Featured RNA-Seq job – Computational Biologist – single cell multi-tissue, multiomics

Job Description

We are looking for a highly motivated and talented individual with a computational/statistical background to lead data analyses for two ambitious projects the Developmental Genotype-Tissue Expression (dGTEx) Project (www.genome.gov/Funded-Programs-Projects/Developmental-Genotype-Tissue-Expression) and the recently launched companion project in non-human primate species (NHP dGTEx). The goal of this effort is to map and study gene expression and regulation across multiple single cells and tissues towards understanding normal development and provide insights into disease processes that have origins in pediatric development. The project(s) aims to identify the specificity and variation of genetic regulation of gene expression across cell types, tissues, organs, species and time. The project will contribute to global efforts including the Human Developmental Cell Atlas (HDCA, https://www.humancellatlas.org/dca/) and the CZI-funded Pediatric Networks for the Human Cell Atlas (humancellatlas.org).

The successful candidate will join an interdisciplinary team working with an unprecedented set of data from a wide range of human tissues and donors, including extensive single cell multiomic, and spatial transcriptomic, data as well as bulk RNA and whole genome sequence data. The scope of this project provides unique opportunities for developing novel analytical methods for data QC, integration, mapping of quantitative trait loci, multi-tissue analyses, and integration with epigenetic and GWAS data sets.

Overall Responsibility

The computational scientist will create and oversee the implementation of experimental work plans, pipelines for data processing, organization, and analysis, and contribute to budgetary and logistical considerations. This role will require strategic coordination of multiple groups at the Broad Institute (e.g. the Gene Regulation Observatory and Cancer Genome Analysis groups) and elsewhere. This individual will serve as a key contact point for project leaders, international collaborators of the project, funders, and junior staff.

Principal Duties and Responsibilities

• Design and execute data analysis strategies involving multimodal datasets, and specifically lead single-cell and spatial transcriptomic data analyses.
• Together with others, develop new methodologies and/or evaluate new methods for integrative analysis of functional and genomic data.
• Apply or develop state-of-the-art computational tools and pipelines to a) assess data quality, b) integrate diverse data types and metadata, and c) quantify developmental and genetic effects on gene regulation.
• Work closely with project team to design, execute, and analyze experiments critical to the development of single-cell pipelines from primary human, and non-human tissues.
• Collaborate with internal technology development efforts and scientists towards the application of high-throughput single cell multiomic mapping strategies to diverse biological models.
• Present ideas and results to the multi-disciplinary members of the dGTEx/NHP dGTEx consortia.
• Mentor junior staff.

Qualifications

• A PhD in Computer Science, Physics, Statistics, Math, Engineering, or a related quantitative discipline is required.
• Substantial experience analyzing single-cell/-nuclei RNA-seq and multiomic data is required.
• Experience with computational analysis, algorithm development, statistics, or machine learning.
• Proficiency in at least one modern programming language. Experience with a scientific programming environment, such as python, Julia, R or MATLAB, is preferred.
• Proficient in design and application of analysis pipelines, and able to quickly learn and adapt computational tools for novel analyses. Experience with cloud computing is a plus.
• Strong communication skills, with ability to effectively communicate with specialists and non-specialists.
• Background in human genetics or biology is a plus.