cryosieve 1.2.4

CryoSieve: a particle sorting and sieving algorithm for single particle analysis in cryo-EM

CryoSieve Overview

CryoSieve is an advanced software solution designed for particle sorting/sieving in single particle analysis (SPA) for Cryogenic Electron Microscopy (cryo-EM). Supported by extensive experimental results, CryoSieve has demonstrated superior performance and efficiency compared to other cryo-EM particle sorting algorithms.

Its unique ability to eliminate unnecessary particles from final stacks significantly optimizes the data analysis process. The refined selection of particles that remain contribute to a notably higher resolution output in reconstructed density maps.

For certain datasets, the precision of CryoSieve's particle subset selection is so refined that it approaches the theoretical limit, delivering unprecedented detail and accuracy.

Video Tutorial

A video tutorial is available within this repository. You can directly download it here.

Publications

For more details, please refer to the paper "A minority of final stacks yields superior amplitude in single-particle cryo-EM". If you find that CryoSieve contributes to your work, we kindly request you to cite this paper.

The List of Available Demo Cases

dataset	number of particles	molecular weight (kDa)	EMPIAR link	expected result link
TRPA1	43,585	688	EMPIAR-10024	link
hemagglutinin	130,000	150	EMPIAR-10097	link
LAT1	250,712	172	EMPIAR-10264	link
pfCRT	16,905	102	EMPIAR-10330	link
TSHR-Gs	41,054	125	EMPIAR-11120	link
TRPM8	42,040	513	EMPIAR-11233	link
apoferritin	382,391	440	EMPIAR-10200	link
streptavidin	23,991	52	EMPIAR-10269	link

Installation

CryoSieve is an open-source software, developed using Python, and is available as a Python package. You can access our source code on GitHub.

Prerequisites

• Python version 3.7 or later.
• NVIDIA CUDA library installed in the user's environment.

Dependencies

The CryoSieve package depends on the following libraries:

numpy>=1.18
mrcfile>=1.2
starfile>=0.4,<0.5
cupy>=10
torch>=1.10

Preparation of CUDA Environment

We recommend you install CuPy and PyTorch initially, as their installation largely depends on your CUDA environment. Please note, your PyTorch package should be CUDA-capable.

To streamline this process, we suggest preparing a conda environment with the following command:

conda create -n CRYOSIEVE_ENV python=3.10 cupy=10.2 cudatoolkit=10.2 pytorch=1.12.1=py3.10_cuda10.2_cudnn7.6.5_0 -c conda-forge -c pytorch

This command is specifically for a CUDA environment version 10.2. If your CUDA environment is higher than 10.2, adjust the command based on the suitable variants and versions recommended by the CuPy and PyTorch developers for your specific CUDA environment.

Installing CryoSieve

After preparing CuPy and PyTorch according to your CUDA environment, it is crucial to activate it before proceeding with the CryoSieve installation.

We recommend using the following command to activate it directly. (replace CRYOSIEVE_ENV with the name of your custom environment).

conda activate CRYOSIEVE_ENV

Then, we turn to the step of installing CryoSieve. CryoSieve can be installed either via pip or conda.

To install CryoSieve using pip, execute the following command:

pip install cryosieve

Alternatively, to install CryoSieve using conda, execute the following command:

conda install -c mxhulab cryosieve

Verifying Installation

You can verify whether CryoSieve has been installed successfully by running the following command:

cryosieve -h

This should display the help information for CryoSieve, indicating a successful installation.

Tutorial

Quickstart: A Toy Example

To validate your successful installation of CryoSieve and familiarize yourself with its functionalities, we highly recommend trying CryoSieve on this toy example. Please follow the steps below:

1. Download the dataset and place it into any directory of your choice, e.g., ~/toy/.
2. Navigate to this directory by executing the following command:

cd ~/toy/

3. Initiate CryoSieve with the following command:

cryosieve-core --i CNG.star --o my_CNG_1.star --angpix 1.32 --volume CNG_A.mrc --volume CNG_B.mrc --mask CNG_mask.mrc --retention_ratio 0.8 --frequency 40

You may find explanation for each option of cryosieve-core in the following section.

When the --num_gpus parameter is used with a value larger than 1, CryoSieve's core program will leverage multiple GPUs to expedite the sieving process. It accomplishes this by using PyTorch's elastic launch feature to initiate multiple processes. Each of these processes will use exactly one GPU.

For instance, on a machine equipped with 4 GPUs, you can use the following command to run the toy example:

cryosieve-core --i CNG.star --o my_CNG_1.star --angpix 1.32 --volume CNG_A.mrc --volume CNG_B.mrc --mask CNG_mask.mrc --retention_ratio 0.8 --frequency 40 --num_gpus 4

Upon successful execution, the command will generate two star files, my_CNG_1.star and my_CNG_1_sieved.star. These files contain the information of the remaining particles and the sieved particles, respectively. You can compare them with the provided CNG_1.star and CNG_1_sieved.star files. If executed correctly, they should contain the same particles.

Processing Real-World Dataset

In this section, we provide a hands-on example of how to utilize CryoSieve for processing the final stack in a real-world experimental dataset.

Download the Dataset

For this tutorial, we'll be using the final particle stack from the EMPIAR-11233 dataset. This dataset includes a final particle stack of TRPM8 bound to calcium, collected on a 300 kV FEI Titan Krios microscope.

To download the final particle stack, navigate to your desired working directory and execute the following command:

wget -nH -m ftp://ftp.ebi.ac.uk/empiar/world_availability/11233/data/Final_Particle_Stack/

Upon completion, you'll find a new directory named XXX/data/Final_Particle_Stack in your working directory. This directory contains a star file with all particle information and an mrcs file representing the final stack.

Additionally, you'll need a mask file. You can generate a mask file using any cryo-EM software, based on the reconstructed volume. If you prefer not to generate a mask file, we've provided one used in our experiments which you can download from this link. Once you have the mask file, move it into the Final_Particle_Stack directory.

You can find additional demonstration data, along with expected results, in this repository.

Iterative Reconstruction and Sieving

To achieve optimal results with real-world datasets, the sieving process generally involves several iterations. In each iteration, we perform 3D reconstruction (and perhaps postprocessing to derive the Fourier Shell Correlation (FSC) curve and resolution). We then apply CryoSieve to sieve a fraction of the particles based on the reconstructed map. The highpass cut-off frequency typically increases with each round.

For your convenience, we've developed an automatic command cryosieve which performs all these steps in a single run. To use it, please follow these steps:

1. Change the working directory to XXX/data/Final_Particle_Stack:

cd XXX/data/Final_Particle_Stack

2. Our automatic program currently uses Relion for 3D reconstruction and postprocessing. Therefore, make sure that relion_reconstruct or relion_reconstruct_mpi and relion_postprocess are accessible. Once confirmed, run the following command:

cryosieve --reconstruct_software relion_reconstruct --postprocess_software relion_postprocess --i diver2019_pmTRPM8_calcium_Krios_6Feb18_finalParticleStack_EMPIAR_composite.star --o output/ --mask mask.mrc --angpix 1.059 --num_iters 10 --frequency_start 40 --frequency_end 3 --retention_ratio 0.8 --sym C4

For a detailed explanation of each cryosieve option, please refer to the following section Options/Arguments of cryosieve.

The entire process may take over an hour, depending on your system resources. Multiple result files will be generated and saved in the output/ directory. For instance, the _iter{n}.star file contains particles that remain after the n-th sieving iteration, and the _postprocess_iter{n} folder houses the postprocessing result after the n-th iteration.

[Recommended] Re-estimate poses by CryoSPARC

The objective of re-estimating poses using CryoSPARC is to prevent the unintentional transfer of information from the discarded particles to those that are retained. This process of re-estimating poses with CryoSPARC can be conducted either manually or through automation using a Python script. The script, named cryosieve_auto_cryosparc.py, is provided by us in this repository.

For manually re-estimating poses with CryoSPARC, particles must be imported using CryoSPARC’s import particle stack job. This importation is from the _iter{n}.star file, which contains particles remaining after the n-th sieving iteration. Subsequently, the process involves conducting sequential ab-initio jobs, followed by either homogeneous refinement or non-uniform refinement jobs.

Alternatively, users can utilize the cryosieve_auto_cryosparc.py script, available in this repository, to circumvent the labor-intensive manual operations in CryoSPARC. The prerequisite for executing the cryosieve_auto_cryosparc.py script is the loading of CryoSPARC’s environment variables. This can be accomplished by executing the command eval $(cryosparcm env) in the shell. The successful loading of this environment can be verified by using the which python command. Upon successful execution, this command should point to a Python interpreter located within CryoSPARC’s installation path. Utilizing this Python interpreter, users can execute the cryosieve_auto_cryosparc.py script to automatically and sequentially carry out import particle stack, ab-initio, homogenous refinement, and non-uniform refinement jobs. These operations are performed for each particle stack listed in a sheet, which is provided as an argument to the cryosieve_auto_cryosparc.py script. For a detailed explanation of each cryosieve_auto_cryosparc.py option, please refer to the following section Options/Arguments of cryosieve_auto_cryosparc.py.

Options/Arguments of cryosive-core, cryosieve and cryosieve_auto_cryosparc.py

Options/Arguments of cryosieve-core

The program cryosieve-core is the core particle sieving module.

$ cryosieve-core -h
usage: cryosieve-core [-h] --i I --o O [--directory DIRECTORY] --angpix ANGPIX --volume VOLUME [--mask MASK] [--retention_ratio RETENTION_RATIO] --frequency
                      FREQUENCY [--balance] [--num_gpus NUM_GPUS]

CryoSieve core.

options:
  -h, --help            show this help message and exit
  --i I                 input star file path.
  --o O                 output star file path.
  --directory DIRECTORY
                        directory of particles, empty (current directory) by default.
  --angpix ANGPIX       pixelsize in Angstrom.
  --volume VOLUME       list of volume file paths.
  --mask MASK           mask file path.
  --retention_ratio RETENTION_RATIO
                        fraction of retained particles, 0.8 by default.
  --frequency FREQUENCY
                        cut-off highpass frequency.
  --balance             make retained particles in different subsets in same size.
  --num_gpus NUM_GPUS   number of GPUs to execute the cryosieve program, 1 by default.

Options/Arguments of cryosieve

The program cryosieve is an integreted program iteratively calling relion and cryosieve-core to do sieving process.

$ cryosieve -h
usage: cryosieve [-h] --reconstruct_software RECONSTRUCT_SOFTWARE [--postprocess_software POSTPROCESS_SOFTWARE] --i I --o O --angpix ANGPIX [--sym SYM]
                 [--num_iters NUM_ITERS] [--frequency_start FREQUENCY_START] [--frequency_end FREQUENCY_END] [--retention_ratio RETENTION_RATIO] --mask MASK
                 [--balance] [--num_gpus NUM_GPUS]

CryoSieve: a particle sorting and sieving software for single particle analysis in cryo-EM.

options:
  -h, --help            show this help message and exit
  --reconstruct_software RECONSTRUCT_SOFTWARE
                        command for reconstruction.
  --postprocess_software POSTPROCESS_SOFTWARE
                        command for postprocessing.
  --i I                 input star file path.
  --o O                 output path prefix.
  --angpix ANGPIX       pixelsize in Angstrom.
  --sym SYM             molecular symmetry, c1 by default.
  --num_iters NUM_ITERS
                        number of iterations for applying CryoSieve, 10 by default.
  --frequency_start FREQUENCY_START
                        starting threshold frquency, in Angstrom, 50A by default.
  --frequency_end FREQUENCY_END
                        ending threshold frquency, in Angstrom, 3A by default.
  --retention_ratio RETENTION_RATIO
                        fraction of retained particles in each iteration, 0.8 by default.
  --mask MASK           mask file path.
  --balance             make remaining particles in different subsets in same size.
  --num_gpus NUM_GPUS   number of gpus to execute CryoSieve core program, 1 by default.

There are several useful remarks:

• CryoSieve utilizes the RECONSTRUCT_SOFTWARE in its reconstruction command. This enables you to enhance the speed of the reconstruction step through multiprocessing by using the option --reconstruct_software "mpirun -n 5 relion_reconstruct_mpi". Additionally, you can further boost the reconstruction speed by using the option --reconstruct_software "mpirun -n 5 relion_reconstruct_mpi --j 20", leveraging multi-threading.
• If POSTPROCESS_SOFTWARE is not given, CryoSieve will skip the postprocessing step. Notice that postprocessing is not necessary for the sieving procedure.
• Since relion_reconstruct use current directory as its default working directory, user should ensure that relion_reconstruct can correctly access the particles.

Options/Arguments of cryosieve_auto_cryosparc.py

The cryosieve_auto_cryosparc.py is a Python script designed to automatically and sequentially execute a series of operations in CryoSPARC, namely import particle stack, ab-initio, homogenous refinement, and non-uniform refinement jobs.

usage: cryosieve_auto_cryosparc.py [-h] [--particles_sheet PARTICLES_SHEET] [--cryosparc_user_id CRYOSPARC_USER_ID]
                                   [--cryosparc_project_uid CRYOSPARC_PROJECT_UID] [--cryosparc_workspace_uid CRYOSPARC_WORKSPACE_UID]
                                   [--cryosparc_lane CRYOSPARC_LANE] [--molecular_symmetry MOLECULAR_SYMMETRY]

The cryosieve_auto_cryosparc.py is a Python script designed to automate CryoSPARC operations via the command line. Its purpose is to
bypass the labor-intensive manual processes.

options:
  -h, --help            show this help message and exit
  --particles_sheet PARTICLES_SHEET
                        a file containing a list of starfiles; each starfile corresponds to a single-particle dataset; NOTE, absolute
                        directory is mandatory
  --cryosparc_user_id CRYOSPARC_USER_ID
                        the E-mail address of the user of CryoSPARC
  --cryosparc_project_uid CRYOSPARC_PROJECT_UID
                        the project UID in cryoSPARC
  --cryosparc_workspace_uid CRYOSPARC_WORKSPACE_UID
                        the workspace UID in cryoSPARC
  --cryosparc_lane CRYOSPARC_LANE
                        the lane for computing resource in cryoSPARC
  --molecular_symmetry MOLECULAR_SYMMETRY
                        molecular symmetry

Release Note

• Version 1.2.4:
  • Fix the bug occurring with starfile >=0.5. CryoSieve now requires starfile >=0.4, <0.5.
  • Support gloo backend for multi-GPU version of cryosieve-core.
  • Fix the bug of SequentialSampler occurring with pytorch >=2.
• Version 1.2.3: Add new video tutorial.
• Version 1.2.1: fix the bug occurring when multiple optic groups are present in a star file.
• Version 1.2: Initial stable release.