Full-Time Summer Internship in Discovery Proteomics @ South San Francisco, California, United States
The Proteomics and Biological Resources group is comprised of two arms serving the collaborative needs of research. The Biological Resource Management group synthesizes DNA and RNA oligonucleotides, performs various scales of DNA purification and provides inventory management for research-wide biologics resources including oligonucleotides, proteins & antibodies. The Proteomics groups develop and implement cutting edge technologies including mass spectrometry, expression cloning, protein microarray and conditioned media screening to elucidate protein-protein interactions, investigate post-translational modifications and assemble a quantitative, mechanistic understanding of other protein biochemistry at the cellular level. In addition we characterize biomolecules such as antigens, antibodies, lipids and oligosaccharides to support Research, Development and beyond.
The intern would be working on either of these two projects, depending on skill and interest.
Project 1: Benchmarking and refining algorithms to prioritize biologically significant protein interactions in large AP-MS datasets
The discovery proteomics group is currently evaluating experimental and computational protocols for sensitive and specific discovery of protein-protein interactions, often in the presence of perturbing agents, by Affinity Purification Mass spectrometry (AP-MS). As experimental platforms increasingly become more automated and sensitive, accurately identifying biologically relevant interactions in large AP-MS datasets is an important, challenging task that requires innovative approaches beyond traditional statistical methods. To address this challenge we propose a project to systematically:
- Benchmark the performance of selected, current state-of-the art AP-MS scoring algorithms on recently published large-scale AP-MS and gold-standard protein interaction datasets.
- Organize in-house AP-MS datasets into a unified database model, score and evaluate the unified data with the selected algorithms.
- Formulate either a case-dependent strategy to select the best performing method and/or formulate a novel approach towards scoring AP-MS data using machine learning methods.
Project 2: Integration and modeling of global proteomic and genomic datasets
Genentech hosts a large in-house data repository of integrated gene-centric information called Genehub. There is an urgent need to systematically incorporate the results of in-house Affinity Purification Mass spectrometry (AP-MS) experiments performed by the Discovery Proteomics group as well as recently published high-quality protein interaction data into Genehub. In addition to improving the visibility of these proteomic datasets, integration with Genehub would allow us to efficiently leverage the wealth of analyzed genomic data when analyzing our own experiments. To address this challenge we propose a project to systematically:
- Collect, mine, QC and organize proteomic datasets to be incorporated into Genehub including publicly available large-scale high-quality protein interaction datasets as well as in-house proteomic datasets (specifically those acquired by AP-MS).
- Investigate visualization methods to integrate proteomic and genomic data and explore the potential of network modeling algorithms to detect hidden associations in these integrated datasets.
Who We Are
At Roche, 88,500 people across 150 countries are pushing back the frontiers of healthcare. Working together, we’ve become one of the world’s leading research-focused healthcare groups. Our success is built on innovation, curiosity and diversity. A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 35 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. The headquarters for Roche pharmaceutical operations in the United States, Genentech has multiple therapies on the market for cancer and other serious illnesses. Please take this opportunity to learn about Genentech, where we believe that our employees are our most important asset and are dedicated to remaining a great place to work.
Who You Are
- Master of Science (1st or 2nd Year or Higher)
- Majoring in bioinformatics / computational scientist
- Desired interests and/or skills: R programming, Data integration & mining, Data visualization, Databases, Statistics, Machine learning algorithms, Interaction networks, Systems biology