact of these elements varies according to their location, classification, and their effects on specific genes or regulatory components.

Comparison of structural variant callers for massive whole-genome sequence data.

Background: Detecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals.

Exploring the DNA methylome of Korean patients with colorectal cancer consolidates the clinical implications of cancer-associated methylation markers.

Aberrant DNA methylation plays a critical role in the development and progression of colorectal cancer (CRC), which has high incidence and mortality rates in Korea. Various CRC-associated methylation markers for cancer diagnosis and prognosis have been developed; however, they have not been validated for Korean patients owing to the lack of comprehensive clinical and methylome data. Here, we obtained reliable methylation profiles for 228 tumor, 103 adjacent normal, and two unmatched normal colon tissues from Korean patients with CRC using an Illumina Infinium EPIC array; the data were corrected for biological and experiment biases.

Comprehensive profiling of DNA methylation in Korean patients with colorectal cancer.

Alterations in DNA methylation play an important pathophysiological role in the development and progression of colorectal cancer. We comprehensively profiled DNA methylation alterations in 165 Korean patients with colorectal cancer (CRC), and conducted an in-depth investigation of cancer-specific methylation patterns. Our analysis of the tumor samples revealed a significant presence of hypomethylated probes, primarily within the gene body regions; few hypermethylated sites were observed, which were mostly enriched in promoter-like and CpG island regions.

Deciphering the DNA methylation landscape of colorectal cancer in a Korean cohort.

Aberrant DNA methylation plays a pivotal role in the onset and progression of colorectal cancer (CRC), a disease with high incidence and mortality rates in Korea. Several CRC-associated diagnostic and prognostic methylation markers have been identified; however, due to a lack of comprehensive clinical and methylome data, these markers have not been validated in the Korean population. Therefore, in this study, we aimed to obtain the CRC methylation profile using 172 tumors and 128 adjacent normal colon tissues of Korean patients with CRC.

Epigenetic insights into colorectal cancer: comprehensive genome-wide DNA methylation profiling of 294 patients in Korea.

DNA methylation regulates gene expression and contributes to tumorigenesis in the early stages of cancer. In colorectal cancer (CRC), CpG island methylator phenotype (CIMP) is recognized as a distinct subset that is associated with specific molecular and clinical features. In this study, we investigated the genomewide DNA methylation patterns among patients with CRC.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi.

Prediction model construction of mouse stem cell pluripotency using CpG and non-CpG DNA methylation markers.

Background: Genome-wide studies of DNA methylation across the epigenetic landscape provide insights into the heterogeneity of pluripotent embryonic stem cells (ESCs). Differentiating into embryonic somatic and germ cells, ESCs exhibit varying degrees of pluripotency, and epigenetic changes occurring in this process have emerged as important factors explaining stem cell pluripotency.

Results: Here, using paired scBS-seq and scRNA-seq data of mice, we constructed a machine learning model that predicts degrees of pluripotency for mouse ESCs.

Development of Tissue-Specific Age Predictors Using DNA Methylation Data.

DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using methylation array data from normal samples.

Prognostic factor analysis for breast cancer using gene expression profiles.

Background: The survival of patients with breast cancer is highly sporadic, from a few months to more than 15 years. In recent studies, the gene expression profiling of tumors has been used as a promising means of predicting prognosis factors.

Methods: In this study, we used gene expression datasets of tumors to identify prognostic factors in breast cancer.