potentialsfromfret
Code for inferring continuous potentials from FRET data
Science Score: 52.0%
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Low similarity (11.0%) to scientific vocabulary
Repository
Code for inferring continuous potentials from FRET data
Basic Info
- Host: GitHub
- Owner: LabPresse
- License: mit
- Language: Python
- Default Branch: main
- Size: 2.72 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
PotentialsFromFRET
Code for inferring continuous potentials from FRET data.
Setting up the environment
To run the code please git clone the repository. We recommend using a virtual environment to run the code. To clone the repo on mac/linux and create the virtual environment, please run the following commands in the terminal:
bash
git clone https://github.com/LabPresse/PotentialsFromFRET.git
cd PotentialsFromFRET
python -m venv .env
source .env/bin/activate
pip install -r requirements.txt
Using the code
The code is organized into a class called "FRETAnalyzer". FRETAnalyzer contains a function, "learn_potential", which is used to infer potential energy landscapes from FRET data. FRET analyzer takes in two channel FRET data of shape (N,2) where each row is the number of photons collected in each channel and each column is the photons collected in a time level. FRET analyzer also optionally takes in a dictionary, "parameters", which contains information relevant to the experiment such as the time step (dt), temperature (kT), and characteristic FRET pair distance (R0).
To analyze a data set simply import FRETAnalyzer and run learnpotential on your dataset and parameters. The output is the maximum a posteriori set of variables, which can be visualized using FRETAnalyzer.plotvariables.
See example.py for a demonstration.
Comparing to HMM
We additionally created a class "FRETAnalyzerHMM" which performs the same analysis with HMM methods. Running the FRETAnalyzerHMM is identical to running FRETAnalyzer.
Questions and further explanation
FRETAnalyzer is a work in progress. Further documentation will be provided as it is created. If you require assistance or would like more details, please do not hesitate to contact us at jsbryan4@asu.edu
Owner
- Name: Pressé Lab
- Login: LabPresse
- Kind: organization
- Email: spresselab@gmail.com
- Location: United States of America
- Website: statphysbio.physics.asu.edu
- Twitter: LabPresse
- Repositories: 13
- Profile: https://github.com/LabPresse
The Pressé Lab develops and uses tools from statistics, statistical physics and stochastic processes to unravel the rules of life at the subcellular scale.
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Bryan IV"
given-names: "J Shepard"
- family-names: "Presse"
given-names: "Steve"
title: "Skipper-FRET"
version: "1.0"
date-released: "2022-9-22"
url: "https://github.com/LabPresse/PotentialsFromFRET"
GitHub Events
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- Push event: 4
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- Push event: 4
Dependencies
- contourpy ==1.3.0
- cycler ==0.12.1
- dill ==0.3.9
- fonttools ==4.55.0
- h5py ==3.12.1
- importlib_resources ==6.4.5
- joblib ==1.4.2
- kiwisolver ==1.4.7
- llvmlite ==0.43.0
- matplotlib ==3.9.2
- numba ==0.60.0
- numpy ==2.0.2
- packaging ==24.2
- pillow ==11.0.0
- pyparsing ==3.2.0
- python-dateutil ==2.9.0.post0
- scipy ==1.13.1
- six ==1.16.0
- zipp ==3.21.0