🧑🏻💻 About me
🎓 I’m a third-year Ph.D. Candidate in Computer Science at the Center for Computational Biology, Johns Hopkins University, working with Steven Salzberg and Mihaela Pertea. My academic journey started in Electrical Engineering at National Taiwan University (NTU), shifting towards computer science in my final year at the College of Engineering & Computer Science at Australian National University (ANU) 🦘🐨
🧬 My research interest intersects “graphs” with genomics and transcriptomics:
- In genome assembly, De Bruijn graphs are widely used. For an example of its application, check out my Han1 assembly, the first gapless Southern Chinese Han genome.
- For pangenome indexing, I’ve contributed to making the Wheeler graph recognition problem computationally feasible through innovative heuristic methods combined with an SMT solver (Learn more).
- In genome annotation, I have utilized graph-based methods to stitch together fragmented DNA and protein alignments, thereby assembling them into more accurate annotations. (Learn more).
- My transcriptome assembly work focuses on modeling RNA-Seq data using directed acyclic splice graphs, with ongoing research into graph neural networks to decode the complexities of RNA splicing. (Learn more).
💻 As a staunch advocate for open-source software, I invite you to explore my NEWS page for the latest updates on my projects.
💬 Feel free to reach out to me for collaborations, discussions, or just to say hi! Coffee chat! ☕️
Selected Publication
- Kuan-Hao Chao*, K. Barton, S. Palmer, and R. Lanfear* (2021). sangeranalyseR: simple and interactive processing of Sanger sequencing data in R, Genome Biology and Evolution
- Kuan-Hao Chao*, A.V. Zimin, M. Pertea, S.L. Salzberg* (2023). The first gapless, reference-quality, fully annotated genome from a Southern Han Chinese individual, G3: Genes, Genomes, Genetics
- Kuan-Hao Chao*†, Pei-Wei Chen†, Sanjit A. Seshia, Ben Langmead* (2023). WGT: Tools and algorithms for recognizing, visualizing and generating Wheeler graphs, iScience
- Kuan-Hao Chao*, Alan Mao, Steven L Salzberg, Mihaela Pertea* (2023). Splam: a deep-learning-based splice site predictor that improves spliced alignments, bioRxiv
Selected Presentation
- RECOMB-seq Proceeding Talk, Research in Computational Molecular Biology on Biological Sequence Analysis, Istanbul, Türkiye, 2023, Link
- ISMB/ECCB Poster, Intelligent Systems for Molecular Biology / European Conference on Computational Biology 2023, Lyon, France, 2023, Link
- BDS Poster, Biological Data Science, Cold Spring Harbor, New York, 2022, Link
Education
- Ph.D. Candidate in Computer Science, Johns Hopkins University, Sep/2021 - Present
- M.S.E. in Computer Science, Johns Hopkins University, Sep/2021 - May/2023
- B.S. in Electrical Engineering, National Taiwan University, Sep/2016 - Jan/2021
Experience
- Research Intern, Kelley lab, Calico, Expected May/20204 - Aug/2024
- Research Assistant, Institute of Information Science, Academia Sinica, Jul/2020 - Jan/2021
- Research Student, Research School of Biology, The Australian National University, Jul/2019 - Jun/2020
- Research Student, Centers of Genomic and Precision Medicine, National Taiwan University, Aug/2018 - Jul/2019
Selected open-source software
- Splam, splice site predictor
Code Documentation Poster Paper Cite - LiftOn, annotation lift-over tool
Code Documentation Paper - sangeranalyseR, R package for analyzing Sanger sequence Code Documentation Poster Paper Cite
- Wheele Graph Toolkit Code Poster Paper Cite
Side Projects
- Biobaby, Unity WebGL game, Play it now!
- Flappy penguin, Unity WebGL game, Play it now!
- Tank fire, Unity WebGL game, Play it now!
Teaching
- Johns Hopkins University
- EN.580.458 / 658 Computing the Transcriptome, Teaching assistant, Spring 2023
- National Taiwan University
- CSX 4001 Data Science Programming, Teaching assistant, Spring 2019
- EE 1006 Cornerstone EECS Design and Implementation, Teaching assistant, Fall 2018
Service
- Reviewer
- Chromatographia: 2023
- G3: Genes, Genomes, Genetics: 2024
- BMC Bioinformatics: 2024
- International Society for Computational Biology (ISCB): 2024
- Sub-reviewer
- Genome Research: 2024
- Nature Machine Intelligence: 2023
- G3: Genes, Genomes, Genetics: 2022