Single-Cell Cancer Genomics Bioinformatics Research Associate

Washington University School of Medicine
Neurosurgery, Medicine, and Genetics
United States MO St. Louis


A Bioinformatics Research Associate position is available at Washington University School of Medicine in St. Louis. The bioinformatician will use computational approaches to analyze, integrate, and interpret large-scale genomic data -- including whole genome sequencing data, single-cell RNA-sequencing data, and spatial transcriptomic data -- to study intratumoral heterogeneity and the immune microenvironment in cancer. Current projects address topics such as the interplay of genetic, transcriptional, spatial, and immunological heterogeneity in brain tumors such as glioblastoma. Opportunities to participate in ongoing group projects and to publish are plentiful. The bioinformatician will work in a highly collaborative environment, involving extensive interaction with faculty, molecular biologists, computational biologists, programmers, and clinicians.

Essential responsibilities:
1. Analyze WGS and exome data using pre-existing pipelines on a local linux cluster and in the cloud.
2. Implement and develop quantitative methods to analyze, interpret, and visualize single-cell RNA-sequencing data.
3. Integrate genetic and epigenetic information about tumor samples, including mutation, gene expression, and methylation data.
4. Develop and perform integrated pathway and gene network analyses related to cancer.
5. Communicate with colleagues and collaborators.
6. Read the literature and contribute to scientific publications.


Requires at least a bachelor’s degree in computer science, statistics, computational biology, applied math, physics, bioinformatics, genomics, or related field plus 4 years of experience; OR a master’s degree plus 2 years of experience; OR combined education and experience of 9 years. Ph.D. preferred.
• Proficiency in the *nix shell and at least one programming language (Python, R, Perl, C, etc).
• Experience with statistical software packages in R and/or Python.
• Experience with quantitative analysis of large, multidimensional data sets, particularly gene expression and/or next-generation sequencing data.
• Working knowledge of genetics and cell biology.
• Interest in Machine Learning.
• Strong oral and written communication skills.
• Ability to solve scientific and quantitative problems creatively.
• Ability to work effectively in a team.

Start date

As soon as possible

How to Apply

Interested candidates should submit their curriculum vitae, statement of research interests, and contact information for three references via e-mail to Dr. Allegra Petti (

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