Published July 31, 2026 | Version v2

J-AST: a web-based analysis platform for antimicrobial susceptibility testing - Supplementary data

  • 1. ROR icon Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI)
  • 2. Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
  • 3. Shmunis School of Biomedical and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel
  • 4. Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Friedrich Schiller University Jena, Germany

Description

Supplementary data for J-AST: a web-based analysis platform for antimicrobial susceptibility testing.

Package contents

  • j-ast-source-code.zip - Source code of the application

  • J-AST Video Tutorial.mp4 - Tutorial video (same that is hosted on YouTube)

  • timeline-example-data-small.zip - Data used in the tutorial video

  • timeline-example-data-small-project.jast.zip - Data used in the tutorial video, but as fully annotated J-AST project for testing

  • mic-detection-inputs.jast.zip - J-AST project that contains the MIC analysis - can be directly imported into J-AST¹

  • mic_analysis_validation.zip - Validation for the MIC analysis

  • fig x JAST vs diskImageR.zip and JAST vs diskImageR_project.jast.zip¹ - Data and R scripts required to  produce figure 6

  • fig x ddas vs etests.zip and ddas vs etests_project.jast.zip¹ - Data and R scripts required to produce figure 5

¹ the size of the project may be beyond the allowance for our public J-AST instance. Please use the desktop application on a desktop computer with Windows 11 or Ubuntu Linux 26.04 and at least 16GB RAM.

System requirements

Web instance

  • Any desktop computer with sufficiently large screen (at least 1920x1024)

  • Modern web browser (Firefox 150+, Chrome 151+)

Desktop software

  • Screen size at least 1920x1024

  • Windows 11 / Ubuntu Linux 26.04 / macOS 14²

  • At least 16GB RAM

² We do not recommend to run the software on macOS due to dependency on now deprecated x86_x64 components and the macOS security functions interfering with the software

Installation

Free cloud instance

  1. Visit https://asb.hki-jena.de/j-ast-app/

  2. Register a new guest account

Desktop (Windows 11/Linux/macOS)

  1. Download J-AST from https://asb.hki-jena.de/j-ast/download 

Demo

Please download the timeline-example-data-small-project.jast.zip file.

  1. Start J-AST (Desktop) or log into our cloud instance

  2. Click "New Project"

  3. Set the name to "Demo", ensure that "Timeline" is selected, and select the project ZIP file to be uploaded

  4. Click "OK" and after the import select the "Demo" project

  5. Click "Select all" in the right-hand panel and switch to the PROCESS tab

  6. Scroll down to the "Analyze" category, expand it, and select "DiskImageR (RAD/FoG/ZOI) - Timeline only"

  7. Confirm the operation with "OK" and wait until it finishes

  8. Click the "Results" button at the top right screen and click the only item in the list

  9. You will find the final result in statistics_aio.csv

Running the software on the data

DDA vs ETest comparison

Required data: ddas vs etests_project.jast.zip

  1. Start J-AST (Desktop) or log into our cloud instance

  2. Create a new project (Single images) named “MIC analysis”. Select the downloaded ddas vs etests_project.jast.zip file as the project to import.

  3. Select all images

  4. Go to the PROCESS tab, select “Analyze”, followed by “DiskImageR (RAD/FoG/ZOI) - Timeline only”

  5. Confirm with “OK”

  6. Wait until the process has finished

  7. Click the “Results” button at the top right of the application and select the generated MIC results

  8. The statistics_aio.csv file contains the final results

The scripts for the result processing and generation of figures can be found here: fig x ddas vs etests.zip

J-AST vs diskImageR comparison

Required data: JAST vs diskImageR_project.jast.zip

Please note that we will only describe how to apply the J-AST related analysis. If you want to also reproduce the diskImageR results, please download and run diskImageR https://github.com/acgerstein/diskImageR 

  1. Start J-AST (Desktop) or log into our cloud instance

  2. Create a new project (Single images) named “MIC analysis”. Select the downloaded JAST vs diskImageR_project.jast.zip file as the project to import.

  3. Select all images

  4. Go to the PROCESS tab, select “Analyze”, followed by “DiskImageR (RAD/FoG/ZOI) - Timeline only”

  5. Confirm with “OK”

  6. Wait until the process has finished

  7. Click the “Results” button at the top right of the application and select the generated MIC results

  8. The statistics_aio.csv file contains the final results

The scripts for the result processing and generation of figures can be found here: fig x JAST vs diskImageR.zip

MIC analysis

Required data: mic-detection-inputs.jast.zip

 

  1. Start J-AST (Desktop) or log into our cloud instance

  2. Create a new project (Single images) named “MIC analysis”. Select the downloaded mic-detection-inputs.jast.zip file as the project to import.

  3. Select all images

  4. Go to the PROCESS tab, select “Analyze”, followed by “Auto-detect MIC v1”

  5. Confirm with “OK”

  6. Wait until the process has finished

  7. Click the “Results” button at the top right of the application and select the generated MIC results

  8. The mic_results.csv file contains the final results

 

 The analysis scripts and results can be found here: mic_analysis_validation.zip



Files

ddas vs etests_project.jast.zip

Files (3.0 GB)

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Additional details

Related works

Is identical to
Lesson: https://youtu.be/KKC7ilyRaPE (URL)

Funding

Deutsche Forschungsgemeinschaft
National Research Data Infrastructure NFDI4Bioimage 501864659
European Research Council
Horizon 2020 research and innovation program 951475

Software

Repository URL
https://github.com/applied-systems-biology/j-ast
Programming language
Java