The Ho Laboratory focuses on the use of bioinformatics and systems biology approaches to tackle longstanding problems in basic and translational medicine. Most projects in this laboratory involve integrative analysis next-generation sequencing (NGS) data such as single-cell RNA-seq, ChIP-seq, and whole genome sequencing data. Here are some major research themes. Multiple projects are available under each theme. Students interested in joining his lab please contact Dr Ho.
- Bioinformatics algorithms for single cell RNA-seq analysis. Single-cell RNA sequencing (scRNA-Seq) enables researchers to study heterogeneity among tens of thousands of individual cells and define cell types from a transcriptomic perspective. However, fast and reliable analysis of these large and noisy data requires new statistical and computational considerations. In this project we will develop cutting-edge bioinformatics methods to analyze a range of scRNA-seq data to answer important biological questions.
- Scalable 3D virtual reality visualization of biological data. Visualizations of biological data is critical in the analysis and interpretation of large biological data, such as single-cell RNA-seq data and 3D biomedical imaging data. In this project, we will use state-of-the-art virtual reality (VR) technology to construct effective and scalable 3D visualizations of various biological data. This project is ideally suited for students who have an interest in large-scale data visualization and virtual reality.
- Wearable device, physical activity and heart rate dynamics. Being able to track the change in cardiac function in real time under a person's realistic physical activity profile is now feasible due to the wide availability of consumer-grade wearable devices (e.g., fitbit, AppleWatch, etc). Our group is developing new big data machine-learning algorithms extract, de-noise, analyze and correlate physical activity data and heart rate dynamics. Our long-term goal is to establish new non-invasive screening tools to monitor cardiac function and disease risk.
- Causal disease mutation identification in whole genome sequencing data. Whole genome sequencing is now highly cost-effective. Nonetheless, while a large number of sequence or structural variants can be identified in each individual, it is often difficult to pin-point the disease causing genetic mutation. In this project, we will develop novel bioinformatics methods to integrate diverse functional genomic data to prioritize likely causal mutations that underlie a disease. Our group is particularly interested in the genetic cause of congenital heart disease.
- A cloud-based approach for incorporating scalability in genome informatics. NGS enables low-cost, high-throughput sequencing for a wide variety of genome-wide scale analysis of the genome, epigenome and the transcriptome. However, with this vast quantity of data, we are faced with unprecedented technical challenges in terms of computational analysis and storage of these data. The goal of this research project is to investigate the use of cloud computing technology to deal with these challenges.
For postdoc/students/RA who wants to join this laboratory: All projects require proficiency in at least one programming/scripting language (R, Perl, Python, Java, C++, C) Familiarity with the Unix operating system is desirable but not required. Individual project can be tailored to fit each student's personal interest and skill set. Most projects involve close interactions with local and international collaborators. This is a highly interdisciplinary laboratory. We welcome perspective group members from diverse background, such as medicine, biology, physics, computer science, mathematics, statistics, and engineering. Expression of interest, along with your CV, can be sent to Dr. Ho.