WikiSysop: Created page with "== Citation == Zhang, J.; Wang, Z.; Chen, Y.; Han, R.; Liu, Z.; Sun, F. Zhang, F. PIXER: an automated particle-selection method based on segmentation using a deep neural netw..."

2019-02-27T05:36:31Z

Created page with "== Citation == Zhang, J.; Wang, Z.; Chen, Y.; Han, R.; Liu, Z.; Sun, F. Zhang, F. PIXER: an automated particle-selection method based on segmentation using a deep neural netw..."

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== Citation ==

Zhang, J.; Wang, Z.; Chen, Y.; Han, R.; Liu, Z.; Sun, F. Zhang, F. PIXER: an automated particle-selection method based on segmentation using a deep neural network. BMC bioinformatics, 2019, 20, 41

== Abstract ==

Cryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements. To free researchers from this laborious work and to acquire a large number of high-quality particles, we propose an automated particle-selection method (PIXER) based on the idea of segmentation using a deep neural network. First, to accommodate low-SNR conditions, we convert micrographs into probability density maps using a segmentation network. These probability density maps indicate the likelihood that each pixel of a micrograph is part of a particle instead of just background noise. Particles selected from density maps have a more robust signal than do those directly selected from the original noisy micrographs. Second, at present, there is no segmentation-training dataset for cryo-EM. To enable our plan, we present an automated method to generate a training dataset for segmentation using real-world data. Third, we propose a grid-based, local-maximum method to locate the particles from the probability density maps. We tested our method on simulated and real-world experimental datasets and compared PIXER with the mainstream methods RELION, DeepEM and DeepPicker to demonstrate its performance. The results indicate that, as a fully automated method, PIXER can acquire results as good as the semi-automated methods RELION and DeepEM. To our knowledge, our work is the first to address the particle-selection problem using the segmentation network concept. As a fully automated particle-selection method, PIXER can free researchers from laborious particle-selection work. Based on the results of experiments, PIXER can acquire accurate results under low-SNR conditions within minutes.

== Keywords ==

== Links ==

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2614-y

== Related software ==

== Related methods ==

== Comments ==

2019Zhang Pixer - Revision history

WikiSysop: Created page with "== Citation == Zhang, J.; Wang, Z.; Chen, Y.; Han, R.; Liu, Z.; Sun, F. Zhang, F. PIXER: an automated particle-selection method based on segmentation using a deep neural netw..."