The Duke-NUS Centre for Computational Biology provides a nucleus for expertise in computational biology and bioinformatics within Duke-NUS and SingHealth.
Biomedical Research that depends heavily on computational biology and bioinformatics and research on methods for computational biology and bioinformatics. We pursue research projects that have substantive scientific impact and have published > 250 scientific papers since 2013.
• We supervise students in the Duke-NUS PhD programme Integrated Biology and Medicine and the Duke-NUS PhD programme Quantitative Biology and Medicine.
• We offer the Bioinformatics Seminar Series and tutorial talks on computational biology and bioinformatics
• We offer an annual week-long RNA-seq and ChIP-seq analysis workshop
• We provide one-to-one mentoring of faculty, postdoctoral fellows, research staff, and students in computational biology and bioinformatics
Collaborative Research, including providing a source of computational biology and bioinformatics expertise for funding proposals. We provide computational biology and bioinformatics expertise and collaboration within Duke-NUS and SingHealth. In some cases, our faculty may be an integral part of the scientific team (e.g. co-investigators on grant applications). In other cases, Duke-NUS or SingHealth investigators can approach the Director or Centre staff to engage, on a part-time basis, non-faculty Centre staff to support the conduct of their projects. We work closely with the Duke Genome Biology Facility.
Areas of expertise:
Next generation sequencing study design and analysis, including
• Genetic variation discovery and analysis (in germ-line and in cancers)
• RNA seq
• CHIP seq
Microarray study design and analysis
• Gene expression, microRNA, DNA methylation, SNP chips
• Elucidation of molecular signatures and modeling for diagnostic and prognostic purposes
Integrated analyses of genomics data for system biology approaches
•“Pathway” and “gene set” enrichment analysis
•Genomic approaches for therapeutics
•Other genomics and genetics analyses