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jin_shirazinejad_et_al_branched_actin_manuscript

code for "Asymmetric actin force production at stalled clathrin-mediated endocytosis sites"

https://github.com/drubinbarnes/jin_shirazinejad_et_al_branched_actin_manuscript

Science Score: 49.0%

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    Links to: biorxiv.org, nature.com
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Repository

code for "Asymmetric actin force production at stalled clathrin-mediated endocytosis sites"

Basic Info
  • Host: GitHub
  • Owner: DrubinBarnes
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 631 MB
Statistics
  • Stars: 1
  • Watchers: 3
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created about 5 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License Citation

README.md

JinShirazinejadetalbranchedactinmanuscript

code for "Asymmetric actin force production at stalled clathrin-mediated endocytosis sites", submitted

bioRxiv link:

first version: https://www.biorxiv.org/content/10.1101/2021.07.16.452693v1

second version: https://www.biorxiv.org/content/10.1101/2021.07.16.452693v2

publication: https://www.nature.com/articles/s41467-022-31207-5 https://doi.org/10.1038/s41467-022-31207-5

The live-cell imaging data (TIRF), tracked events, plots used for analysis, and arrays used throughout our analysis can be found at: https://tinyurl.com/z29zy93j

The repo for the code development can be found ony my personal Github page: https://github.com/cynashirazinejad/track_processing

Prior to running these notebooks, processed tracks from cmeAnalysis ("ProcessedTracks.mat" via https://github.com/DanuserLab/cmeAnalysis) can be generated using MATLAB. It is recommended that MATLAB 2018/2019 is used, since we have seen some slight differences in how fitted values are obtained in 2020/2021 versions.

The principle routines that these notebooks perform are: 1) visualizing the dynamics of tracked events out of cmeAnalysis** 2) clustering these tracked events into groups of similarly-behaved events 3) visualizing the results of clustering to understand how clusters are similar and different 4) identifying events with characteristic peaks of protein recruitment 5) using a trained clustering model to make predictions about the identity of new data 6) comparing the dynamics of different experimental groups 7) linking tracked events from separate tracking experiments 8) visualizing the results of multi-channel protein dynamics

** this step can be generalized to any tracking scheme with an output consisting of fitted intensities, positions, and statistical tests of detection confidences

Owner

  • Name: Drubin / Barnes Lab
  • Login: DrubinBarnes
  • Kind: organization

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