Job Location: New York, NY
529540
Medical Center
Herbert Irving Comprehensive Cancer Center
Full Time
Grade 104
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Position Summary
Columbia University JP Sulzberger Genome Center and Herbert Irving Comprehensive Cancer Center are seeking a highly motivated Bioinformatics Analyst with strong expertise in bioinformatics/biostatistics and single-cell RNA sequencing and an interest in GI cancer biology. The Genome Center is equipped with state-of-the-art high-throughput sequencing instruments, a Single Cell Analysis Core with equipment for gene expression profiling in individual cells, and a cancer biostatistics group with the expertise in statistical analysis of high dimensional genomic data. It has extensive collaborations with GI cancer investigators at Herbert Irving Comprehensive Cancer Center. This position specifically involve the application and development of statistical/computational methods for analyzing single-cell RNA sequencing data, focusing on custom bioinformatics projects for GI cancer investigators at the Cancer Center.
This position reports to Peter Sims, Ph.D. (Associate Professor of Systems Biology and Biochemistry & Molecular Biophysics, Director of Single Cell Core), Jianhua Hu, Ph.D. (Professor of Biostatistics, Director of Cancer Biostatistics Shared Resource), and Anil Rustgi, MD (Irving Professor of Medicine, Director Herbert Irving Comprehensive Cancer Center)
More information can be found at https://systemsbiology.columbia.edu/genome-center/single-cell-analysis-core
The successful applicant would jointly join the Single Cell Analysis Core bioinformatics team and cancer biostatistics group as the lead bioinformatician for GI Cancer investigators and be housed in the Department of Systems Biology, a vibrant hub for interdisciplinary research.
Responsibilities
Maintain and improve robust and highly automated computational pipelines for analyzing and storing single cell RNA-Seq, single nuclei ATAC-Seq, and supporting data.
Analyze and integrate newly reported computational tools. This includes recently described computational methods for regulatory network inference and analysis (e.g. VIPER, metaVIPER, ARACNE), matrix factorization methods (e.g. scHPF), and trajectory analysis tools (e.g. topological data analysis, Monocle3).
Work with sequencing team to improve Standard Operating Procedures and troubleshoot experiments. This includes both 3’- and 5’-end implementations of single-cell RNA-seq with the 10x Genomics Chromium platform, T cell receptor repertoire profiling with the 10x Genomics Chromium platform, single-cell RNA-seq with plate-based whole transcriptome amplification, and single-cell ATAC-seq with the 10x Genomics Chromium platform.
Provide advice in study design and analytical approaches to GI cancer center investigators and their lab members. Interact with lab personnel to explain the experimental results and to develop further analyses based on user feedback.
Carry out data analysis in strategic research projects, including new technology development and collaborations; participate manuscript writing.
Develop new and effective statistical/machine learning methods to address various challenges in analyzing (spatial) single-cell RNA-seq data.
Develop software for new single-cell RNA-Seq applications. This includes analytical tools for unsupervised clustering, data visualization, differential gene expression analysis, batch effect correction, and dimensionality reduction.
System administration for the pipeline computing environment by monitoring the running processes; tracking capacity and throughput; architect storage platform; upgrade and install software when necessary.
Develop and automate analytics pipeline for spatial transcriptomics analyses, including integration of imaging data with sequencing data and analytical tools as described above.
Minimum Qualifications
Requires a bachelor’s degree or equivalent in education and experience plus 3 years of related experience.
Preferred Qualifications
Graduate degree in Computational Biology, Bioinformatics, Genetics, Biostatistics, or equivalent.
Experience with pipeline development or automation using Python.
Other Requirements
Bachelor’s Degree in Computational Biology, Bioinformatics, Genetics, Statistics, or equivalent in education and experience, plus three years of related experience.
Demonstrated experience in high-throughput sequencing data analysis or method development.
Excellent communication skills and teamwork.
Programming: Fluency with Python, R, and UNIX. Experience with source code version control systems. Experience with AWS.
Strong creative thinking and problem-solving skills.
Equal Opportunity Employer / Disability / Veteran
Columbia University is committed to the hiring of qualified local residents.
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