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

Madugula, Sita Sirisha; Massenburg, Lynn; Cox, Spenser; Webb, Amber; Harris, Chanda; Zhang, Lance; Parker, Kiara; Retterer, Scott; Morrell-Falvey, Jennifer; Vasudevan, Rama K; Williams, Alexis

doi:10.13139/ORNLNCCS/2997581

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

Dataset
Other Related Research

Abstract

DOI 10.13139/ORNLNCCS/2997581

Authors:

;

; Harris, Chanda; Zhang, Lance; Parker, Kiara;

;

ORNL
Materials MicroAnalysis

Publication Date:: Wed Oct 01 00:00:00 EDT 2025

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE

OSTI Identifier:: 3002004

DOI:: https://doi.org/10.13139/ORNLNCCS/2997581

Citation Formats


                    Madugula, Sita Sirisha, Massenburg, Lynn, Cox, Spenser, Webb, Amber, Harris, Chanda, Zhang, Lance, Parker, Kiara, Retterer, Scott, Morrell-Falvey, Jennifer, Vasudevan, Rama K, and Williams, Alexis. Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures.  United States: N. p., 2025. 
        Web.  doi:10.13139/ORNLNCCS/2997581.

Copy to clipboard


                    Madugula, Sita Sirisha, Massenburg, Lynn, Cox, Spenser, Webb, Amber, Harris, Chanda, Zhang, Lance, Parker, Kiara, Retterer, Scott, Morrell-Falvey, Jennifer, Vasudevan, Rama K, & Williams, Alexis. Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures.  United States.  doi:https://doi.org/10.13139/ORNLNCCS/2997581

Copy to clipboard


                    Madugula, Sita Sirisha, Massenburg, Lynn, Cox, Spenser, Webb, Amber, Harris, Chanda, Zhang, Lance, Parker, Kiara, Retterer, Scott, Morrell-Falvey, Jennifer, Vasudevan, Rama K, and Williams, Alexis. 2025.  
"Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures".  United States.  doi:https://doi.org/10.13139/ORNLNCCS/2997581.  https://www.osti.gov/servlets/purl/3002004. Pub date:Wed Oct 01 00:00:00 EDT 2025

Copy to clipboard


                    
@article{osti_3002004,

  title        = {Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures},

  author       = {Madugula, Sita Sirisha and Massenburg, Lynn and Cox, Spenser and Webb, Amber and Harris, Chanda and Zhang, Lance and Parker, Kiara and Retterer, Scott and Morrell-Falvey, Jennifer and Vasudevan, Rama K and Williams, Alexis},

  abstractNote = {DOI 10.13139/ORNLNCCS/2997581},

  doi          = {10.13139/ORNLNCCS/2997581},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Oct 01 00:00:00 EDT 2025},

  month        = {Wed Oct 01 00:00:00 EDT 2025}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.13139/ORNLNCCS/2997581

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

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

Dataset Madugula, Sita S ; Massenburg, Lynnicia N ; Brown, Spenser R ; ...

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
Building Morphologies of the USA Structures Database; a Gauntlet Feature Set

Dataset Hauser, Taylor ; Stipek, Clinton ; Singh, Nagendra ; ...

Building Morphology features and training labels for 131 million buildings in the USA Structures database.
United States CMM Insights Dataset

Dataset Miller, Logan ; Doyle, Jessie ; Hoover, Karla ; ...

Geo-data science applications for critical mineral analysis, development acceleration, economic impact assessment, and project efficiency. Coverage: 10 years (2014-2023), 3 geographic levels (county, state, tract), 45 features Data Categories: - census: 30 features (e.g., asian population percentage, black population percentage, citizen voting age population percentage) - ejscreen: 3 features (e.g., P_DSLPM, P_PM25, P_PWDIS) - energyexpenditure: 12 features (e.g., Housing adjustment factor, Income adjustment factor, Monthly electricity cost)
FleetREDI Insight: Intrastate Coach Bus Dataset

Dataset Birky, Alicia

Capturing real-world data is critical to reducing the barriers to vehicle decarbonization and technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores coach buses operating in Colorado. Coach buses are a primary mover for intrastate transit and are primarily used for longer trips with more comfortable seats and a restroom. All Aboard America! Holdings Inc. offers various fixed-service and charter routes across Coloradomore »« less
Dataset for the Danczak et al., 2025 manuscript about bacterial-fungal interactions

Dataset Danczak, Robert E. ; Bell, Sheryl L. ; Warburton, Evan J. ; ...

We generated genome-resolved multiomics data from a series of metagenomic and metatranscriptomic sequencing. Specifically, we acquired, functionally annotated, and taxonomically classified both bacterial and eukaryotic metagenome assembled genomes (MAGs). For bacterial MAGs, we assembled eukaryotic float metagenomic sequencing data from JGI using MEGAHIT, binned and refined MAGs using MetaWRAP and dRep, functionally annotated MAGs using eggNOG mapper, and assigned taxonomy using GTDB-tk. For eukaryotic MAGs, we first identified potentially eukaryotic contigs from a coassembly of eukaryotic float metagenomic sequencing data from JGI using EukRep and Whokaryote, binned MAGs using MetaBAT2, functionally annotated MAGs using eggNOG mapper, and assigned taxonomy usingmore »« less

Similar Records