Skip to main content

Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

  • Madugula, Sita S | Oak Ridge National Laboratory
  • Massenburg, Lynnicia N | Oak Ridge National Laboratory
  • Brown, Spenser R | Oak Ridge National Laboratory
  • Bible, Amber N | Oak Ridge National Laboratory
  • Harris, Chanda R | Oak Ridge National Laboratory
  • Zhang, Lance X | Oak Ridge National Laboratory
  • Parker, Kiara | Oak Ridge National Laboratory
  • Retterer, Scott T | Oak Ridge National Laboratory
  • Morrell-Falvey, Jennifer L | Oak Ridge National Laboratory
  • Vasudevan, Rama K | Oak Ridge National Laboratory
  • Williams, Alexis N | Oak Ridge National Laboratory
Download Dataset
Overview

Description

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

Funding Resources

DOE Contract Number

AC05-00OR22725

Originating Research Organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Other Contributing Organizations

U.S. Department of Energy, Office of Science; Center for Nanophase Materials Sciences (CNMS) at ORNL; Materials Characterization Core at ORNL

Sponsoring Organization

Office of Science (SC)

Details

DOI

10.13139/ORNLNCCS/2997581

Release Date

October 20, 2025

Dataset

Dataset Type

SM Specialized Mix

Software

Ultralytics (YOLOv11)

Other ID Number(s)

U.S. Department of Energy, Office of Science FWP ERKCZ64

Acknowledgements

This work was carried out [in part] at Oak Ridge National Laboratory, managed by UT-Battelle, LLC for the U.S. Department of Energy under contract DE-AC05-00OR22725.

Category

  • 60 APPLIED LIFE SCIENCES

Keywords

  • machine learning,
  • computer vision,
  • artificial Intelligence,
  • bacteria