Exploring applications of crowdsourcing to cryo-EM

Area of Science:

• Structural Biology
• Biophysics
• Computational Biology

Background:

• Particle extraction from cryo-electron microscopy (cryo-EM) micrographs is essential for single-particle analysis.
• Current automated particle picking algorithms can introduce biases due to reliance on 2D templates.
• Manual particle selection, while accurate, is prohibitively time-consuming for large cryo-EM datasets.

Purpose of the Study:

• To investigate the application of crowdsourcing for particle picking in cryo-electron microscopy.
• To evaluate the effectiveness of untrained citizen scientists in annotating cryo-EM particle datasets.
• To explore the potential and limitations of crowdsourcing for cryo-EM data processing.

Main Methods:

• Development and implementation of novel crowdsourcing experiments for cryo-EM particle selection.
• Utilizing citizen science platforms to engage untrained users in particle identification tasks.
• Comparison of crowdsourced particle picking with automated and manual methods.

Main Results:

• Demonstration of successful particle set annotation by untrained citizen scientists.
• Identification of possibilities and limitations associated with crowdsourcing in cryo-EM particle picking.
• Validation of crowdsourcing as a complementary approach to existing particle selection strategies.

Conclusions:

• Crowdsourcing presents a scalable and effective method for particle picking in cryo-electron microscopy.
• Citizen science can significantly contribute to processing large cryo-EM datasets, overcoming manual effort limitations.
• Further exploration of crowdsourcing applications in broader cryo-EM data processing workflows is warranted.