Computational Biology Lab @ UNIST

Our laboratory focuses on characterizing cancer genomes using multi-omics approaches based on advanced next-generation sequencing technologies. We aim to identify a wide spectrum of cancer-causing germline and somatic variations, including single nucleotide variants, structural variations, and epigenetic modifications. By integrating genomic, transcriptomic, epigenomic, and proteomic data, we strive to uncover the complex mechanisms underlying cancer initiation, progression, and metastasis. Our research also emphasizes the development of computational pipelines and bioinformatics tools to enhance the accuracy and efficiency of cancer genome analysis, ultimately contributing to the discovery of novel biomarkers and therapeutic targets for precision oncology.

Multi-omics analysis of cancer genomes

Our laboratory specializes in the multi-omics analysis of cancer genomes. By examining a large number of cancer genomes using multiple omics approaches—including genomics, transcriptomics, epigenomics, and proteomics—we aim to characterize various kinds of somatic alterations across many different human cancer types. Through large-scale cancer genome analyses, we can detect significant numbers of cancer driver mutations that play crucial roles in cancer initiation and progression.

By integrating data from different omics layers, we strive to uncover the complex molecular mechanisms underlying cancer development and progression. Our research focuses on identifying novel cancer driver mutations, including single nucleotide variants, insertions and deletions, copy number variations, and structural rearrangements. Additionally, we are committed to developing advanced bioinformatics tools and computational pipelines to enhance the accuracy and efficiency of our analyses.

Ultimately, our goal is to contribute to the advancement of precision oncology by discovering new biomarkers for early detection and prognosis, as well as identifying potential therapeutic targets to improve patient outcomes.

Single-cell genomics

Our laboratory is at the forefront of single-cell genomics research, utilizing advanced technologies to dissect somatic mosaicism and cellular heterogeneity within cancers. By integrating genomic and transcriptomic data at the single-cell level, we focus on understanding the complex cellular interactions and genetic variations that occur during cancer initiation, progression, and metastasis.

To achieve this, we employ cutting-edge techniques such as single-cell RNA sequencing, single-cell DNA sequencing, and spatial transcriptomics. Leveraging machine learning and big data analytics, we efficiently process and interpret vast amounts of data. Our research aims to discover novel cancer biomarkers and contribute to the development of personalized therapeutic strategies.

Additionally, our laboratory collaborates with various national and international research institutions and hospitals to implement a multidisciplinary approach. Ultimately, we aim to improve patient prognosis and enhance the quality of life for those affected by cancer.

Multi-omics analysis of patient-derived 3D organoids

Our laboratory specializes in the multi-omics analysis of patient-derived three-dimensional (3D) organoids to advance personalized cancer therapy. By performing whole-exome sequencing (WES), whole-transcriptome sequencing (RNA-seq), and whole-methylome sequencing on these organoids, we characterize patient-specific genomic and epigenomic alterations in cancer.

We integrate this comprehensive data with drug response profiles to identify patient-specific biomarkers and therapeutic targets. This approach enables us to understand the molecular mechanisms driving individual tumors and to predict their responsiveness to various therapeutic agents.

In addition to sequencing technologies, we employ advanced bioinformatics and computational biology tools to analyze and interpret the vast amount of data generated. Our team develops and refines computational pipelines to enhance the accuracy and efficiency of multi-omics data integration and analysis.

Collaborating with clinicians and researchers both nationally and internationally, we aim to translate our findings into clinical applications. Through these multidisciplinary efforts, we strive to bridge the gap between bench and bedside, ultimately improving patient outcomes by tailoring treatments to individual genetic and epigenetic profiles.

Our research not only contributes to the discovery of novel biomarkers but also advances the field of precision oncology. By leveraging patient-derived 3D organoids, we create more accurate models of tumor biology, facilitating the development of effective, personalized cancer therapies.

Personalized cancer therapy

Our laboratory is dedicated to advancing personalized medicine by integrating genomic information into clinical practice. Recent progress in genomics and next-generation sequencing (NGS) technologies has made it increasingly feasible to tailor treatments according to the unique genetic composition of each patient’s cancer.

We closely collaborate with clinicians to apply NGS and establish its clinical utility for medical advancements. By analyzing patient-specific genomic data, including whole-genome sequencing, whole-exome sequencing, and transcriptome profiling, we aim to identify genetic alterations that drive cancer progression and response to therapy.

Our research focuses on discovering novel biomarkers and therapeutic targets by studying a wide range of genomic variations such as single nucleotide variants, copy number alterations, structural rearrangements, and epigenetic modifications. We also investigate mechanisms of drug resistance and sensitivity by integrating genomic data with clinical outcomes and drug response profiles.

To enhance the accuracy and efficiency of genomic analysis, we develop and refine computational pipelines and bioinformatics tools. Leveraging machine learning algorithms and big data analytics, we process and interpret vast amounts of sequencing data to generate actionable insights for precision oncology.

In addition to our clinical collaborations, we engage in multidisciplinary partnerships with researchers in molecular biology, computational biology, and pharmacology. Through these efforts, we aim to bridge the gap between bench and bedside, translating our findings into clinical applications that improve patient outcomes.

Our ultimate goal is to contribute to the advancement of personalized cancer therapy by harnessing the power of genomics. By tailoring treatments based on individual genetic profiles, we strive to enhance the effectiveness of cancer therapies and improve the quality of life for patients.

Machine learning for genomic research

Our laboratory specializes in developing advanced machine learning algorithms and software packages to predict disease risk and classify cancer types using large-scale genomics data. By harnessing high-throughput sequencing technologies and big data analytics, we aim to transform vast amounts of genomic information into actionable insights for precision medicine.

We focus on creating predictive models that integrate various types of omics data, including genomic, transcriptomic, epigenomic, and proteomic datasets. Utilizing techniques such as deep learning, neural networks, and ensemble methods, our goal is to enhance the accuracy and reliability of disease risk assessments and cancer subtype identification.

In addition to algorithm development, we work on building user-friendly computational tools and pipelines that can be easily adopted by the research community and clinicians. Our software packages are designed to handle complex datasets efficiently, providing robust and interpretable results that can inform clinical decision-making.

Collaborating with interdisciplinary teams of geneticists, oncologists, bioinformaticians, and data scientists, we strive to bridge the gap between computational research and clinical application. Through these collaborative efforts, we aim to improve early disease detection, personalize treatment strategies, and ultimately contribute to better patient outcomes.

Our research not only advances the application of machine learning in genomics but also addresses challenges such as data heterogeneity, scalability, and model interpretability. By pushing the boundaries of computational biology, we are committed to driving innovation in healthcare through the power of machine learning and big data.

Human microbiome

Our Computational Biology Group is dedicated to unraveling the complexities of the human microbiome and its profound influence on health and disease. Utilizing advanced computational methods and bioinformatics tools, we analyze extensive microbiome datasets to understand the intricate relationships between microbial communities and their human hosts. By examining the composition and diversity of microbes across different body sites and populations, we aim to elucidate how factors like diet, environment, lifestyle, and genetics shape the microbiome’s structure and function.

A significant focus of our research is exploring how alterations in microbial communities are associated with various diseases, including inflammatory bowel disease, obesity, diabetes, neurological disorders, and cancer. Through the integration of microbiome data with clinical and genomic information, we strive to identify microbial signatures that can serve as biomarkers for disease diagnosis and prognosis. This involves deep metagenomic and metatranscriptomic analyses to uncover the functional capabilities of microbial ecosystems, such as gene expression patterns and metabolic pathways, and how these influence host physiology and immune responses.

Our team is committed to advancing the field by developing sophisticated machine learning algorithms and statistical models that enhance the analysis of complex microbiome datasets. These computational tools enable us to predict disease risk, classify microbial communities more accurately, and identify key microbial biomarkers with greater precision. By refining computational pipelines, we ensure that microbiome data analysis becomes more efficient and accessible to the broader research community.

Collaboration is integral to our approach. We work closely with clinicians, microbiologists, immunologists, and other researchers to translate our scientific findings into practical applications. This includes the development of microbiome-based diagnostics, personalized therapeutics, probiotics, and dietary interventions designed to modulate the microbiome for improved health outcomes. Our interdisciplinary efforts aim to bridge the gap between computational research and clinical practice, fostering innovations that can be directly applied to patient care.

Ultimately, our goal is to make significant contributions to the understanding of the human microbiome’s role in health and disease. By uncovering new insights into microbial functions and interactions, we hope to enhance disease prevention and treatment strategies, advance personalized medicine, and positively impact public health. Through harnessing the power of the human microbiome, we aspire to improve patient well-being and contribute to the broader field of precision medicine.

Rare diseases research

Our Computational Biology Group is dedicated to advancing the understanding and treatment of rare diseases through the application of cutting-edge computational methods and bioinformatics tools. Rare diseases, while individually uncommon, collectively affect millions of people worldwide and often lack effective diagnostics and treatments due to limited research resources. By leveraging high-throughput sequencing technologies and large-scale genomic data analysis, we aim to uncover the genetic and molecular underpinnings of these conditions.

We focus on identifying novel genetic variants and mutations that contribute to rare diseases. Utilizing whole-genome and whole-exome sequencing, we analyze patient DNA samples to discover pathogenic variants that may be responsible for disease phenotypes. By integrating genomic data with transcriptomic, proteomic, and epigenomic information, we strive to construct comprehensive models of disease mechanisms.

Our team develops advanced computational algorithms and machine learning techniques to analyze complex datasets. These tools enable us to sift through vast amounts of genomic data to pinpoint candidate genes and mutations. We also create predictive models to understand genotype-phenotype correlations, which can aid in diagnosing patients and predicting disease progression.

Collaboration is central to our approach. We work closely with clinicians, geneticists, and researchers globally to validate our findings and translate them into clinical applications. By sharing data and resources, we aim to accelerate the discovery of effective diagnostics and therapeutic strategies for rare diseases.

Our ultimate goal is to improve the lives of patients affected by rare diseases by contributing to personalized medicine. Through our research, we hope to enable earlier and more accurate diagnoses, identify potential drug targets, and facilitate the development of tailored treatments. By shedding light on the genetic basis of rare diseases, we aspire to make a meaningful impact on patient care and outcomes.