Senior Computational Biologist
Stanford University is seeking a Computational Biologist/ Biostatistician 1 to join The Translational Genomics of Diabetes (TGD) Lab which is headed by Dr Anna Gloyn. The overall research focus of the lab is to use human genetics to understand pancreatic islet dysfunction in type 2 diabetes and related disorders. Current projects involve integrating diverse genetic, epigenomic and cellular datasets to identify causal variants and effector transcripts at GWAS loci for type 2 diabetes and related disorders. The TGD Laboratory headed by Dr Anna Gloyn is affiliated with the Division of Endocrinology in the Department of Pediatrics although the group are physically located at Porter Drive with research groups from the Department of Genetics including Mike Cherry and Mike Synder.
The Department of Pediatrics in the Stanford University School of Medicine is committed to advancing the health of infants, children, and adolescents through innovative medical care, research, training, and advocacy. With over 400 pediatricians and pediatric subspecialties, our department is one of the largest in the Stanford University School of Medicine. Ranked among the top in the nation by US News and World Report, we attract medical students, interns, residents, and postdoctoral fellows from around the world. In partnership with Lucile Packard Children's Hospital Stanford, Stanford Children's Health and other hospital partners and affiliations, our programs and services offer state-of-the-art primary and specialty care. Our researchers and clinicians collaborate across the bench-to-bedside-to-backyard continuum, transforming their discoveries into the most effective diagnostics, treatments and prevention therapies available today. The Division of Pediatric Endocrinology & Diabetes in the Department of Pediatrics, successfully combines the worlds of investigation, innovation, and clinical care to improve the diagnosis and treatment of endocrine disorders.
The Translational Genomics of Diabetes Laboratory
The group is now based in the Division of Endocrinology in the Department of Pediatrics at Stanford University after a very successful 16 years based at the University of Oxford, UK. We aim to understand the genetic basis of diabetes and related metabolic conditions and to use this to leverage a better understanding of what causes diabetes and how we can improve treatment options for patients. Our work is predominantly focused on understanding what causes pancreatic islets to release insufficient insulin to control blood glucose levels after a meal in patients with type 2 diabetes, but often extends to efforts to relate this to metabolic dysfunction in other relevant tissues such as fat and liver.
We are an inter-disciplinary team of basic and clinical scientists with shared interests in using molecular genetics as a tool to uncover novel biology. We use a variety of different approaches to address important challenges in the field, which range from studies that work genome wide to those which are focused on specific genes and even precise nucleotide changes to understand their impact on pancreatic islet biology.
We have developed a series of pipelines that use primary human islets and authentic beta-cell models which allow us to generate and then integrate complex genomic, transcriptomic and cellular datasets. We use state-of-the art genome engineering approaches combined with induced pluripotent stem-cells to study the impact of T2D-associated genetic variants on islet cell development and function. We are also funded to investigate the impact of T2D risk variants on pancreatic beta-cell function in vivo.
We are a highly collaborative team and work with multiple national and international consortia involved in efforts to understand the genetic basis of type 2 diabetes (DIAMANTE, T2DGENES, DIAGRAM) and related glycaemic traits (MAGIC). We are also part of several Innovative Medicines Initiatives (IMIs) efforts and Horizon 2020 initiatives, which are working to develop tools and frameworks to capitalize on genetic and genomic data. We are also part of the NIDDK funded Human Islet Research Network (HIRN) where we play a role in two of their initiatives. The Human Pancreas Atlas Program- T2 (HPAP-T2D) and the Integrated Islet Phenotype Program (IIPP). Our role is to support the genetic and genomic characterization of islets which are distributed for research and to support the genomic characterization of the pancreas’ phenotyped within the HPAP-T2D program.
Our work extends to playing a role in the interpretation of genetic variants identified in genes with known roles in monogenic forms of diabetes. We are part of the Clin Gen Expert Review Panel for Monogenic Diabetes where are expertise contributes to interpretation of coding alleles in glucokinase (GCK) and Hepatocyte Nuclear Factor 1 alpha (HNF1A). We are a number of on-going projects which are supporting efforts to better understand how to use exome-sequencing data in a diagnostic setting.
• Collaborate with experimentalists and computational biologists to develop and apply functional genomics techniques
• Design and implement generalizable algorithms and tools for analysis of biological data, including high-throughput functional genomics assays
• Evaluate and recommend new emerging technologies, approaches, and problems
• Design and lead independent projects
• Create scientifically rigorous visualizations, communications, and presentations of results
• Contribute to generation of protocols, publications, and intellectual property
• Maintain and organize computational infrastructure and resources
* - Other duties may also be assignedDESIRED QUALIFICATIONS:
• Suggested: Ph.D. in computational biology, genetics, computer science, statistics, math, molecular biology, or related field, or equivalent practical experience. Talented applicants of all levels are encouraged to apply.
• Demonstrated expertise in statistical methods in data analysis, preferably with applications to high-throughput sequencing or other biological assays
• Strong knowledge of molecular biology and genomics; wet-lab experience a plus
• Fluency in Unix, standard bioinformatics tools (Python, R, or equivalent), and a programming language (C/C++, Java)
• Support and train other lab members in computational biology and statistics
• Excellent communication, organization, and time management skills
• Creative, organized, motivated, team player
• A passion for science and sense of urgency to find new medicines to benefit patients
EDUCATION & EXPERIENCE (REQUIRED):
Master's degree in biostatistics, statistics or related field.
KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):
• Proficient in at least two of R, SAS, SPSS, or STATA.
• Skills in descriptive analysis, modeling of data, and graphic interfaces.
• Outstanding ability to communicate technical information to both technical and non-technical audiences.
• Demonstrated excellence in at least one area of expertise, which may include coordinating studies; statistical methodology such as statistical genetics, or informatics; database design (e.g., expertise in RedCAP or MySQL); graphical techniques (e.g., expertise in Illustrator).
CERTIFICATIONS & LICENSES:
• Frequently perform desk based computer tasks, seated work and use light/ fine grasping.
• Occasionally stand, walk, and write by hand, lift, carry, push pull objects that weigh up to 10 pounds.
* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.
May work extended or non-standard hours based on project or business cycle needs.
- Schedule: Full-time
- Job Code: 5521
- Employee Status: Regular
- Grade: H
- Department URL: http://pediatrics.stanford.edu/
- Requisition ID: 86815