Associate Data Scientist (Genomic) - Epigenetics & Molecular Carcinogenesis (Ting Laboratory)

The Department of Epigenetics and Molecular Carcinogenesis is an interdisciplinary, basic science research department that conducts groundbreaking research aimed at defining the mechanisms controlling normal cell growth, proliferation, differentiation, and survival as well as mechanisms controlling genome maintenance to identify the aberrations in these processes that drive cancer.

Epigenetics and Molecular Carcinogenesis

Epigenetic gene regulation is important for both normal development and disease states. In cancers, aberrant promoter CpG island hypermethylation correlates highly with gene inactivation and can account for lack of gene expression where mutations do not exist. Dr. Ting is interested in dissecting the mechanisms of epigenetic gene silencing and understanding the functional relevance of DNA methylation in diseases. Dr. Ting's lab has three major focus areas:

• Pioneering technical and computational tools for genome-wide DNA methylation assay. Dr. Ting developed MBD-isolated Genome Sequencing (MiGS), which is a cost-effective technique to survey whole genome DNA methylation patterns. The Ting lab also develops computational tools that facilitate sequencing data analyses and interpretation.
• Defining novel and clinically relevant functions for DNA methylation. We have defined genome-wide abnormal DNA methylation patternsin colon, bladder, and prostate cancers. Knowing where these disruptions occur enabled us to generate and test hypotheses regarding the function of these changes. We investigate both gene promoters and non-promoter regions with the goal of defining context-specific functions of DNA methylation.
• Delineating the mechanics of DNA methylation in cancer. Wide-spread disruptions to DNA methylation patterns are contribute to tumorigenesis and progression, but the regulatory mechanisms that establish, maintain, and modify these patterns are still being worked out. Researchers in Dr. Ting's lab explore the roles for epigenetic enzymes, small non-coding RNAs, and environmental exposures in the initiation and maintenance of abnormal DNA methylation patterns in cancer.

KEY FUNCTIONS

Statistical Analysis

• Under supervision, compiles, analyzes and reports statistical data for various projects including, but not limited to manual and computer-aided data abstraction and evaluation, computerized imaging and bioinformatics.

• Assesses relevant literature as well as existing data, evaluates the quality of data used in reports and assists with preparation and distribution of data for committee and scientific meetings.

Computational skills

• With direction, develops and maintains software tools and infrastructure for resolving specific problems.

• Performs gene expression data analysis / transcriptomics and genomic data analysis.

• Performs analysis of cancer genome datasets such as TCGA, ICGC and dep map with an emphasis on translational research.

• Responsible for the integration of large scale bulk sequencing and single cell sequencing multi-omic datasets.

Research Support

• Contributes to the development of procedures and makes recommendations on new procedures.

• Explore and learn new technologies relevant to lab research projects.

• Co llaborate with PIs and other research, computational, scientific or support staff to accomplish project focus or results.

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: Two years experience in scientific software development/analysis.

Preferred: Experiences in genomic data analyses, such as single cell RNA-seq, RNA-seq, ChIP-seq, and HiC-seq.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html