Magellon: A next generation software package for automated cryo-EM data collection and processing

computer science, biochemistry, workflows

Research Mentor: Scott Stagg,
Department, College, Affiliation: Biological Sciences / Institute of Molecular Biophysics, Arts and Sciences
Contact Email: sstagg@fsu.edu
Research Assistant Supervisor (if different from mentor): Behdad Khoshbin
Research Assistant Supervisor Email: bk22n@fsu.edu
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 3
Relevant Majors: Computer Science, Biochemistry, Engineering
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link: Thursday, 9/7 4:00-5:00 - https://fsu.zoom.us/j/97609646320
Friday, 9/8 4:00-5:00 - https://fsu.zoom.us/j/97609646320

Project Description

We are developing the next generation of software for automated data collection and processing for cryo-electron microscopy (cryo-EM). Cryo-EM is now a widely established and indispensable method for determining the high-resolution structures of biomedically important molecules. The pioneering software packages Leginon and Appion demonstrated the power of automated data acquisition and real-time processing, and there are now numerous programs for automated data acquisition and real-time processing. Despite advances in automation, data collection and processing still require a good deal of manual involvement of an expert electron microscopist. Building on the foundations of Leginon and Appion, we are developing the next generation software package that we call “Magellon”. Magellon will overcome existing bottlenecks and provide an avenue toward fully automated data acquisition that bypasses need for user input during data collection. Importantly, this software will support the computational infrastructure to enable real-time image processing results to inform on and modify the ongoing data collection regime by learning where to acquire images in regions that will yield the highest resolution structures. Using machine learning and the new industry standard tools for distributed processing, we will develop new fast image assessment routines, and provide an application programming interface to enable the incorporation of extensions and plugins from developers in the community.

Research Tasks: Students will work with a team of programmers and biochemists to develop new tools for data collection and processing. The overall goals of the project have been modularized so that individual team members can work on a discrete task that contributes to the overall project. There are many potential tasks available depending on the interests of the student. Examples include: 1) adapting existing code to build backend tools that interface with the electron microscopes to drive data collection from the web, 2) using machine learning tools to automatically identify good areas of the sample for data collection, 3) developing front end web tools to interface with the instruments, 4) developing and testing deployment strategies for the software

Skills that research assistant(s) may need: We can take most any student with a computational mindset. Students should have at least one skill in the following list: Python, MySQL, Web development, HTML, Javascript, RESTful programming