Ayelet Heimowitz, Princeton University.

Title: From raw data to particle stacks: data-driven methods for processing of low SNR experimental data in cryo-electron microscopy

Single-particle cryo-electron microscopy aims to determine the structure of 3D macromolecules from multiple 2D projections. The high levels of noise present in experimental data, in conjunction with a distortion applied by the electron microscope, is a cause of significant challenges to the process of acquiring particle stacks from the raw data outputted by the electron microscope. First and foremost, the noise and distortion complicate the selection of particle projections, making edge detection methods ineffective and adding many pitfalls to template-based methods. Furthermore, even after a successful particle picking, any 3D volume reconstructed from the distorted projections selected may yield an unreliable representation of the macromolecule. It is therefore necessary to estimate the distortion applied by the electron-microscope to each of the picked projection images.

In this talk I will introduce a simple and novel approach for fast, accurate, and template-free particle picking. This method, which can be considered as a mirror-image of pattern recognition, includes no model for the particle, and instead focuses on prior knowledge on the noise. I will also discuss a novel method for estimating the distortion applied by the electron-microscope, and include an evaluation of our methods on several publicly available datasets.