Explore the vast range of titles available.

Background DNA methylation is an important epigenetic modification that is known to regulate gene expression. Whole-genome bisulfite sequencing (WGBS) is a powerful method for studying cytosine methylation in a whole genome. However, it is difficult to obtain methylation profiles using the WGBS raw reads and is necessary to be proficient in all types of bioinformatic tools for the study of DNA methylation. In addition, recent end-to-end pipelines for DNA methylation analyses are not sufficient for addressing those difficulties. Results Here we present msPIPE, a pipeline for DNA methylation analyses with WGBS data seamlessly connecting all the required tasks ranging from data pre-processing to multiple downstream DNA methylation analyses. The msPIPE can generate various methylation profiles to analyze methylation patterns in the given sample, including statistical summaries and methylation levels. Also, the methylation levels in the functional regions of a genome are computed with proper annotation. The results of methylation profiles, hypomethylation, and differential methylation analysis are plotted in publication-quality figures. The msPIPE can be easily and conveniently used with a Docker image, which includes all dependent packages and software related to DNA methylation analyses. Conclusion msPIPE is a new end-to-end pipeline designed for methylation calling, profiling, and various types of downstream DNA methylation analyses, leading to the creation of publication-quality figures. msPIPE allows researchers to process and analyze the WGBS data in an easy and convenient way. It is available at https://github.com/jkimlab/msPIPE and https://hub.docker.com/r/jkimlab/mspipe .

Background Many studies have been performed to identify various genomic loci and genes associated with the meat quality in pigs. However, the full genetic architecture of the trait still remains unclear in part because of the lack of accurate identification of related structural variations (SVs) which resulted from the shortage of target breeds, the limitations of sequencing data, and the incompleteness of genome assemblies. The recent generation of a new pig breed with superior meat quality, called Nanchukmacdon, and its chromosome-level genome assembly (the NCMD assembly) has provided new opportunities. Results By applying assembly-based SV calling approaches to various genome assemblies of pigs including Nanchukmacdon, the impact of SVs on meat quality was investigated. Especially, by checking the commonality of SVs with other pig breeds, a total of 13,819 Nanchukmacdon-specific SVs (NSVs) were identified, which have a potential effect on the unique meat quality of Nanchukmacdon. The regulatory potentials of NSVs for the expression of nearby genes were further examined using transcriptome- and epigenome-based analyses in different tissues. Conclusions Whole-genome comparisons based on chromosome-level genome assemblies have led to the discovery of SVs affecting meat quality in pigs, and their regulatory potentials were analyzed. The identified NSVs will provide new insights regarding genetic architectures underlying the meat quality in pigs. Finally, this study confirms the utility of chromosome-level genome assemblies and multi-omics analysis to enhance the understanding of unique phenotypes.

Abies holophylla is an evergreen coniferous species that has been widely used for treating pulmonary diseases and colds. Previous research has demonstrated the anti-inflammatory effect of Abies species and the anti-asthmatic activities of Abies holophylla leaf essential oil (AEO). As asthma and allergic rhinitis (AR) share pathophysiology and pharmacotherapeutic interventions, AEO inhalation can also ameliorate upper respiratory allergic diseases. This study explored the protective effects of AEO on AR with network pharmacological pathway prediction. The potential target pathways of AEO were analyzed by a network pharmacological approach. The BALB/c mice were sensitized by ovalbumin (OVA) and 10 μm particular matter (PM10) to induce allergic rhinitis. Aerosolized AEO 0.0003% and 0.03% were delivered by nebulizer for 5 min a day, 3 times a week for 7 weeks. Nasal symptoms (sneezing and rubbing), histopathological changes in nasal tissues, serum IgE, and zonula occludens-1 (ZO-1) expressions on nasal tissues were analyzed. After AR induction with OVA+PM10 and inhalation of AEO 0.0003% and 0.03% treatment, AEO significantly decreased allergic symptoms (sneezing and rubbing), hyperplasia of nasal epithelial thickness, goblet cell counts, and serum IgE level. The network analysis demonstrated that the possible molecular mechanism of AEO is highly associated with the IL-17 signaling pathway and tight junction. The target pathway of AEO was investigated in RPMI 2650 nasal epithelial cells. Treatment of AEO on PM10-treated nasal epithelial cells significantly reduced the production of inflammatory mediators related to the IL-17 signaling pathway, NF-κB, and the MAPK signaling pathway and prevented the reduction in TJ-related factors. When taken together, AEO inhalation may be considered as a potential treatment for AR by alleviating nasal inflammation and recovering the tight junction.

Pig as a food source serves daily dietary demand to a wide population around the world. Preference of meat depends on various factors with muscle play the central role. In this regards, selective breeding abled us to develop “Nanchukmacdon” a pig breeds with an enhanced variety of meat and high fertility rate. To identify genomic regions under selection we performed whole-genome resequencing, transcriptome, and whole-genome bisulfite sequencing from Nanchukmacdon muscles samples and used published data for three other breeds such as Landrace, Duroc, Jeju native pig and analyzed the functional characterization of candidate genes. In this study, we present a comprehensive approach to identify candidate genes by using multi-omics approaches. We performed two different methods XP-EHH, XP-CLR to identify traces of artificial selection for traits of economic importance. Moreover, RNAseq analysis was done to identify differentially expressed genes in the crossed breed population. Several genes ( UGT8, ZGRF1, NDUFA10, EBF3, ELN, UBE2L6, NCALD, MELK, SERP2, GDPD5, and FHL2 ) were identified as selective sweep and differentially expressed in muscles related pathways. Furthermore, nucleotide diversity analysis revealed low genetic diversity in Nanchukmacdon for identified genes in comparison to related breeds and whole-genome bisulfite sequencing data shows the critical role of DNA methylation pattern in identified genes that leads to enhanced variety of meat. This work demonstrates a way to identify the molecular signature and lays a foundation for future genomic enabled pig breeding.

As data-driven medical research advances, vast amounts of medical data are being collected, giving researchers access to important information. However, issues such as heterogeneity, complexity, and incompleteness of datasets limit their practical use. Errors and missing data negatively affect artificial intelligence-based predictive models, undermining the reliability of clinical decision-making. Thus, it is important to develop a quality management process (QMP) for clinical data. This study aimed to develop a rules-based QMP to address errors and impute missing values in real-world data, establishing high-quality data for clinical research. We used clinical data from 6491 patients with colorectal cancer (CRC) collected at Gachon University Gil Medical Center between 2010 and 2022, leveraging the clinical library established within the Korea Clinical Data Use Network for Research Excellence. First, we conducted a literature review on the prognostic prediction of CRC to assess whether the data met our research purposes, comparing selected variables with real-world data. A labeling process was then implemented to extract key variables, which facilitated the creation of an automatic staging library. This library, combined with a rule-based process, allowed for systematic analysis and evaluation. Theoretically, the tumor, node, metastasis (TNM) stage was identified as an important prognostic factor for CRC, but it was not selected through feature selection in real-world data. After applying the QMP, rates of missing data were reduced from 75.3% to 35.7% for TNM and from 24.3% to 18.5% for surveillance, epidemiology, and end results across 6491 cases, confirming the system's effectiveness. Variable importance analysis through feature selection revealed that TNM stage and detailed code variables, which were previously unselected, were included in the improved model. In sum, we developed a rules-based QMP to address errors and impute missing values in Korea Clinical Data Use Network for Research Excellence data, enhancing data quality. The applicability of the process to real-world datasets highlights its potential for broader use in clinical studies and cancer research.

As plentiful high-quality genome assemblies have been accumulated, reference-guided genome assembly can be a good approach to reconstruct a high-quality assembly. Here, we present a chromosome-level genome assembly of the Korean crossbred pig called Nanchukmacdon (the NCMD assembly) using the reference-guided assembly approach with short and long reads. The NCMD assembly contains 20 chromosome-level scaffolds with a total size of 2.38 Gbp (N50: 138.77 Mbp). Its BUSCO score is 93.1%, which is comparable to the pig reference assembly, and a total of 20,588 protein-coding genes, 8,651 non-coding genes, and 996.14 Mbp of repetitive elements are annotated. The NCMD assembly was also used to close many gaps in the pig reference assembly. This NCMD assembly and annotation provide foundational resources for the genomic analyses of pig and related species.

The circadian clock orchestrates behavior and physiology through the oscillation of key clock proteins like PERIOD (PER). Here, we investigate the role of ubiquitin-specific peptidase 14 (USP14) in modulating PER stability and circadian rhythms in Drosophila . We find that overexpression of USP14 in clock cells reduces PER protein levels without altering its mRNA levels whereas USP14 knockdown increases PER protein levels, suggesting that USP14 regulates PER post-translationally. Interestingly, despite these alterations in PER levels, neither USP14 overexpression nor knockdown significantly impacts circadian behavioral rhythms, likely because of slight effects on PER levels in small ventral lateral neurons (sLN v s). Further analysis shows that USP14 physically interacts with Supernumerary Limbs (SLIMB), a protein involved in PER degradation. Moreover, reducing slimb expression mitigates the effects of USP14 on PER protein stability. Mass spectrometry identifies two ubiquitination sites on PER (Lys1117 and Lys1118) critical for its degradation. Expression of PER 1117A, 1118A mutant in per 01 background impairs circadian rhythm strength. In conclusion, this study demonstrates that Drosophila USP14 indirectly modulates PER protein stability by affecting SLIMB and highlights the critical role of specific ubiquitination sites on PER in maintaining circadian rhythms. USP14 regulates PERIOD (PER) protein stability post-translationally in Drosophila via interaction with SLIMB, a protein involved in PER degradation, and Lys1117 and Lys1118 are key ubiquitination sites critical for maintaining PER stability.

Endoplasmic reticulum (ER) stress and its adaptive cellular response, the unfolded protein response (UPR), are involved in various diseases including neurodegenerative diseases, metabolic diseases, and even cancers. Here, we analyzed the novel function of ubiquitin-specific peptidase 14 (USP14) in ER stress. The overexpression of Drosophila USP14 protected the cells from ER stress without affecting the proteasomal activity. Null Hong Kong (NHK) and alpha-1-antitrypsin Z (ATZ) are ER-associated degradation substrates. The degradation of NHK, but not of ATZ, was delayed by USP14. USP14 restored the levels of rhodopsin-1 protein in a Drosophila model for autosomal dominant retinitis pigmentosa and suppressed the retinal degeneration in this model. In addition, we observed that proteasome complex is dynamically reorganized in response to ER stress in human 293T cells. These findings suggest that USP14 may be a therapeutic strategy in diseases associated with ER stress.

Background Dementia is a major public health problem affecting millions of people worldwide. Early diagnosis and intervention are essential to improve quality of life and reduce the burden of dementia. Recently, voice digital biomarkers have emerged as a promising approach for the early detection of dementia owing to its clinical utility and accessibility. Method In this study, we present a voice biomarker‐based dementia prediction system using a Korean dataset from the AI hub entitled cognitive disorder diagnostic speech/dialogue. A total of 5,769 voice recordings were obtained, of which 2,424 were from CN, 1,611 from MCI, and 1,734 from AD, and were recorded during the administration of cognitive tests. We first extracted dementia‐related speech biomarkers using signal processing and speech activity detection. The extracted speech biomarkers were then used to predict the Clinical Dementia Rating (CDR) to discriminate the severity of dementia from the dataset using ML/DL framework. We also developed an automated engine to extract the voice biomarkers and predict the CDR. The engine was deployed using Docker containers and a web service was implemented using the Flutter framework. Result The results showed that the proposed system achieved a CDR prediction accuracy of 83.6% and precision of 0.872. This is comparable to the accuracy of other voice biomarker‐based dementia prediction systems developed using Western datasets. A Flutter‐based web service is shown in the attached figure. Conclusion The proposed system is a promising new approach for early detection of dementia. It is easy to use, accessible and inexpensive, making it a potential tool for widespread use. The system could be used to screen large numbers of people for dementia, potentially leading to earlier diagnosis and intervention. In addition, the use of a low‐language digital voice biomarker makes the system more accessible to people from different linguistic backgrounds. This could help to reduce inequalities in dementia detection and care. In the future, we plan to add a variety of predictive models, including depression and Parkinson's disease. More detailed information, including a published web site, will be released through the AAIC 2024 poster session.

Advancements in next-generation sequencing (NGS) technologies have led to a substantial increase in the availability of population genetic variant data, thus prompting the development of various population analysis tools to enhance our understanding of population structure and evolution. The tools that are currently used to analyze population genetic variant data generally require different environments, parameters, and formats of the input data, which can act as a barrier preventing the wide-spread usage of such tools by general researchers who may not be familiar with bioinformatics. To address this problem, we have developed an automated and comprehensive pipeline called PAPipe to perform nine widely used population genetic analyses using population NGS data. PAPipe seamlessly interconnects and serializes multiple steps, such as read trimming and mapping, genetic variant calling, data filtering, and format converting, along with nine population genetic analyses such as principal component analysis, phylogenetic analysis, population tree analysis, population structure analysis, linkage disequilibrium decay analysis, selective sweep analysis, population admixture analysis, sequentially Markovian coalescent analysis, and fixation index analysis. PAPipe also provides an easy-to-use web interface that allows for the parameters to be set and the analysis results to be browsed in intuitive manner. PAPipe can be used to generate extensive results that provide insights that can help enhance user convenience and data usability. PAPipe is freely available at https://github.com/jkimlab/PAPipe.