Research
🌎 Our Vision
We aim to create a computational bridge between genome, proteome, and immunome — to reveal the molecular complexity of tumors and guide the next generation of personalized cancer therapies.
🧬 Computational Bioinformatics Lab — Research Focus
Our lab is dedicated to advancing computational proteogenomics for precision oncology. We develop integrative algorithms and pipelines to connect genomic alterations with their proteomic manifestations, aiming to uncover novel molecular mechanisms and therapeutic targets in cancer.
⸻
1️⃣ Proteogenomic Integration for Tumor Antigen Discovery
We explore how somatic mutations, alternative splicing, and non-coding region translations give rise to novel peptides that may serve as tumor-specific antigens. By integrating whole-genome sequencing, RNA-seq, and mass spectrometry-based proteomics, we systematically identify and prioritize candidate neoantigens. Our recently developed tool, pXg, enables precise and scalable detection of variant peptides, facilitating downstream antigen ranking and validation.
⸻
2️⃣ Personalized Protein Databases
To better capture individual tumor heterogeneity, we construct customized protein sequence databases directly from patient-derived genome (VCF) and transcriptome (GTF) data. This allows comprehensive detection of non-reference peptides arising from mutations, fusions, or non-canonical translation events — enabling patient-specific proteomic analysis.
⸻
3️⃣ Computational Methods and Machine Learning
Our lab leverages computational modeling, statistical learning, and AI-driven inference to interpret large-scale multi-omics data. We develop algorithmic frameworks for peptide-spectrum matching, FDR control, and feature-based ranking to improve confidence in proteogenomic discovery pipelines. We are also interested in exploring deep learning approaches for peptide identification and immunogenicity prediction.
⸻
4️⃣ Translational Proteogenomics
By bridging molecular signatures with clinical phenotypes, we aim to translate computational findings into actionable insights for cancer therapy. This includes identifying druggable targets, biomarkers, and immune-relevant neoantigens that can inform immunotherapy and vaccine development.