Context Awared Template Matching (CATM) for Cryo-ET data analysis

CATM version 0.1

Introduction

This software is designed for the assign the models in crowded cryo-ET tomograms. The main point is to use the pre-picked coordinates from the other software as input, and calculate the cross-correlation coefficients (CCCs) with the templates provided. The software then use these information and combine the geometric restrict between the models to archive high accurate assignments. It involved with a clash resolver (CRer) module to optimize the pose between the models.

Installation

python version at least 3.5 is required, create a separated environment if you want.

1. Creat a new vitual enviroment (optional)

conda create -n catm python 

2. Download the package from gitlab and change directory to the CATM folder

git clone https://git.biohpc.swmed.edu/s194231/cryo-catm.git
cd cryo-catm

2. Install the package, if you want to build your own code activiely, add -dev option

pip install .

3. Test the installation

cd test
catm #run the main program

Usage

• There are three different modes the program currently supported, "guilded-TM-CRer", "guilded-localTM-CRer", "traditional TM"

1. Prepare your data, required files are:

• a tomogram file, usually produced by back-projection and the low-pass filtered, in mrc format
• a list of candidate particles from deep-learning based pickers, manual picking or others, support csv, coord, star formats
• a 3D ctf model from Warp or relion (optional), if not provide, the missing wedge info show be given
• one or a few templates files, the templates need to be in a cubic volume for now
• corresponding mask files for each templates (optional), need to be the same dimensions as the templates if provided. If not provided, a tight mask will be generated according (recommended)

2. Determine the counter level for template matching and clash resolver

• run the counter_level_check.py file for your template, this only need to be done once for your similar dataset ''' python counter_level_check.py path_to_template.mrc [ctf_model or missing wedge info] '''
• check the counter level in Chimera/ChimeraX and choose the level with least artifact

3. copy the config.py file to your work dir, and adjust the parameters for your run，the key paremeters

• There are three different modes the program currently supported, "Context",

4. run the program in the work dir and analysis the result

CATM

The program will write the final results with csv, coord and star format, a back-mapped tomogram will also be generated.