PRIDICT2.0: PRIme editing guide RNA preDICTion 2.0

Overview

PRIDICT2.0 is an advanced version of the original PRIDICT model designed for predicting the efficiency of prime editing guide RNAs. This repository allows you to run the model locally. For details on advancements over the original model, refer to our BioRxiv preprint.

Complementary Model

• ePRIDICT: This model focuses on the influence of local chromatin context (K562) on prime editing efficiencies and is designed to complement PRIDICT2.0. Access GitHub Repository

Resources

• Supplementary Files: Access Here
• Web Application: For an online version of PRIDICT2.0, visit our webapp.

Contact

For questions or suggestions, please either:

• Email us at nicolas.mathis@pharma.uzh.ch
• Open a GitHub issue

Citation

If find our work useful for your research please cite:

• Mathis et al., BioRxiv, 2023 (PRIDICT2.0)
• Mathis & Allam et al., Nature Biotechnology, 2023 (PRIDICT)

Getting Started

Installation using Anaconda (Linux or Mac OS) 🐍

Windows is currently NOT supported!

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 PRIDICT2.0 repository
  git clone https://github.com/uzh-dqbm-cmi/PRIDICT2.git
  # navigate into repository
  cd PRIDICT2
  # create conda environment and install dependencies for PRIDICT2 (only has to be done before first run/install)
  conda env create -f pridict2_repo.yml
      
  # activate the created environment
  conda activate pridict2
  
  # pytorch has to be installed separately here:
  pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
  
  	### 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 # or pip install numpy
  	###
  	
  
  # run desired PRIDICT2.0 command (manual or batch mode, described below)
  python pridict2_pegRNA_design.py manual --sequence-name seq1 --sequence 'GCCTGGAGGTGTCTGGGTCCCTCCCCCACCCGACTACTTCACTCTCTGTCCTCTCTGCCCAGGAGCCCAGGATGTGCGAGTTCAAGTGGCTACGGCCGA(G/C)GTGCGAGGCCAGCTCGGGGGCACCGTGGAGCTGCCGTGCCACCTGCTGCCACCTGTTCCTGGACTGTACATCTCCCTGGTGACCTGGCAGCGCCCAGATGCACCTGCGAACCACCAGAATGTGGCCGC'
  # results are stored in 'predictions' folder

• PRIDICT2.0 environment only has to be installed once. When already installed, follow the following commands to use PRIDICT2.0 again:

  # open Terminal/Command Line
  # navigate into repository
  # activate the created environment
  conda activate pridict2
  # run desired PRIDICT2.0 command (manual or batch mode, described below)
  python pridict2_pegRNA_design.py manual --sequence-name seq1 --sequence 'GCCTGGAGGTGTCTGGGTCCCTCCCCCACCCGACTACTTCACTCTCTGTCCTCTCTGCCCAGGAGCCCAGGATGTGCGAGTTCAAGTGGCTACGGCCGA(G/C)GTGCGAGGCCAGCTCGGGGGCACCGTGGAGCTGCCGTGCCACCTGCTGCCACCTGTTCCTGGACTGTACATCTCCCTGGTGACCTGGCAGCGCCCAGATGCACCTGCGAACCACCAGAATGTGGCCGC'
  # results are stored in 'predictions' folder

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Running PRIDICT2.0 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. Maximum 3 cores due to memory limitations. Default value 0 uses 3 cores if available.
• --nicking: Additionally, design nicking guides for edit (PE3) with DeepSpCas9 prediction (Kim et al. 2019).
• --ngsprimer: Additionally, design NGS primers for edit based on Primer3 design.

python pridict2_pegRNA_design.py manual --sequence-name seq1 --sequence 'GCCTGGAGGTGTCTGGGTCCCTCCCCCACCCGACTACTTCACTCTCTGTCCTCTCTGCCCAGGAGCCCAGGATGTGCGAGTTCAAGTGGCTACGGCCGA(G/C)GTGCGAGGCCAGCTCGGGGGCACCGTGGAGCTGCCGTGCCACCTGCTGCCACCTGTTCCTGGACTGTACATCTCCCTGGTGACCTGGCAGCGCCCAGATGCACCTGCGAACCACCAGAATGTGGCCGC'

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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. Maximum 3 cores due to memory limitations. Default value 0 uses 3 cores if available.
• --nicking: Additionally, design nicking guides for edit (PE3) with DeepSpCas9 prediction (Kim et al. 2019).
• --ngsprimer: Additionally, design NGS primers for edit based on Primer3 design.

 python pridict2_pegRNA_design.py batch --input-fname batch_template.csv --output-fname batchseqs

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