Interactive, exploratory visual analysis of genomic data
Statistical and computational methods for high-throughput genomics.
Interactive data analysis. Metagenomics. Cancer epigenomics. Transcriptomics.
What we work on
Our research focuses on efficient and effective interactive analysis of high-throughput genomic data. We develop new methods and tools from multiple areas in the computational and statistical sciences: basic bioinformatics/biostatistics, statistical and machine learning, data visualization and management, and numerical optimization. Applications include cancer epigenetics, metagenomics, pre-processing of measurements from high-throughput assays and disease risk models that integrate high-throughput genomic and other data.
Interactive, exploratory visual analysis of metagenomic data
Analysis tools for large high-throughput metagenomics sequencing projects
Methods for analysis of DNA methylation from second-generation sequencing and microarrays.
Simple models of intensity measurements in second-generation sequencing data give easily interpretable quality assessment metrics while capturing uncertainty...