APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework

TL;DR

The paper introduces APPLE, a fully automatic, template-free particle picker for cryo-EM that significantly reduces manual effort and bias in selecting particles from micrographs, enabling faster high-resolution reconstructions.

Contribution

It presents a novel, fully automatic particle picking method that is simple, fast, and template-free, improving upon semi-automated approaches in cryo-EM.

Findings

Achieves high accuracy in particle selection

Reduces manual effort and bias

Effective on publicly available datasets

Abstract

Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While semi-automated particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, semi-automated particle picking is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and fully automatic particle picking. While our approach was inspired by template matching, it is completely template-free. This approach is evaluated on publicly available datasets containing micrographs of $\beta$-galactosidase and keyhole limpet hemocyanin projections.