{"id":15359670,"url":"https://github.com/jhsmit/t3ss-paper","last_synced_at":"2025-04-04T17:24:23.781Z","repository":{"id":99380897,"uuid":"427034742","full_name":"Jhsmit/T3SS-paper","owner":"Jhsmit","description":"Code for processing of live-cell microscopy data in Yuan 2021 paper","archived":false,"fork":false,"pushed_at":"2021-11-12T13:33:42.000Z","size":16716,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-10T02:46:51.653Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Jhsmit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-11-11T14:41:09.000Z","updated_at":"2021-11-12T13:33:45.000Z","dependencies_parsed_at":null,"dependency_job_id":"3af54afd-c57b-41e7-b5d4-2a29b180ab48","html_url":"https://github.com/Jhsmit/T3SS-paper","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jhsmit%2FT3SS-paper","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jhsmit%2FT3SS-paper/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jhsmit%2FT3SS-paper/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Jhsmit%2FT3SS-paper/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Jhsmit","download_url":"https://codeload.github.com/Jhsmit/T3SS-paper/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247218426,"owners_count":20903246,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-01T12:45:37.265Z","updated_at":"2025-04-04T17:24:23.758Z","avatar_url":"https://github.com/Jhsmit.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# T3SS-paper\n\n\n[![DOI](https://zenodo.org/badge/427034742.svg)](https://zenodo.org/badge/latestdoi/427034742)\n\n\n\n**Code for processing of live-cell microscopy data in Yuan 2021 type III paper**: \nYuan, B.; Portaliou, A. G.; Parakra, R.; Smit, J. H.; Wald, J.; Li, Y.; Srinivasu, B.; Loos, M. S.; Dhupar, H. \nS.; Fahrenkamp, D.; Kalodimos, C. G.; Duong van Hoa, F.; Cordes, T.; Karamanou, S.; Marlovits, T. C.; Economou, A. \n[Structural Dynamics of the Functional Nonameric Type III Translocase Export Gate. ](https://doi.org/10.1016/j.jmb.2021.167188)\nJournal of Molecular Biology 2021, 433 (21), 167188.\n\n\n\nData is available on Zenodo: https://doi.org/10.5281/zenodo.5680700\n\nThe final dataset contains 14209 c41 cells and 10770 EPEC cells. This is a result of segmentation and \nfiltering individual cells starting from 38GB of raw data.\n\nThe code in this repository reproduces supplementary figures S2 D,E.\n\nThis is a copy from my working directory and isn't the cleanest and probably will not directly run out of the box. Some \npaths need to be adjusted, neural network weights are missing, probably some code cells are redundant/erroneous. Please drop me an\nemail/DM if you have any questions!\n\nTo recreate the conda environment used (on Windows 10):\n\n```console\n$ conda env create -f environment.yml\n```\n\n### Contents\n\nIn the `src_data` directory are jupyter notebooks which take the raw data and apply (pre)processing. To run, \nthe `data_dir` input directory should be set to the correct raw data input directory (Extract zip from Zenodo) \n\n01_preprocessing.ipynb: Apply darkfield/brighfield and beam profile corrections\n\n02_segmentation.ipynb: Segment brightfield images with U-net Neural Network\n\n03_cell_objects.ipynb: Make ColiCoords cell objects from brightfield images\n\n04_optimization_and_selection.ipynb: This notebook is used to check the output and optimize cell coordinates.\nHowever, optimization output here is not used but repeated later.\n\n05_number_of_peaks.ipynb: Detect fluorescence peaks/blobs in cells and add as 'storm' dataset. This analysis \nis also repeated later\n\nIn the `src` directory are 5 more jupyter notebooks which aggregate all the data, repeat peak finding, filter\npeaks, optimize cells coordinate system and produce final output graphs.\n\nOn Zenodo, the colicoords cell objects of the final step are included ('data/brightfield_opt' folder). Other\nintermediate steps can be reproduced by following the pipeline starting from the raw data.\n\n![image](figures/EPEC_aligned_viridis_cropped.png)\n*Aggregated image with localizations of 16396 T3SS foci in 10770 EPEC cells*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhsmit%2Ft3ss-paper","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjhsmit%2Ft3ss-paper","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhsmit%2Ft3ss-paper/lists"}