Specialist Computational Biology in translational breast cancer research

Assistant Specialists MUST have an MSc in Bioinformatics or Computational Biology or related program or a bachelor's degree in Bioinformatics or Computational Biology or related program with three or more years of research experience. Associate Specialists MUST have an MSc in Bioinformatics or Computational Biology or related program or five to ten years of relevant experience. Candidates must have several years of experience including machine learning and programming, track record of completing and looking to apply their skill set in a clinical setting for translational research, preferably breast cancer.

The main responsibilities will be to analyze next-generation sequencing data from serial samples of breast cancer patients with significant residual disease after neoadjuvant targeted and/or chemotherapy from the I-SPY 2 TRIAL. I-SPY 2 is a multicenter standing platform trial for women with high-risk early-stage breast cancer. The primary endpoint is pathological complete response (pCR), defined as the absence of invasive tumor in the breast and the nodes at the time of surgery. I-SPY 2 is a biomarker-rich trial, where tissue and blood samples and MRI-imaging parameters are collected serially over the course of therapy. The candidate will work in the context of the I-SPY 2 Bioinformatics and Biostatistics Core (UCSF Core Lead: Christina Yau, and Computational Scientists Denise Wolf and Rosalyn Sayaman) of a funded P-01 proposal on Project 3, and the I-SPY 2 Biomarker Data Repository hosted by the trial sponsor Quantum Leap Healthcare Collaborative (QLHC, Director Biomarker Innovation: Annuska Glas) to evaluate the biological features characterizing residual disease and the evolution of these characteristics to potentially inform treatment decisions (Project PI: van' t Veer, P-01 PI: Esserman and Hylton). Candidate is expected to support the P-01 work and is encouraged to develop additional projects (e.g., novel algorithms for integrated analysis of our wealth of I-SPY 2 biomarker data and related projects).

Experience processing next-generation sequencing data, particularly whole exome sequencing data from matched normal and tumor samples. Candidate should be familiar with pre-processing steps (including alignment, recalibration, mark duplicates, realignment), and variant calling and copy number analysis. Analysis of tumor mutational burden analysis preferred.

Additional roles may include data management of I-SPY2 data, including maintenance of data structures and manifests in the Cancer Center C4 high-performance environment; user administration of I-SPY2 Cancer Center C4 storage and compute servers in collaboration with C4 bioinformatics core; and data transfer to Amazon S3 storage server, or similar systems, in collaboration with Quantum Leap Healthcare Collaborative (QLHC administrators.

Candidate must have direct experience working on projects utilizing current methodologies to perform the following analyses employing certain systems and tools:

• Next-generation DNA sequencing pre-processing pipelines (including alignment and mutation calling and QC)
• Clonality analysis of serial samples, including phylogenetic trees
• Copy number analysis based on sequencing data
• Functional significance analysis of somatic alterations
• R-coding in biological and clinical context
• Linux, command line, bash script, SAM tools usage
• Experience with high-performance computing resources such as Cloud, or similar systems, and running pre- and post-processing workflows in cluster environment
• Applying machine learning methodologies to biological problems
• Relaying information on next-generation pipelines and resulting data to other users

Preferred experiences include:

• A MSc in Bioinformatics or Computational Biology or related program
• Knowledge of Breast Cancer etiology and progression; drug resistance
• Working with RNA-sequencing data (including pre-processing)
• Familiar working with GATK, Picard, Mutect2, VarScan, FACETS and optional working with Slurm Workload Manager, AmazonS3
• Integrating DNA and RNA sequencing data for calling of expressed mutations
• Allelic frequency estimation
• Population structure inference modeling
• Integrating somatic alterations in a pathway-based approach

Please apply online at https://aprecruit.ucsf.edu/JPF05073, with a CV, cover letter, statement of research, and two references.

Contact for further information: Laura van 't Veer, PhD (Professor, Department of Laboratory Medicine, UCSF) at laura.vantveer@ucsf.edu

See Table 24B for the salary range for this position. A reasonable estimate for this position is $59,200-$82,100.

• 2 required (contact information only)

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.

UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.