A bioinformatics specialist position is available for highly collaborative research involving human aging and cancer precision medicine. This position provides the opportunity to work closely with investigators at the California Pacific Medical Center (CPMC) Research Institute and University of California, San Francisco (UCSF). The bioinformatics specialist will perform statistical modeling and integrative analysis of genome-wide SNP data, next-generation sequence data, and expression data in the Longevity Genomics Research Group (www.longevitygenomics.org) and the Cancer Avatar Group (www.cpmcri-currents.org) to identify causal genetic relationships with aging and cancer leading to novel therapeutics or interventions. This research program makes use of genetic, clinical, and phenotypic data from several human aging cohorts totaling 50,000 participants and CPMC patient clinical and genomic data generated from primary tumors, preclinical models, and electronic health records among patients.
This position provides the opportunity to lead and support analysis projects to identify longevity-associated genes in large human cohort studies and to identify novel therapeutics for treatment of several highly malignant cancers. Tasks will involve management of next-generation sequencing data and SNP genotypes imputed to large reference panels (1000 Genomes and HRC). Statistical modeling will include linear and logistic regression, survival analysis, and longitudinal analysis using mixed effects models. The bioinformatics specialist will be closely involved in a large-scale discovery project using RNA-seq and next-generation sequencing for cancer genome characterization and pharmacogenetic profiling of drug response in preclinical models of cancer.
- Bachelor’s or Master’s degree in statistics, genetics, mathematics, biology, computer science, bioinformatics, or a related field. Experience in human genetics and/or genetic epidemiology is a plus.
- Experience in statistical analysis, including regression modeling, meta-analysis, and cluster analysis.
- Experience with gene expression analysis using R/Bioconductor and/or GenePattern.
- Familiarity with gene and disease ontologies, pathway databases, and databases of drug properties.
- Familiarity with computational biology/bioinformatics techniques to link human cellular networks to disease networks and pharmacogenomics databases.
- Familiarity with common file formats, such as vcf, bam, fastq, bed, and the specialized tools designed to work with them.
- Expertise in the R statistical programming language. Should be able to write functions and should be familiar with data.table package and Bioconductor. Experience with reproducible research practices, such as markdown, github, and R package development. Experience building Shiny apps is a plus.
- Experience with Bash, shell scripting, and use of high performance compute clusters.
- Experience with Web frameworks, such as Django.
- Familiarity with interrogating public databases relevant to cancer (e.g., TCGA).
How to apply
Email your CV, cover letter, and reference letters to Dan Evans and Greg Tranah.
devans [at] psg [dot] ucsf [dot] edu
gtranah [at] psg [dot] ucsf [dot] edu