📣 📣 📣 Update: Check out PRIDICT2.0 from our updated study here. 📣 📣 📣

PRIDICT: PRIme editing guide RNA preDICTion

For accessing Supplementary Files, click here.

Repository containing python package for running trained PRIDICT (PRIme editing guide RNA preDICTion) models. prieml package includes modules to setup and run PRIDICT models for predicting prime editing efficiency and product purity.

To run PRIDICT online, see our webapp.

Installation using Anaconda (Linux and Mac OS) 🐍

📣 PRIDICT can only be installed on Linux and Mac OS since ViennaRNA package is not available for Windows 📣

The easiest way to install and manage Python packages on various OS platforms is through Anaconda. Once installed, any package (even if not available on Anaconda channel) could be installed using pip.

• Install Anaconda.

• Start a terminal and run:

  # clone PRIDICT repository
  git clone https://github.com/uzh-dqbm-cmi/PRIDICT.git
  # navigate into repository
  cd PRIDICT
  # create conda environment and install dependencies for PRIDICT (only has to be done before first run/install)
  # use pridict_linux for linux machine or pridict_mac for a macbook
  conda env create -f pridict_linux.yml # pridict_mac.yml for macbook
  # note that this step ('Solving environment:') can take a while (sometimes up to 45 min), but should eventually succeed.
  # if it doesn't succeed, try to remove viennarna from the .yml file and install it separately with 
  # conda install -c conda-forge -c bioconda viennarna
  
  # activate the created environment
  conda activate pridict
  
      ### ONLY FOR M1 (or newer) Mac you need to additionally run the following conda install command (tensorflow): 
      conda install conda-forge::tensorflow
      # optional (only if encountering error with libiomp5.dylib on MacOS):
      pip uninstall numpy
      pip install numpy==1.22.1
      ###
  
  # run desired PRIDICT command (manual or batch mode, described below)
  python pridict_pegRNA_design.py manual --sequence-name seq1 --sequence 'GCCTGGAGGTGTCTGGGTCCCTCCCCCACCCGACTACTTCACTCTCTGTCCTCTCTGCCCAGGAGCCCAGGATGTGCGAGTTCAAGTGGCTACGGCCGA(G/C)GTGCGAGGCCAGCTCGGGGGCACCGTGGAGCTGCCGTGCCACCTGCTGCCACCTGTTCCTGGACTGTACATCTCCCTGGTGACCTGGCAGCGCCCAGATGCACCTGCGAACCACCAGAATGTGGCCGC'
  # results are stored in 'predictions' folder

• PRIDICT environment only has to be installed once. When already installed, follow the following commands to use PRIDICT again:

  # open Terminal/Command Line
  # navigate into repository
  # activate the created environment
  conda activate pridict
  # run desired PRIDICT command (manual or batch mode, described below)
  python pridict_pegRNA_design.py manual --sequence-name seq1 --sequence 'GCCTGGAGGTGTCTGGGTCCCTCCCCCACCCGACTACTTCACTCTCTGTCCTCTCTGCCCAGGAGCCCAGGATGTGCGAGTTCAAGTGGCTACGGCCGA(G/C)GTGCGAGGCCAGCTCGGGGGCACCGTGGAGCTGCCGTGCCACCTGCTGCCACCTGTTCCTGGACTGTACATCTCCCTGGTGACCTGGCAGCGCCCAGATGCACCTGCGAACCACCAGAATGTGGCCGC'
  # results are stored in 'predictions' folder

Running PRIDICT in 'manual' mode:

Required:

• --sequence-name: name of the sequene (i.e. unique id for the sequence)
• --sequence: target sequence to edit in quotes (format: "xxxxxxxxx(a/g)xxxxxxxxxx"; minimum of 100 bases up and downstream of parentheses are needed; put unchanged edit-flanking bases outside of parentheses (e.g. xxxT(a/g)Cxxx instead of xxx(TAC/TGC)xxx)

  Optional:

• --output-dir: output directory where results are dumped on disk (default: ./predictions; directory must already exist before running)
• --use-5folds: Use all 5-folds trained models. Default is to use fold-1 model
• --cores: Number of cores to use for multiprocessing. Default value 0 uses all available cores.
• --nicking: Additionally, design nicking guides for edit (PE3) with DeepSpCas9 prediction.
• --ngsprimer: Additionally, design NGS primers for edit based on Primer3 design.

python pridict_pegRNA_design.py manual --sequence-name seq1 --sequence 'GCCTGGAGGTGTCTGGGTCCCTCCCCCACCCGACTACTTCACTCTCTGTCCTCTCTGCCCAGGAGCCCAGGATGTGCGAGTTCAAGTGGCTACGGCCGA(G/C)GTGCGAGGCCAGCTCGGGGGCACCGTGGAGCTGCCGTGCCACCTGCTGCCACCTGTTCCTGGACTGTACATCTCCCTGGTGACCTGGCAGCGCCCAGATGCACCTGCGAACCACCAGAATGTGGCCGC'

--------------------------

### Running in batch mode:
  ####  Required:
  -  `--input-fname`: input file name - name of csv file that has two columns [`editseq`, `sequence_name`]. See `batch_template.csv` in the `./input` folder
  ####  Optional:
  -  `--input-dir` : directory where the input csv file is found on disk
  -  `--output-dir`: directory on disk where to dump results (default: `./predictions`)
  -  `--output-fname`: output filename used for the saved results
  -  `--use-5folds`: Use all 5-folds trained models. Default is to use fold-1 model
  -  `--cores`: Number of cores to use for multiprocessing. Default value 0 uses all available cores.
  -  `--nicking`: Additionally, design nicking guides for edit (PE3) with DeepSpCas9 prediction.
  -  `--ngsprimer`: Additionally, design NGS primers for edit based on Primer3 design.
```shell

 python pridict_pegRNA_design.py batch --input-fname batch_example_file.csv --output-fname batchseqs

Citation

If you find our work is useful in your research, please cite the following paper:

@article {Mathis et al.,
author = {Mathis, Nicolas and Allam, Ahmed and Kissling, Lucas and Marquart, Kim Fabiano and Schmidheini, Lukas and Solari, Cristina and Balázs, Zsolt and Krauthammer, Michael and Schwank, Gerald},
title = {Predicting prime editing efficiency and product purity by deep learning},
year = {2023},
doi = {10.1038/s41587-022-01613-7},
URL = { https://www.nature.com/articles/s41587-022-01613-7 },
journal = {Nature Biotechnology}
}