Computational Biology Lab @ UNIST
Our laboratory focuses on characterizing caner genomes using multi-omics approaches based on the next-generation sequencing technologies to identify various kinds of cancer-causing germline & somatic variations and to eventually understand mechanisms of cancer initiation and development.
Multi-omics analysis of cancer genomes
We analyze large-number of cancer genomes using multiple omics approaches to characterize various kinds of somatic alterations in many different human cancer types. From the large-scale cancer genome analyses, we can detect Significant number of cancer driver mutations which play important roles in cancer initiation and progression.
To understand somatic mosaicism and heterogeneity in cancer, we use single-cell genomics approaches. Especially for human brain neurons, we have identified lineage relationship among single neurons using single nucleotide variants detected using single-cell whole-genome sequencing.
For pancreatic cancer and its liver metastasis, we analyze single cells from fine needle aspiration (FNA) samples to understand their evolution.
We also develop machine learning packages to predict disease risk and cancer type using large-scale genomics data.
Multi-omics analysis of patient-derived 3D organoids
By performing whole-exome, whole-transcriptome, and whole-methylome sequencing of patient-derived 3D organoids, we characterize patient-specific genomic & epigenomic alterations in cancer and associate them with drug response data to identify patient-specific biomarkers for personalized cancer therapy.
Personalized cancer genomics
Recent progress in incorporating genomic information into clinical practice means that it is becoming increasingly feasible to personalize treatment according to the composition of the patient's genome. Our lab closely collaborates with clinicians to apply next-generation sequencing and establish its clinical utility for medical advancements.
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Last modified at 2022-11-21T18:37:18+09:00