The Abraham lab studies gene expression-regulation mechanisms in healthy and diseased mammalian cells. We are recruiting computational biologists to collaboratively develop computational tools and frameworks to analyze high-throughput sequencing (-omics) data. We build analytical software pipelines to find answers to biological questions about gene regulation in big datasets, usually from applied sequencing experiments like ChIP-Seq, RNA-Seq, and Hi-ChIP. Our interests center on enhancers and super-enhancers. Specifically, we seek to understand how these regulatory elements establish gene expression programs in healthy cells, and how enhancers are altered by mutation, abused by mistargeting, and targetable with drugs in diseased cells. We focus on characterizing the core regulatory circuitries driving disease-relevant cells, and on understanding how mutations in the non-coding DNA of such cells can drive disease, including cancers, through gene misregulation.
The successful candidate will become a fundamental component of a multidisciplinary, inter-institutional team assembled to study how genome structures meaningfully differ between normal and pediatric cancer cells.
Ideal candidates will have experience building, tailoring, and deploying analysis pipelines using widely available genomic analysis toolkits (e.g. bedtools, samtools), as well as experience managing large numbers of datasets. The successful candidate will be tasked with collaborative research within and beyond the lab, so strong communication and interpersonal skills are essential. Additional experience in fundamental understanding of gene expression mechanisms (e.g. transcription factors, enhancers, genome structure, and transcriptional condensates), and experience building succinct, clear figures using R are preferred.
The department of Computational Biology provides access to high performance computing clusters, cloud computing environment, innovative visualization tools, highly automated analytical pipelines and mentorship from faculty scientists with experience in data analysis, data management and delivery of high-quality results for competitive projects. We encourage first author, high profile publications to share this element of discovery.
Take the first step to join our team by applying now!