Bioinformatics Scientist - Genetics & Bioinformatics
Language : English, Arabic is a Plus
Years of Experience : 4-5 Years Post-Doctoral Experience in the Analysis of High Throughput Next-Generation Sequence Data
Closing Date : 22 August 2021
Genetics and Bioinformatics department endeavors to delineate the genetic basis of the different types of diabetes and other metabolic disorders. The department performs genome-wide genotyping, whole-exome/genome sequencing, transcriptome-wide profiling, and epi-transcriptome-wide profiling on deeply phenotype cohorts. Population-based as well as family-based genetic association approaches are being performed. The department is committed to develop functional genomics approaches followed by translational biology.
- Playing a key role within the Genetics and Bioinformatics department in carrying out research projects dealing with analysis of genome-wide genotype, whole-exome/genomes, transcriptome, and epi-transcriptome data to reveal novel targets for therapeutic development.
- Executing projects to delineate the genetic basis of metabolic disorders with identification of risk factors and drug targets as outcomes.
- Participating in a direct development of data analysis pipelines with genome-wide genotype, whole-exome/genomes, transcriptome, and epi-transcriptome data.
- Developing multi-disciplinary approaches by way of interaction with researchers and resources from various departments of the institute.
- Analyzing internal data sets and publicly available large-scale genetics datasets by way of working together with other members of the department.
- Perform other tasks/duties within given deadlines as assigned by Head of Unit.
Required Skills and Expertise:
- 4-5 years post-doctoral experience in the analysis of high throughput next-generation sequence data.
- Demonstrated experience in the interpretation of genetics/genomics data in the context of gene/protein networks and pathways.
- Working knowledge of statistical genetics.
- A good record of peer-reviewed publication with 5-6 first-authored publications.
- Familiarity with methodologies and approaches for both population-based and family-based genetic association studies.
- Experience with software, such as BWA, GATK and genetic variant annotation software, in relation to NGS data analysis pipelines.
- Solid knowledge of PERL or C or Python or R, and Unix shell. API scripting to access external genome resources.
PhD in bioinformatics or Computational Biology or Statistical Genetics.