Research Areas
12 core research areas driving innovation in bioinformatics and computational biology
Core Research Areas
Our program integrates computational and biological expertise across 12 specialized research domains, enabling students to pursue cutting-edge research in their area of interest.
01 Sequence Analysis and Genomics
Comprehensive computational approaches to analyze DNA, RNA, and protein sequences, including whole-genome sequencing, variant calling, and comparative genomics
Sequence Analysis and Genomics
Comprehensive computational approaches to analyze DNA, RNA, and protein sequences, including whole-genome sequencing, variant calling, and comparative genomics
Overview
Sequence analysis and genomics encompasses the computational methods used to analyze biological sequences at scale. This research area covers everything from raw sequencing data processing to high-level comparative genomic studies.
Key Topics
- Whole-genome sequencing and assembly
- SNP/indel detection and variant calling
- Genome annotation and functional element prediction
- Comparative genomics and synteny analysis
- Long-read sequencing (PacBio, Oxford Nanopore)
- Population genomics
Applications
- Disease variant discovery
- Crop genome improvement
- Phylogenomic studies
- Personalized medicine
02 Structural Bioinformatics
Prediction, analysis, and simulation of three-dimensional protein and RNA structures to understand molecular function, interactions, and drug targets
Structural Bioinformatics
Prediction, analysis, and simulation of three-dimensional protein and RNA structures to understand molecular function, interactions, and drug targets
Overview
Structural bioinformatics bridges computational methods and molecular biology to understand how the three-dimensional shape of biological macromolecules determines their function.
Key Topics
- Protein structure prediction (homology modeling, AlphaFold)
- Molecular dynamics (MD) simulation
- Protein-ligand and protein-protein docking
- RNA structure prediction
- Coarse-grained and all-atom simulation
- Structure-function relationship analysis
Applications
- Drug target identification
- Understanding ion channel mechanisms
- Enzyme engineering
- Vaccine design
03 Systems Biology and Networks
Mathematical and computational modeling of biological networks and pathways to understand complex cellular behavior and emergent system properties
Systems Biology and Networks
Mathematical and computational modeling of biological networks and pathways to understand complex cellular behavior and emergent system properties
Overview
Systems biology integrates experimental data with computational models to understand how biological components interact at the systems level. Network analysis reveals how genes, proteins, and metabolites are functionally connected.
Key Topics
- Gene regulatory network modeling
- Protein-protein interaction (PPI) networks
- Metabolic flux balance analysis (FBA)
- Signal transduction pathway modeling
- Network topology and hub gene analysis
- Multi-omics data integration
Applications
- Identifying drug targets in disease networks
- Metabolic engineering
- Understanding antibiotic resistance
- Systems-level biomarker discovery
04 Metagenomics and Microbial Diversity
Analysis of microbial communities from environmental or clinical samples using 16S rRNA profiling and whole-metagenome shotgun sequencing
Metagenomics and Microbial Diversity
Analysis of microbial communities from environmental or clinical samples using 16S rRNA profiling and whole-metagenome shotgun sequencing
Overview
Metagenomics enables the study of entire microbial communities without requiring individual cultivation. This research area covers the computational pipelines and statistical methods needed to characterize microbial diversity and function.
Key Topics
- 16S rRNA amplicon sequencing and OTU/ASV analysis
- Whole-metagenome shotgun sequencing
- Microbiome diversity indices (alpha/beta diversity)
- Functional gene profiling and pathway analysis
- Host-microbiome interaction analysis
- Long-read metagenomic assembly
Applications
- Gut microbiome and human health
- Environmental monitoring
- Infectious disease epidemiology
- Agricultural soil microbiome studies
05 Transcriptomics and Gene Expression
Computational analysis of RNA sequencing data to quantify gene expression, identify differentially expressed genes, and characterize the transcriptome
Transcriptomics and Gene Expression
Computational analysis of RNA sequencing data to quantify gene expression, identify differentially expressed genes, and characterize the transcriptome
Overview
Transcriptomics provides a snapshot of which genes are active in a cell or tissue at a given time. Computational analysis of RNA-seq data enables the discovery of expression patterns, regulatory mechanisms, and disease-associated changes.
Key Topics
- Bulk RNA-seq analysis and differential expression
- Alternative splicing analysis
- Long non-coding RNA (lncRNA) characterization
- RNA modification analysis (m6A, Nanopore-based)
- CRISPR-based functional screens with transcriptomic readout
- Oxford Nanopore direct RNA sequencing
Applications
- Disease biomarker discovery
- Drug response profiling
- Developmental biology
- Stress response in plants and microbes
06 Proteomics and Metabolomics
Mass spectrometry-based computational approaches for large-scale identification, quantification, and functional characterization of proteins and metabolites
Proteomics and Metabolomics
Mass spectrometry-based computational approaches for large-scale identification, quantification, and functional characterization of proteins and metabolites
Overview
Proteomics and metabolomics complement genomics by directly measuring the functional molecules in cells. Computational analysis of mass spectrometry data identifies and quantifies thousands of proteins and metabolites simultaneously.
Key Topics
- Database searching and de novo peptide sequencing
- Label-free and isotope-labeled quantification
- Post-translational modification (PTM) analysis
- Metabolite identification and pathway mapping
- Multi-omics data integration (proteome + metabolome)
- Structural proteomics (cross-linking MS)
Applications
- Biomarker discovery for disease
- Drug mechanism of action
- Plant secondary metabolite profiling
- Clinical diagnostics
07 Machine Learning in Biology
Application of deep learning, neural networks, graph models, and AI methods to predict biological properties, classify diseases, and discover patterns in omics data
Machine Learning in Biology
Application of deep learning, neural networks, graph models, and AI methods to predict biological properties, classify diseases, and discover patterns in omics data
Overview
Machine learning has transformed bioinformatics by enabling pattern recognition in high-dimensional biological data. Deep learning models can predict protein function, classify cancer subtypes, and identify regulatory elements with unprecedented accuracy.
Key Topics
- Deep learning for sequence classification (CNNs, RNNs, Transformers)
- Graph neural networks for biological networks
- Generative models for protein design
- Ensemble methods for clinical prediction
- Feature selection in high-dimensional omics data
- Explainable AI (XAI) for biological insights
Applications
- Protein structure and function prediction
- Cancer subtype classification
- Drug response prediction
- Gene regulatory element identification
- Rare disease diagnosis
08 Computational Drug Discovery
In silico methods for identifying, screening, and optimizing drug candidates including virtual screening, QSAR modeling, and pharmacogenomics
Computational Drug Discovery
In silico methods for identifying, screening, and optimizing drug candidates including virtual screening, QSAR modeling, and pharmacogenomics
Overview
Computational drug discovery accelerates the identification of therapeutic candidates by using simulations, statistical models, and databases to prioritize compounds before experimental testing.
Key Topics
- Virtual screening and molecular docking
- Quantitative structure-activity relationship (QSAR) modeling
- Pharmacophore modeling
- Drug-target interaction prediction
- ADMET property prediction (absorption, distribution, metabolism, excretion, toxicity)
- Repurposing approved drugs for new indications
Applications
- Antibacterial and antiviral drug design
- Kinase inhibitor development
- Natural product-based drug discovery
- Precision oncology target identification
09 Medical and Clinical Bioinformatics
Application of bioinformatics to clinical genomics, rare disease diagnosis, genome-wide association studies, and precision medicine
Medical and Clinical Bioinformatics
Application of bioinformatics to clinical genomics, rare disease diagnosis, genome-wide association studies, and precision medicine
Overview
Medical bioinformatics translates computational genomics methods into clinical practice. It encompasses the analysis of patient genomes, discovery of disease-causing variants, and development of tools for precision medicine.
Key Topics
- Genome-wide association studies (GWAS)
- Rare disease variant interpretation
- Clinical next-generation sequencing (NGS) pipelines
- Pharmacogenomics and drug response prediction
- Cancer genomics (somatic mutation analysis, copy number variation)
- Electronic health record (EHR) data integration
Applications
- Rare and hereditary disease diagnosis
- Tumor genomics and targeted therapy
- Population health genomics
- Drug safety and efficacy prediction
10 Evolutionary Bioinformatics
Computational reconstruction of evolutionary relationships through molecular phylogenetics, phylogenomics, and molecular evolution analysis
Evolutionary Bioinformatics
Computational reconstruction of evolutionary relationships through molecular phylogenetics, phylogenomics, and molecular evolution analysis
Overview
Evolutionary bioinformatics uses molecular sequence data to reconstruct the history of life, understand how genes evolve, and infer ancestral states. These methods are fundamental to comparative genomics, viral epidemiology, and biodiversity research.
Key Topics
- Multiple sequence alignment methods
- Phylogenetic tree construction (maximum likelihood, Bayesian)
- Molecular clock models
- Phylogenomics (genome-scale phylogenetics)
- Positive selection detection
- Population genetics and demographic inference
Applications
- Viral outbreak tracing and epidemiology
- Conservation biology and biodiversity
- Protein function evolution
- Co-evolution of host and pathogen
11 Single-Cell Omics
Computational methods for analyzing single-cell RNA sequencing and multi-omics data to resolve cellular heterogeneity, identify cell types, and study developmental trajectories
Single-Cell Omics
Computational methods for analyzing single-cell RNA sequencing and multi-omics data to resolve cellular heterogeneity, identify cell types, and study developmental trajectories
Overview
Single-cell technologies have revolutionized biology by allowing measurement of gene expression, chromatin accessibility, and other molecular features in individual cells. Computational analysis is essential for extracting meaningful biology from these high-dimensional, sparse datasets.
Key Topics
- scRNA-seq quality control, normalization, and clustering
- Cell type annotation and marker gene identification
- Pseudotime and trajectory analysis
- Cell-cell communication inference
- Spatial transcriptomics data analysis
- Multi-modal single-cell data integration (CITE-seq, ATAC-seq)
Applications
- Tumor microenvironment characterization
- Developmental biology and lineage tracing
- Immune cell profiling
- Drug response heterogeneity
12 Agricultural and Plant Bioinformatics
Computational analysis of plant genomes, transcriptomes, and metabolomes to support crop improvement, stress tolerance, and the study of phytonutrients and bioactive compounds
Agricultural and Plant Bioinformatics
Computational analysis of plant genomes, transcriptomes, and metabolomes to support crop improvement, stress tolerance, and the study of phytonutrients and bioactive compounds
Overview
Agricultural and plant bioinformatics applies computational methods to understand plant biology at the molecular level, supporting efforts to develop more productive, resilient crops and to discover plant-derived bioactive compounds.
Key Topics
- Crop genome assembly, annotation, and pan-genomics
- Stress response transcriptomics (drought, salinity, heat)
- Phytonutrient and secondary metabolite profiling
- CRISPR-Cas genome editing target design
- Quantitative trait loci (QTL) mapping
- Engineering cyanobacteria for plant bioactive compound production
Applications
- Drought and disease-resistant crop varieties
- Enhanced nutritional content in food crops
- Cassava starch biosynthesis optimization
- Antimicrobial peptide discovery in rice
Research Applications
These research areas drive real-world impact across five major application domains