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hatzakislab / DeepFRET-GUI

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DeepFRET

A fully open-source, all-inclusive software platform for doing total internal reflection microscopy (TIRFm) single molecule FRET (smFRET) fast and efficiently. The key feature is reverse-loading of ASCII traces, and Keras/TensorFlow-powered automatic trace sorting. Features include

If you'd like to play around with just the Keras/TensorFlow model, please go to https://github.com/komodovaran/DeepFRET-Model

How to cite

Peer-reviewed article can be found at: https://elifesciences.org/articles/60404

DeepFRET: Rapid and automated single molecule FRET data classification using deep learning
Johannes Thomsen, Magnus B. Sletfjerding, Stefano Stella, Bijoya Paul, Simon Bo Jensen, Mette G. Malle, Guillermo Montoya, Troels C. Petersen, Nikos S. Hatzakis
eLife 2020;9:e60404; doi: 10.7554/eLife.60404

Installing

The easiest way to run DeepFRET-GUI is to download the newest installer for your system from the releases page. We currently support macOS and Windows.

Be aware that macs with the new M1 chip has a seperate installer. If using this installer, it is also necessary to open a terminal and run sudo xattr -rd com.apple.quarantine /Applications/DeepFRET.app after installing to bring the app out of quarantine and allow it to run. This will prompt for the users password.

If you want to change the code, you can also install from source.

Loading data

  1. To load videos, open the application's main window ('Images')

and go to File → Open files. The latest release of DeepFRET supports only videos made with alternating laser excitation (ALEX). However, compatibility with non-ALEX data is implemented in the latest source code (executable files will be released soon). Donor order and appearance can be set in Preferences. The rest of the video layout is auto-guessed by the software and doesn't allow for fine-tuning, so be sure to set up new experiments properly!

  1. If you would like to extract traces from a large number of videos that don't fit into memory, you can tick the 'batch mode' option in Preferences, as well as set a number of detections per movie. This disables the option to interactively re-run spot detection analysis, but allows the software to process an essentially unlimited number of videos.

  2. If you've already exported a lot of smFRET traces from another software, but want to do your analysis in DeepFRET, you can even load in traces directly, without movies. Simply make sure that the trace window ('Traces') is the active window, and follow the same steps as above for loading data. This works both for traces with and without ALEX. NB. The current version only supports traces exported by iSMS; see "supported data formats".

Classifying the data

  1. Go to Analyze → Predict to predict the trace type using deep learning. A confidence score will be given for each trace, which tells you how certain the model is that this is a true smFRET trace.
  1. To sort traces by different things, go to the View menu. The option "advanced sort" includes, among other things, a lower confidence threshold.

Windows

The following shortcuts can be used to navigate the different windows, also found in the menu option Window:

Window Shortcut
Images ⌘1
Traces ⌘2
Histogram ⌘3
Transition Density ⌘4
Trace Simulator ⌘5

Images Window

A test movie can be downloaded at: https://drive.google.com/file/d/1yJ19EAQ2fB6vXWdGEJd3bVTBD1sy2Y3D/view?usp=sharing

Traces Window

Histogram Window

Transition Density Window

Trace Simulator Window

Statistical analysis

  1. To get an overview of data distributions, go to Windows → Histogram. This also allows to fit the FRET distribution with gaussians to estimate the number of underlying conformational states. These plots update with the number of traces selected. Because a large number of operations have to be re-computed every time a trace is selected/de-selected, it can be a bit slow if the window is left open while traces are being selected.

  2. Additionally, DeepFRET includes a Hidden Markov model with the possibility to fit each trace individually (as a smoothing, "step-finding" method), as well as a global fit option, for a true Hidden Markov model fit. This also automatically fits transition lifetimes and plots transition density plots (which can be found in Windows → Transition Density Plot). One can base the model on the donor/acceptor signal, or directly on the FRET signal (though this is less accurate)

The whole process should be cross-platform, but has only been tested on MacOS.

Supported data formats

video_seq

Development

The .ui files for the interface can be edited through Qt Creator, and when you have a working python development env, converted with generate_ui.py.

Development environment

This is only needed if you want to change the code. If you just want to run DeepFRET-GUI, see Install.

Unix

For macOS and Linux.

Make sure you have installed python3.6 and git.

  1. Clone the repo with git clone https://github.com/hatzakislab/DeepFRET-GUI.git.
  2. Change into the directory with cd DeepFRET-GUI.
  3. Create a python virtual environment with python -m venv venv.
  4. Activate the virtual environment with source venv/bin/activate.
  5. Install the python requirements with pip install -r requirements-unix.txt.

You should now be able to open the application with python src/main/python/main.py.

See New release if you now want to make a binary.

Windows

Install dependencies

This needs to be done only once for the machine. The optional steps is needed if you want to use fbs to make a new release. If you only want to change the python code, you can skip them.

  1. Python:
    1. Download and open the python 3.6 from the python web page. Version 3.6.8 is the latest micro version with an executable installer for Windows.
    2. During the installation wizard make sure to check the "Add Python 3.6 to PATH" option.
  2. Git from the git web page.
  3. (Optional) Microsoft dependencies:
    1. Install Windows 10 SDK.
    2. Install Visual C++ Redistributable for Visual Studio 2010.
    3. Install Visual C++ Redistributable for Visual Studio 2012.
    4. If fbs freeze complains about missing DDLs and tells you to install Visual C++ Redistributable for Visual Studio 2012, it could be actually be another version. See fbs issue 147 for details.
  4. (Optional) Nullsoft Install System:
    1. Download and run the installer from NSIS.
    2. The installer does not add the executables like makensis to the path. You need to add C:\Program Files (x86)\NSIS to the path. You can add it permanently by following this guide. Alternatively, you can execute $env:PATH += "C:\Program Files (x86)\NSIS;" in the PowerShell to set it for the session of the PowerShell.
  5. Normal users on Windows are missing the privileged to execute PowerShell scripts. This is needed to enable the Python virtual environment. Open a PowerShell and run Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy Unrestricted. Answer Y when prompted.
  6. (Optional) If you have installed Windows 10 SDK, a C++ Redistributable or NSIS: Restart the machine.

Get the code and install python requirements

  1. Open a PowerShell.
  2. Clone the repo with git clone https://github.com/hatzakislab/DeepFRET-GUI.git.
  3. Change into the directory with cd .\DeepFRET-GUI\.
  4. Create a python virtual environment with python -m venv venv.
  5. Activate the virtual environment with .\venv\Scripts\Activate.ps1.
  6. Install the python requirements with pip install -r requirements-win.txt.

You should now be able to open the application with python .\src\main\python\main.py.

See New release if you now want to make a binary.

Requirements files and pip-tools

The requirements files are generated with pip-tools. This ensures that we have locked down versions for reproducible environments and still a small and manageable list of dependencies.

If you are not changing the dependencies or you pull a new version with updated dependencies, you can just run pip install -r requirements-<your os>.txt as normally.

If however you need to change or update the dependencies you'll have to install pip-tools with pip install pip-tools. You add your changes to requirements.in instead of requirements.txt.

You then have to generate the locked down versions of requirements-unix.txt and requirements-win.txt. Given the nature of the requirements, you have to generate them on their separate systems.

Make sure the changes to requirements.in are on both systems. Then run pip-compile --output-file=requirements-unix.txt requirements.in on macOS or Linux and pip-compile --output-file=requirements-win.txt requirements.in on Windows. Copy one of the output files to the other system and commit the changes to requirements.in, requirements-unix.txt and requirements-win.txt in the same commit.

If you want to update the versions for any of the packages not restricted in requirements.in, give pip-compile the --upgrade flag when you run it.

If you have pip-tools installed: Instead on running pip install -r requirements-<your os>.txt, you can run pip-sync requirements-<your os>.txt. This will both install the versions of any package in requirements-<your os>.txt, but also remove any package not defined. This is especially useful when making a new release as it ensures the correct version of all packages cross both systems and also that no extra packages are included in the binary.

New release

To make a new release there are some considerations. You need access to both a macOS and a Windows machine. They can be virtual.

There are no backward compatibility builds made on macOS. Get a machine with an older version. See the fbs documentation.

Version number

The version number is specified in src/build/settings/base.json. Bump it to a suitable version and make a git commit with the change. git tag this commit with the tag v followed by the same version (ex. git tag v0.2) and push it to GitHub.

Creating installers

The following have to happen on both macOS and Windows.

  1. Navigate to your virtual environment and activate it.
  2. Make sure you are on the commit with the tag you just made.
  3. Run pip-sync to make sure you have the correct versions of all packages. See pip-tools for details.
  4. Clean up any old fbs artifacts with fbs clean.
  5. Generate a binary with fbs freeze. If you get an error about tk/tcl at this point, see https://github.com/hatzakislab/DeepFRET-GUI/issues/12
  6. Generate an installer with fbs installer.

The last line should point you to a target/DeepFRET.dmg or a target\DeepFRETSetup.exe. Save that file and repeat the above the other OS until you have both.

If the correct fbs is not in your path, you can call it directly. On Unix, it is at ./venv/bin/fbs. On Windows it is at .\venv\Scripts\fbs.exe.

Github release

Create a new GitHub release pointing to your newly created tag. Attach DeepFRET.dmg and DeepFRETSetup.exe as binaries during the release.

About fbs and PyInstaller

We have to inject some extra hooks into PyInstaller. Unfortunately fbs does not expose a way to do that. There is a pull request by NileGraddis that introduces such a functionality, but the author of fbs has decided not to implement it. We therefore use the fork by NileGraddis. It enables us to define additional_hooks_dir in src/build/settings/base.json.

fbs have pinned PyInstaller to version 3.4 due to some incompatibilities. The NileGraddis fork we use does not have the pinning of PyInstaller in it. We need to pin it manually. There is an issue tracking the support for PyInstaller 3.6. This pinning can be removed when there is no reason to use for fork anymore or fbs starts supporting PyInstaller 3.6 and the fork is updated.