To study the molecular mechanisms of drug addiction and depression, a lot of high throughput experimental data have been generated. Our lab focuses on the statistical and integrated analysis of these data and generates biological insights. We are especially interested in the next-generation sequencing data which provides a comprehensive and high-resolution view of the biological systems under study. We also develop bioinformatic tools employing Bayesian inference and machine learning to aid our data analysis.