Bioinformatics

Overview

Decoding Genomic Big Data Through Computational Innovation

Bioinformatics stands at the intersection of molecular biology, computer science, and data analytics, serving as an indispensable foundation for modern life sciences. By leveraging computational algorithms, databases, and statistical pipelines, bioinformatics translates raw biological big data into meaningful mechanistic insights and clinical applications.

In this program, students transition from theoretical concepts to practical biological data science, mastering essential computational tools for processing high-throughput genomic and transcriptomic datasets. Guided through research-driven workflows, participants learn to formulate biological hypotheses, execute reproducible analytical pipelines, and interpret genomic variants, equipping them with the analytical rigor required for top-tier academic research and biotechnology careers.

Selected Topics

Sequence Alignment & Homology Searching

Students explore global and local alignment algorithms (Needleman-Wunsch, Smith-Waterman) and utilize BLAST to discover sequence similarity, evolutionary conservation, and functional domain annotations.

Next-Generation Sequencing (NGS) Data Analysis

Covers raw read quality assessment, adapter trimming, reference genome alignment (BWA/Bowtie2), and variant calling workflows for whole-genome and exome sequencing data.

RNA-Seq & Differential Gene Expression Profiling

Focuses on transcript quantification, normalization strategies such as TPM and DESeq2 size-factor normalization, differential expression testing, and pathway enrichment visualization via heatmaps and volcano plots.

Structural Bioinformatics & Protein Docking

Teaches students to retrieve and analyze 3D structures from PDB, evaluate AI-driven protein folding models (AlphaFold), and simulate small-molecule ligand docking.

Phylogenetics & Evolutionary Genomics

Introduces distance-based and maximum likelihood tree-building methods to infer evolutionary relationships, mutation rates, and lineage divergence across species.

Metagenomics & Microbiome Profiling

Examines 16S rRNA and shotgun metagenomic pipelines to characterize complex microbial compositions, taxonomic diversity, and metabolic functional potential.

Sample Project Design

Genomic Data Analysis & Variant Interpretation in Precision Medicine

In this guided computational project, students process high-throughput sequencing data to identify, annotate, and evaluate candidate somatic mutations in target disease pathways.

Research Question

How do detected genomic variants alter gene function, protein structure, and potential clinical drug responsiveness?

Analytical & Computational Activities

Students execute end-to-end bioinformatics pipelines, including:

Evaluating raw FASTQ sequencing quality metrics using FastQC and Trimmomatic.
Aligning sequencing reads to the reference genome (GRCh38) via BWA-MEM.
Executing variant calling pipelines (GATK/Mutect2) to detect SNVs and Indels.
Annotating variants using ANNOVAR with databases like ClinVar, dbSNP, and COSMIC.
Assessing predicted variant effects using SIFT and PolyPhen-2, with AlphaFold-based structural models used to examine potential structural consequences.
Mapping non-synonymous mutations to target signaling pathways and biological networks.

Data Analysis & Pipeline Evaluation

Transforming genomic variants into actionable biological insights:

Visual Genomic Interpretation Students construct publication-ready alignment visualizations (IGV browser tracks), mutation waterfall plots, and pathway enrichment diagrams.

Statistical & Quality Metrics Participants compute alignment coverage, mapping quality scores, and variant allele frequencies to rule out sequencing artifacts.

Pipeline Optimization Students evaluate parameter sensitivity, bench efficiency, and computational bottlenecks within automated Nextflow / Snakemake workflows.

Industry Context & Translational Research

This project bridges computational biology with clinical application, showing how automated genomic workflows form the backbone of modern molecular diagnostics, biomarker discovery, and targeted therapy selection.

Computational Scalability Designing reproducible pipelines capable of handling terabytes of clinical sequencing data efficiently on cloud platforms.

Clinical Variant Rigor Applying established somatic variant interpretation frameworks, such as AMP/ASCO/CAP criteria, to assess clinical significance and therapeutic relevance.

Targeted Biomarker Discovery Identifying gene-drug interactions to support patient stratification and personalized medicine trial design.

Data Privacy & Integrity Adhering to genomic data management, HIPAA compliance, and open-science FAIR data principles.

Possible Project Outputs

Depending on individual progress, deliverables may include:

Modular Bash / Python analytical scripts or Jupyter Notebooks
Standardized VCF files and annotated genomic variant reports
Formal computational biology research manuscript draft
Scientific poster highlighting target mutation analysis
Reproducible workflow repositories (GitHub / GitLab)

Computational Translation

Genomic analysis fuels innovation across biopharma and healthtech. Through mentor guidance, students explore:

How variant detection guides structure-based rational drug design.
How clinical databases integrate sequence interpretation into oncology treatments.
How artificial intelligence models transform structural and sequence predictions.