The research in my lab is 100% computational. We use data sets derived from unique collaborations combined with publicly available sources to inform molecular mechanisms of human diseases. Our work prioritizes molecular targets for experimental follow-up studies which are conducted in collaboration with other researchers.
Chronic obstructive pulmonary disease (COPD), coronary artery disease (CAD) and heart failure (HF) are major causes of death in the United States and worldwide. We are performing integrative analysis of genomic (whole genome sequence), transcriptomic (bulk and single cell RNA-seq) and high-throughput proteomic data from multi-ancestry studies using novel statistical approaches and predictive algorithms to identify causal genes and pathways contributing to risk of these diseases. The student would work closely with the mentor and other team members to learn how to perform the relevant statistical genetic and high-dimensional data analyses. Our work will include hands on application of the latest statistical tools using R, python and other customized genetic and bioinformatic tools within the Unix environment. The student will also be expected to contribute actively to literature review, critical interpretation of data analysis results, scientific writing and presentations.
- Some experience and/or coursework involving basic statistical programming including R and/or python
- Experience and/or willingness to learn how to use a high performance computing environment to submit long-running compute jobs using a command line
- Interest in working with a team and learning how to interpret data analysis results in the context of biological/clinical expertise
- Willingness and interest in working with an in-person team
- Reading of relevant literature in adult human heart/lung diseases
- Reading of literature in high-dimensional molecular 'omics and related statistical and/or bioinformatic methods
- Ability to learn new packages for analysis of high-throughput molecular 'omics data
- Ability to interpret, synthesize and communicate results from analysis of high-dimensional data in collaboration with a team of researchers