REliable PIcking by Consensus (REPIC) is a consensus methodology for harnessing multiple cryogenic-electron microscopy (cryo-EM) particle picking algorithms. It identifies particles common to multiple picked particle sets (i.e., consensus particles) using graph theory and integer linear programming (ILP). Picked particle sets may be found by a human specialist (manual), template matching, mathematical function (e.g., RELION's Laplacian-of-Gaussian auto-picking), or machine-learning method. A schematic representation of REPIC applied to the output of three CNN-based particle pickers is below:
REPIC expects particle sets to be in BOX file format (*.box) where each particle has coordinates, a detection box size (in pixels), and (optional) a score [0-1].
Required:
- Python v3.8 interpreter (Miniconda installation recommended)
- Python package dependencies described in setup.py
- Windows users - Ubuntu terminal environment with Windows Subsystem for Linux (WSL) (v22.04.2 LTS tested)
Optional:
- Gurobi ILP optimizer (v9.5.2 used) - requires free academic license **
- REgularised LIkelihood OptimisatioN (RELION) - particle and density analyses (v3.13 used)
- UCSF Chimera - map alignment and density visualization (v1.16 used)
** Required to reproduce manuscript results but if the Gurobi package is not found, REPIC will use the SciPy ILP optimizer
REPIC installation is expected to only take a few minutes.
Install using Docker (recommended)
- Install Docker if the
dockercommand is unavailable - Install and set up NVIDIA Container Toolkit for building and running GPU-accelerated containers
- Build CUDA-supported image from Dockerfile in REPIC GitHub repo:
docker build -t repic_img https://github.com/ccameron/REPIC.git#main
- Run container with GPU acceleration (example iter_pick.py command shown below):
docker run --gpus all -v <file_path>/REPIC/examples:/examples repic_img repic iter_pick /examples/10057/iter_config.json 4 100
Install using Conda
- Install Miniconda if the
condacommand is unavailable - Create a separate Conda environment and install REPIC and Gurobi:
conda create -n repic -c bioconda -c gurobi repic gurobi
- Activate REPIC Conda environment:
conda activate repic
- Obtain a Gurobi license and set Gurobi key:
grbgetkey <gurobi_key>
- Remove unused or temporary Conda files:
conda clean --all
Install from source using pip
- Either download the package by clicking the "Clone or download" button, unzipping file in desired location, and renaming the directory "REPIC" OR using the following command line:
git clone https://github.com/ccameron/REPIC
- Install Miniconda if the
condacommand is unavailable - Navigate to REPIC directory:
cd <install_path>/REPIC
- Create a separate Conda environment and install Gurobi for REPIC:
conda create -n repic -c gurobi python=3.8 gurobi
- Activate REPIC Conda environment:
conda activate repic
- Install REPIC using pip:
python -m pip install .
- Obtain a Gurobi license and set Gurobi key:
grbgetkey <gurobi_key>
- Remove unused or temporary Conda files:
conda clean --all
To check if REPIC was correctly installed, run the following command (after activating the REPIC Conda environment or using a REPIC container):
repic -h
A help menu should appear in the terminal.
Run using published Docker image (with Singularity/Apptainer)
A REPIC Docker image is published on both DockerHub and the GitHub container registery. Apptainer (formerly Singularity) can be used to run this image:
- Install Apptainer if the
apptainercommand is unavailable - Install and set up NVIDIA Container Toolkit for building and running GPU-accelerated containers
- Pull REPIC Docker image and convert to Singularity image format (SIF) (requires >8 Gb of memory and ~40 mins for conversion):
apptainer pull docker://cjfcameron/repic
If SIF file creation is taking a long time, increase the mksquashfs mem parameter in the Apptainer config file (apptainer.conf). See here for more information.
- Run container with GPU acceleration (example iter_pick.py command shown below):
apptainer run --nv --bind <file_path>/REPIC/examples:/examples repic_latest.sif repic iter_pick /examples/10057/iter_config.json 4 100
Run using Google Colab
A Jupyter Notebook for installing and running REPIC on Google Colab is included in the REPIC GitHub repo: repic_colab.ipynb
To open the notebook in Google Colab:
- Navigate a browser to: https://colab.google/
- Select "Open Colab", then "GitHub"
- Enter the REPIC GitHub web URL: https://github.com/ccameron/REPIC.git
- Select the "repic_colab.ipynb" Jupyter Notebook
Run using Scipion plugin
REPIC is available as a Scipion plugin: https://github.com/scipion-em/scipion-em-repic
See here for information about installing plugins for Scipion.
Example SPHIRE-crYOLO, DeepPicker, and Topaz picked particle coordinate files for
Example motion corrected T20S proteasome (EMPIAR-10057) micrographs and normative particles for iterative ensemble particle picking are freely available via Amazon Web Services (AWS). To download this data, please run get_examples.sh (see Quick start below for how to run this Bash script).
Installation instructions for SPHIRE-crYOLO, DeepPicker, and Topaz are found in docs/.
Example commands for fitting and running SPHIRE-crYOLO, DeepPicker, and Topaz models are found in repic/iterative_particle_picking/.
Parameters used for particle picking algorithms and RELION are found in supplemental_data_file_2.ods.
- Calculate the particle overlap (Jaccard index [JI]) and enumerate cliques using get_cliques.py (expected run time: 1-3 mins):
repic get_cliques examples/10017/ examples/10017/clique_files/ 180
Note - REPIC will use the folder names found in the provided input directory (e.g., examples/10017/) to assign method labels (e.g., "crYOLO", "deepPicker", "topaz")
Correctly executing the above command will produce the following files for each micrograph in the output folder examples/10017/clique_files/:
- *_clique_coords.pick: pickled clique (x,y) coordinates (in BOX format)
- *_constraint_matrix.pickle: pickled constraint matrix file
- *_weight_vector.pickle: pickled clique weight vector file
- *_runtime.tsv: runtime tracking TSV file
- Find optimal cliques using the ILP solver and create consensus particle BOX files using run_ilp.py (expected run time: <1 min):
repic run_ilp examples/10017/clique_files/ 180
Correctly executing the above command will produce a particle coordinate file (in BOX format) for each micrograph in the output directory examples/10017/clique_files/. The final column in these BOX files represents the clique weight for a consensus particle.
- Download example data from AWS S3 bucket using get_examples.sh (expected run time: 1-5 mins):
bash $(pip show repic | grep -in "Location" | cut -f2 -d ' ')/repic/iterative_particle_picking/get_examples.sh examples/10057/data/ &> aws_download.log
- Create a configuration file for iterative ensemble particle picking using iter_config.py (expected run time: <1 min):
repic iter_config examples/10057/ 176 224 <file_path>/gmodel_phosnet_202005_N63_c17.h5 <file_path>/DeepPicker-python 4 22
<file_path> must be replaced with the full file paths to the SPHIRE-crYOLO pre-trained model and DeepPicker directory, respectively. See picker installation instructions in docs/ for more information.
A configuration file iter_config.json will be created in the current working directory.
- Pick particles by iterative ensemble learning using iter_pick.py, a wrapper of run.sh (expected run time: 20-30 min/iteration):
repic iter_pick ./iter_config.json 4 100
The final set of consensus particles for the testing set should be found in:
examples/10057/iterative_particle_picking/round_4/train_100/clique_files/test/*.box
- Calculating particle overlap (JI) and enumerate cliques using get_cliques.py:
usage: repic get_cliques [-h] [--multi_out] [--get_cc] in_dir out_dir box_size
positional arguments:
in_dir path to input directory containing subdirectories of particle bounding box coordinate files
out_dir path to output directory (WARNING - script will delete directory if it exists)
box_size particle bounding box size (in int[pixels])
optional arguments:
-h, --help show this help message and exit
--multi_out set output of cliques to be members sorted by picker name
--get_cc filters cliques for those in the largest Connected Component (CC)
- Finding optimal cliques using ILP solver and creating consensus particle BOX files using run_ilp.py:
usage: repic run_ilp [-h] [--num_particles NUM_PARTICLES] in_dir box_size
positional arguments:
in_dir path to input directory containing get_cliques.py output
box_size particle bounding box size (in int[pixels])
optional arguments:
-h, --help show this help message and exit
--num_particles NUM_PARTICLES
filter for the number of expected particles (int)
run_ilp.py will create a plot of the concensus particle distribution (particle_dist.png) with a recommended (REC) num_particles value (70% of consensus particles) in the in_dir.
- Create a configuration file for iterative ensemble particle picking using iter_config.py:
usage: repic iter_config [-h] [--cryolo_env CRYOLO_ENV] [--deep_env DEEP_ENV] [--deep_model DEEP_MODEL] [--topaz_env TOPAZ_ENV]
[--topaz_model TOPAZ_MODEL] [--out_file_path OUT_FILE_PATH]
data_dir box_size exp_particles cryolo_model deep_dir topaz_scale topaz_rad
positional arguments:
data_dir path to directory containing training data
box_size particle bounding box size (in int[pixels])
exp_particles number of expected particles (int)
cryolo_model path to LOWPASS SPHIRE-crYOLO model
deep_dir path to DeepPicker scripts
topaz_scale Topaz scale value (int)
topaz_rad Topaz particle radius size (in int[pixels])
optional arguments:
-h, --help show this help message and exit
--cryolo_env CRYOLO_ENV
Conda environment name or prefix for SPHIRE-crYOLO installation (default:cryolo)
--deep_env DEEP_ENV Conda environment name or prefix for DeepPicker installation (default:deep)
--deep_model DEEP_MODEL
path to pre-trained DeepPicker model (default:out-of-the-box model)
--topaz_env TOPAZ_ENV
Conda environment name or prefix for Topaz installation (default:topaz)
--topaz_model TOPAZ_MODEL
path to pre-trained Topaz model (default:out-of-the-box model)
--out_file_path OUT_FILE_PATH
path for created config file (default:./iter_config.json)
data_dir/ is expected to contain a three-column TSV file of CTFFIND4 defocus values: (1) micrograph filename, (2) defocus x, and (3) defocus y. If this file is not found, then all micrographs will be assigned the same defocus value. A defocus file can be built from the output of a RELION CTF refinement job using the following Bash script:
EMPIAR_ID=<complete> # only integers - i.e., EMPIAR-10017 would be 10017
out=<install_path>/REPIC/examples/${EMPIAR_ID}/data/defocus_${EMPIAR_ID}.txt
rm -rf ${out}
for file in <relion_path>/relion/CtfFind/job00[0-9]/<mrc_pattern>; do
grep '' /dev/null ${file} | tail -n 1 | awk -F ":| " '{print $1,$3,$4}' >> ${out}
done
<mrc_pattern> is dependent on the naming convention used for micrographs and will need to be set to your specific substring. For EMPIAR-10017 and -10057, the substrings are '*0.txt' and '*[0-9].txt', respectively.
<relion_path>/relion/CtfFind/job00[0-9]/*<mrc_suffix> should list all CTFFIND4 output files in RELION's CtfFind/.
- Iteratively pick particles using iter_pick.py, a wrapper of run.sh:
usage: repic iter_pick [-h] [--semi_auto] [--sample_prob SAMPLE_PROB] [--score] [--out_file_path OUT_FILE_PATH] config_file num_iter train_size
positional arguments:
config_file path to REPIC config file
num_iter number of iterations (int)
train_size training subset percentage (int)
optional arguments:
-h, --help show this help message and exit
--semi_auto initialize training labels with known particles (semi-automatic)
--sample_prob SAMPLE_PROB
sampling probability of initial training labels for 'semi_auto' (default:1.)
--score evaluate picked particle sets
--out_file_path OUT_FILE_PATH
path for picking log file (default:<data_dir>/iter_pick.log)
train_size references the output of build_subsets.py, which builds training subsets of sizes 1%, 25%, 50%, and 100% (i.e., 100% will use the entire training set). For more information on dataset handling please see "iterative ensemble particle picking with REPIC" in the Methods section of the REPIC manuscript.
The REPIC software has been tested on two computer systems:
- Ubuntu 16.04.6 LTS (Xenial Xerus) running CUDA v10.1 with four Nvidia GP102 TITAN Xp
- Ubuntu 16.04.7 LTS (Xenial Xerus) running CUDA v11.3 with four Nvidia GeForce GTX 1080
If REPIC was used in your analysis/study, please cite:
Cameron, C.J.F., Seager, S.J.H., Sigworth, F.J., Tagare, H.D., and Gerstein, M.B. REPIC - an ensemble learning methodology for cryo-EM particle picking. BioRxiv. DOI: 10.1101/2023.05.13.540636v1
Submitting a GitHub issue is preferred for all problems related to REPIC.
For other concerns, please email Christopher JF Cameron.
- Google Colab integrated
- Scipion plugin created
- Docker/Singularity/Apptainer integrated
- k-d tree algorithm integrated to reduce graph building runtime
- Approval to include DeepPicker with REPIC install/distribution added: https://github.com/ccameron/REPIC/blob/main/imgs/deeppicker_approval.png
- Various bug fixes
- SciPy ILP optimizer integrated to remove Gurobi package requirement
- Read the Docs documentation created: https://repic.readthedocs.io/en/latest/
- Various bug fixes