SUMMARY
Genetic alterations are known cancer drivers, but not all patients harbor sufficient driver mutations to elicit the onset and progression of the disease. There also exist cancers such as pediatric tumors and several liquid cancers that carry very few genetic alterations, highlighting the contribution of non-genetic alterations to tumor formation. The Lee Laboratory seeks to elucidate how RNA aberrations impact the development and progression of cancer. We employ cutting-edge sequencing to uncover errors hiding in RNA and use multidisciplinary approaches including cell biology, biochemistry, molecular biology and protein biology to determine the pathogenic role of altered RNAs in malignant transformation. We hope to provide novel strategies for cancer diagnosis, prognosis, and therapeutic intervention.
The primary purpose of the Assistant Data Scientist is to provide bioinformatic support to ongoing research efforts and develop pipelines and novel methods for analysis of data from next generation sequencing.
Ideal candidate will have experience analyzing bulk or single cell RNA-seq data.
KEY FUNCTIONS
- Performs bioinformatics analysis of bulk and single cell RNA-seq datasets
- Maintains and develops pipelines to analyze 3′-seq data
- Develops methods to provide bioinformatic and statistical support for research projects
- Organizes, maintains and documents data, algorithms and software
- Willing to learn and establish new methods that are required for lab research
Other Skills
- Expertise in scripting and programming languages such as Python, PERL, Unix or R
- Knowledge of bioinformatics tools used in gene expression analysis
- Knowledge of biology, gene expression
- Excellent communication skills and team-working attitude
Other duties as assigned.
EDUCATION
Required: Bachelor’s degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Statistics, Computer Science, Computational Biology, or related field.
EXPERIENCE
Required: None.
Preferred: working experience analyzing bulk or single cell RNA-seq data.
