서울대학교 생물정보학 협동과정 주최로 세미나를 아래와 같이 열고자 하오니 많은 참여 바랍니다.

세미나는 Zoom을 통한 온라인 강의로 예정되어 있으며 강의에 참여하고자 하시는 분께서는 아래의 링크로 참여 부탁드리겠습니다.

*줌으로 참석하기 위해서는 "참석자로 회의 참가" --> 이름, 성, 이메일 주소, 이메일 주소 확인을 입력하시고 등록하시면 회의 참석이 가능합니다.

일시: 2023년 4월 6일 목요일 오전 11시

연사: 정인욱 교수님 (경북대학교)

Zoom link: http://snu-ac-kr.zoom.us/s/93573789211

Inuk Jung

School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Deagu, Republic of Korea

The COVID-19 pandemic has initiated a surge of research projects that largely hunt for clues to shed light on the biological mechanisms that lead to severe symptoms. Especially, understanding the immune system’s response to the infection is being studied in depth. Recently, an extensive set of multi-omics data of a cohort of patients with COVDI-19 were collected in a time-course manner by a project driven by the Korea Disease Control and Prevention Agency (KDCA). In this study, we made effort to analyze the highly complex data to discover strong immune responsive signals that differ between patients with normal, mild and severe symptoms. The analysis involves two approaches, 1) a multi-omics network construction analysis and 2) a time-course cell type specific trajectory analysis. In the network analysis, multi-omics layers are connected per cell type to observe the cell-cell interaction between the mild and severe patients. In the trajectory analysis, a clustering approach was developed to capture the real-time trajectory of cell types to detect severe specific trajectories using single-cell RNA-seq data. During the study, we also discovered that the gene expression in individual cell types showed a bimodal distribution, which was used to discover genes with highly stable and variable expression level. The highly stable expressed genes may be utilized as candidates for batch correcting single-cell expression data.