A Lead Bioinformatics Programmer position is available in Dr. Bing Zhang’s laboratory to work on Next Generation Sequence (NGS) data analysis. Our research is well-funded by the National Cancer Institute (NCI), the Cancer Prevention and Research Institute of Texas (CPRIT), and the McNair Medical Institute at The Robert and Janice McNair Foundation. We are involved in several large projects that produce multidimensional omics data at DNA, mRNA, protein, and phenotype levels from human tumors, patient-derived xenografts (PDXs), and cancer cell lines. The candidate will lead the NGS data analysis effort and will work with other team members to integrate NGS data with proteomic and clinical data to drive precision oncology discovery and practice.
The position offers an excellent opportunity to conduct research in a supportive and stimulating environment, and to collaborate with bioinformaticians, biostatisticians, computer scientists, biologists, and clinicians.
ESSENTIAL FUNCTIONS AND DUTIES
- Prepares detailed specifications and algorithms for exome sequencing and RNA-Seq data analysis from which programs and workflows will be implemented.
- Researches, assesses, imports, configures, and customizes existing bioinformatics software.
- Designs, codes, tests, debugs, and maintains programs and workflows for NGS data analysis.
- Applies bioinformatics tools and workflows to both well-defined and complex NGS data analysis problems.
- Generates data in the right format that can be used for downstream pathway and network analysis and proteogenomic data integration.
- Prepares detailed documentations for implemented tools and workflows
- Provides technical guidance, work direction and training to other lab members on NGS data analysis.
- Performs other job-related duties as assigned.
- Bachelor's degree in Computer Science or Biological Sciences.
- Five years’ experience in NGS data analysis with application to human cancer studies or a Master's degree in Computer Science or Biological Sciences and 3 years’ experience in NGS data analysis.
- Programming experience in UNIX/LINUX environment
- Proficiency in scripting languages such as Python, Perl, Bash, and R
- Strong communication skills
- Demonstrated experience with pipeline and workflow development
- Good understanding of statistical techniques for differential expression, clustering, classification, and gene set enrichment analysis
- Experience with version control tools such as git
- Experience with Amazon Web Services