Summer Intern - Data Science (QCS)

Key Skills

API, Civil Engineering, Data pipelines, ETL frameworks, Git, JavaScript, Mathematics, NoSQL, NumPy, Pandas, Rust, SQL, Statistics

Job Description

The Chan Zuckerberg Biohub (https://www.czbiohub.org) is a one-of-a-kind independent nonprofit research institute that brings together three powerhouse universities - Stanford, UC Berkeley, and UC San Francisco - into a single collaborative technology and discovery engine. CZ Biohub itself supports some of the brightest, boldest engineers, data scientists, and biomedical researchers to make breakthroughs in medicine and develop new technologies, frequently in collaboration with our partner universities. We are guided by our values of scholarly excellence; disruptive innovation; hands-on engineering/hacking/building; partnership and collaboration; open communication and respect; inclusiveness; and opportunity for all.

Our Vision

• We pursue large scientific challenges that cannot be pursued in conventional environments
• We enable individual investigators to pursue their riskiest and most innovative ideas
• The technologies developed at CZ Biohub facilitate research by scientists and clinicians at our home institutions and beyond

Diversity of thought, ideas, and perspectives are at the heart of CZ Biohub and enable disruptive innovation and scholarly excellence. We are committed to cultivating an inclusive organization where all colleagues feel inspired and know their work makes an important contribution.

The Biohub's Summer Internship Program offers currently enrolled undergraduate students the opportunity to work on research projects in a one-of-a-kind environment onsite in San Francisco, CA. This 10-week program provides interns with hands-on experience, mentorship and training, and personal and professional development designed to meet interns where they are at while also propelling them forward in their career.

The internship program goals are to:

• Help interns build a strong foundation in scientific research and laboratory skills
• Provide scientific education and enrichment to underserved and underrepresented groups in STEM

  (Students from historically marginalized or underrepresented groups are strongly encouraged to apply.)

• Establish valuable and sustainable mentoring relationships
• Equip interns with the skills and network needed to be competitive in STEM
• Build a pipeline of STEM professionals

The Opportunity

The Data Science (Qualitative Cell Science) platform is seeking a talented undergraduate student to join the team for the 10-week program in San Francisco, CA. This team is working at the intersection of computational biology and quantitative cell biology. They work with Biohub’s research groups with the aim of understanding cellular biology at multiple scales ranging from transcriptome, epigenome, proteome of single cells to multicellular organism's development. The team leads the Tabula projects - making transcriptomic atlases of model organisms such as mouse, zebrafish, and human tissues. They are also eager to implement and test new computational tools and algorithms which can expand the capabilities of Biohub investigators. During the internship, the intern will learn how cutting-edge computational tools are applied to fundamental biological questions and participate in innovative research at Biohub. The successful candidate may work on one or more of the following projects:

Spatial transcriptomics technology allows us to measure the physical location of transcripts such that we can map the observed transcriptome to its position within the tissue. Next-Generation Sequencing (NGS)-based spatial transcriptomics methods can capture the whole transcriptome, 20K-30K genes, yet not at the single-cell resolution, but scales from 1-10 cells. Since the observed transcripts are potentially from multiple cell-types, there needs to be a decomposition of the measured transcriptome into known cell-types, which is called deconvolution. Although there are many deconvolution algorithms published over the past years, it is unclear how a user should choose an algorithm, and how one could interpret the deconvolution results.

The intern will work to test and benchmark computational deconvolution algorithms for spatial transcriptomics datasets, then streamline the deconvolution workflows to be used at CZ Biohub and beyond. The intern will have the opportunity to learn the basics of spatial transcriptomics technology and basic statistical methods used for benchmarking different algorithms.

Skills Required –

• Enrolled in a science/engineering degree/major (e.g. biology, physics, mathematics, engineering, computer science, etc.)

Preferred Qualifications –

• Familiarity with scientific programming (ideally in Python)

A key factor of any single cell RNA sequencing (scRNA-seq) dataset is the depth at which the sample is sequenced. Depending on the sequencing depth, all, most, or only some of the RNA molecules inside a cell may be accurately detected. While semi-quantitative rules of thumb exist for determining what sequencing depth is sufficient for any given experiment, we would like to establish a statistical framework to determine if a dataset is of sufficient sequencing depth for downstream analysis.

 The intern will computationally downsample our sequencing datasets with different levels of sequencing depth, then compute the information saved in these downsampled datasets. By increasing the simulated sequencing depth, we would like to see at which point the information saturates, which will give a quantitative metric of sequencing saturation. In addition, a systematic study of sequencing depth at the per-gene or per-cell type level is needed. The intern will learn the basics of single-cell RNA-sequencing technology, statistics, as well as computational tools to interface with sequencing data while developing computational workflow to assess the sequencing depth.