Job Description

We are seeking an experienced bioinformatician for the Tisch Cancer Center Bioinformatics for Next Generation Sequencing (BiNGS) shared resource facility, to lead transcriptomics and epigenetics data analysis projects in Cancer biology.

More specifically, we are working on multiple projects together with PIs in the Tisch Cancer Center and the broader Mount Sinai community to analyze NGS related datasets. For example, we are working to understand the role of RAS mutations in the development of Leukemia utilizing single cell RNA- and ATAC-seq; understand the role of histone variants in cancer development utilizing enhancers analysis, transcriptomic analysis, open chromatin analysis and transcription factor network analysis; in another project, we are utilizing bulk and single cell, and spatial transcriptomics to understand breast cancer metastasis to bone. Finally, we are utilizing FIBER-seq (third generation PacBio long read sequencing), to better understand how chromatin remodelers shape the chromatin landscape.

We are seeking a highly motivated senior Bioinformatic Scientist who wants the opportunity to significantly impact the growth and success of our research programs, the bioinformatics core and the services we provide. The candidate will work closely with investigators to facilitate and enhance the processing of their projects. Commitment to accuracy, high attention-to-details, and ability to work independently are critical competencies for the role. The ability to lead projects and a team of bioinformaticians is a must. We feel that BiNGS presents a unique opportunity for senior bioinformaticians who seek to expand their data analysis skills; their understanding of transcriptional and epigenetic programs and their role in driving cancer initiation, progression and metastasis, and other diseases; their ability to communicate science clearly and efficiently; their ability to lead a small team of junior of bioinformaticians, and their involvement in reducing racial bias in science through mentorship of URM in Science.

Responsibilities

• Execute computational analysis for bulk RNA- and ATAC-seq, scRNA-seq, scATAC-seq, and scMultiome.
• Developing, tracking and reporting of ongoing projects including presentation of analysis progress in internal meetings and communications with PI to present results, interpret data and come up with new hypotheses.
• Integrate data across different datatypes and from publicly available resources (e.g., ENCODE and TCGA), with experimental data generated by PIs. For example, correlation with gene expression signatures, and Histone PTM enrichment analysis. The ideal candidate will be able to navigate the available datasets and extract meaningful data based on the system he/she works on.
• Provide analyses and visualization for presentations and publications.
• Participate in the implementation of new software tools (e.g., MOFA and SNF) to process, analyze and visualize high-throughput multidimensional sequencing data. The candidate will be required to identify the main requirements based on relevant questions, identify available tools, implement and test.
• Manage data and interactive tools on local HPC and commercially available clouds preferably Amazon Web Services. The candidate will execute all data analysis on HPC using bash and manage the storage and archiving of all NGS datasets on the MSSM HPC. Further managing of our HTML reports will be done on Amazon cloud.
• Provide training to TCC investigators and trainees.

Qualifications

• M.S. in Bioinformatics, Biomedical Informatics, Computational Biology, or Genomics. Alternately, M.S. in a discipline requiring strong computational and analytical skills supplemented with some biology exposure. Ph.D in a related field preferred. Those with a Bachelors degree and additional post-graduate experience are considered.
• 2+ years post-graduate experience in a research environment, including the manipulation of large biological datasets.
• Advanced knowledge of genetics and/or statistical analysis software and online resources. Experience in programming environments such as MatLab, R statistical package, BioConductor, Perl and C++.

Preferred Skills

• MSc or PhD in bioinformatics, computer Sciences or related fields.
• Proven experience in analyzing bulk RNA- and ATAC-seq, scRNA-seq, scATAC-seq, and scMultiome.
• Proven experience with Python, R and Linux. Additional experience with standard genomics tools for high-throughput sequencing data analysis (e.g.
• Bowtie2, Cellranger, Samtools, STAR, MACS2, Seurat, Signac, Cicero, Chromvar, Scenicplus and the UCSC genome browser).
• Experience with markup languages such as HTML is preferred.
• Proven ability to work on the AWS cloud.
• Experience in bash HPC cluster environment with a parallel file system.
• Proven ability to research, analyze, recommend, communicate, and implement solutions.
• Good understanding of chromatin biology, and NGS technologies.
• Must be able to work as an individual while part of a small team.