Min-Ah Roh

Evidence indicates that Helicobacter pylori is the causative agent of chronic gastritis and perhaps gastric malignancy. Extracellular vesicles (EVs) play an important role in the evolutional process of malignancy due to their genetic material cargo. We aimed to evaluate the clinical significance and biological mechanism of H. pylori EVs on the pathogenesis of gastric malignancy. We performed 16S rDNA-based metagenomic analysis of gastric juices either from endoscopic or surgical patients. From e

Recent evidence has indicated that bacteria-derived extracellular vesicles (EVs) are important for host–microbe communication. The aims of the present study were to evaluate whether bacteria-derived EVs are excreted via the urinary tract and to compare the composition of bacteria-derived EVs in the urine of pregnant and non-pregnant women. Seventy-three non-pregnant and seventy-four pregnant women were enrolled from Dankook University and Ewha Womans University hospitals. DNA was extracted from

Microorganisms play a vital role in living systems in numerous ways. In the soil or ocean environment, microbes are involved in diverse processes, such as carbon and nitrogen cycle, nutrient recycling, and energy acquisition. The relation between microbial dysbiosis and disease developments has been extensively studied. In particular, microbial communities in the human gut are associated with the pathophysiology of several chronic diseases such as inflammatory bowel disease and diabetes. Therefo

Next generation sequencing (NGS) technologies have revolutionized many areas of biological research due to thesharp reduction in costs that has led to the generation of massive amounts of sequence information. Analysis of large genomedata sets is however still a challenging task because it often requires significant computer resources andknowledge of bioinformatics. Here, we provide a guide for an uninitiated who wish to analyze high-throughput NGSdata. We focus specifically on the analysis of o

Purpose: Atopic dermatitis (AD) is an inflammatory skin disease, significantly affecting the quality of life. Using AD as a model system, we tested a successive identification of AD-associated microbes, followed by a culture-independent serum detection of the identified microbe. Methods: A total of 43 genomic DNA preparations from washing fluid of the cubital fossa of 6 healthy controls, skin lesions of 27 AD patients, 10 of which later received treatment (post-treatment), were subjected to high

Purpose: The microbial environment is an important factor that contributes to the pathogenesis of atopic dermatitis (AD). Recently, it was revealed that not only bacteria itself but also extracellular vesicles (EVs) secreted from bacteria affect the allergic inflammation process. However, almost all research carried out so far was related to local microorganisms, not the systemic microbial distribution. We aimed to compare the bacterial EV composition between AD patients and healthy subjects and

그 동안 NGS 데이터를 기반으로 SNP/indel을 찾는 수 많은 프로그램들이 개발되어 왔다. 하지만 사용자가 직접 매개 변수를 정해서 variant를 찾는 방식의 기존 프로그램들은 많은 true positive를 놓치거나 많은 false positive를 찾는다. 이는 특히 한 위치에 서로 다른 SNP/indel이 적용된 경우일 때 더욱 심하다. 이러한 문제점을 해결하기 위해 우리는 일반적인 알고리즘보다 좋은 성능을 보이는 딥 러닝 방법을 적용하여 NGS 데이터로부터 SNP/Indel을 찾는 방법을 시도하였다. 특히 텍스트 기반의 NGS 데이터에 적합하도록 read pileup data를 가공한 후 Transformer 모델을 기반으로 변형한 딥러닝 모델에 적용하였다. 이 방식은 SNP와 Indel이 혼용되어 나타나는 케이스에서 더 좋은 성능을 보였으며, 그 외의 경우에도 기존의 다른 프로그램들과 유사한 성능을 보였다.