이인석 교수
In-seok Lee
연세대학교 생명공학부 · 공학
연구실 소개
이인석 교수 연구실은 유전자 기능 해석과 병변 기반 유전자 네트워크 분석을 중심으로, 고처리량 유전체 및 전사체 데이터를 기반으로 한 생물학적 과정의 시스템적 이해를 추구합니다. 특히, 전통적인 유전자 집합 분석의 한계를 보완하기 위해 유전자 간 상호작용 네트워크를 기반으로 기능적 유전자 집합을 평가하는 '네트워크 기반 GSEA'(NGSEA)와 같은 혁신적 알고리즘을 개발하여 복잡한 질병 및 병원성 미생물의 기능 유전자 네트워크를 규명하고자 합니다. 메타게놈 데이터 기반의 유전자 기능 네트워크 구축 기술도 확장하고 있어, 미세생물군집의 기능적 기작 해석에 기여하고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
9Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional
Advanced high-throughput sequencing technology accumulated massive amount of genomics and transcriptomics data in the public databases. Due to the high technical accessibility, DNA and RNA sequencing have huge potential for the study of gene functions in most species including animals and crops. A proven analytic platform to convert sequencing data to gene functional information is co-functional network. Because all genes exert their functions through interactions with others, network analysis i
Various genome-wide approaches to identifying the genetic components that underlie phenotypes in animals and humans have been developed during the last several decades. The relationship between gene and phenotype, however,cannot be represented by a simple one-to-one correspondence. Rather, many genes are typically related to a single phenotype and many phenotypes can be associated with a single gene, a major theme within the study of complex phenotypes. Therefore, to dissect the genetics of comp
Multiple genes and their interactions are involved in most human diseases. This pathway-centric view of human pathology is beginning to guide our approaches to disease research. Analytical algorithms describing human gene networks have been developed for three major tasks in disease research: (i) disease gene prioritization, (ii) disease module discovery, and (iii) stratification of complex diseases. To understand the underlying biology of human diseases, identification of disease genes and dise
Bacterial species in the genus Xanthomonas infect virtually all crop plants. Although many genes involved in Xanthomonas virulence have been identified through molecular and cellular studies, the elucidation of virulence-associated regulatory circuits is still far from complete. Functional gene networks have proven useful in generating hypotheses for genetic factors of biological processes in various species. Here, we present a genome-scale co-functional network of Xanthomonas oryze pv. oryzae (
The gene neighborhood in prokaryotic genomes has been effectively utilized in inferring cofunctional networks in various organisms. Previously, such genomic context information has been sought among completely assembled prokaryotic genomes. Here, we present a method to infer functional gene networks according to the gene neighborhood in metagenome contigs, which are incompletely assembled genomic fragments. Given that the amount of metagenome sequence data has now surpassed that of completely as
Although approved programmed cell death protein (PD)-1 inhibitors show durable responses, clinical benefits to these agents are only seen in one-third of patients in most cancer types. Therefore, strategies for improving the response to PD-1 inhibitor for treating various cancers including non-small cell lung cancer (NSCLC) are urgently needed. Compared with genome and transcriptome, tumor DNA methylome in anti-PD-1 response was relatively unexplored. We compared the pre-treatment methylation st
Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework for integrative cell-type-specific co-expression network inference from single-cell transcriptome data, demonstrating its utility in augmenting the human interactome for more accurate disease gene pred
In the fabrication process of optical elements (OEs) by the laser scanning method using a dichromated gelatin (DCG) photoplate, the expansion and drying stress of gelatine caused by inhomogeneous liquid flow inside the gelatine affects the shape of OEs. The reason this inhomogeneous liquid flow exists in the energy oversaturated parts of OEs is the presence of surplus energy. In order to obtain the OEs of desired spherical lens shape, the drying stress should be reduced and therefore the maximum
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