Overview

napari-tomoslice is a tool for interactively annotating geometrical structures in cryo-ET data and generating particle poses from these geometrical annotations for subtomogram averaging.

With napari-tomoslice you can

• Annotate geometrical structures in napari
• Generate poses from the geometrical annotations
• Convert poses for your favourite subtomogram averaging package

napari-tomoslice allows you to annotate points, spheres, paths and dipoles. Poses can be sampled in a number of ways from each annotation type, for a complete list see: pose generation

Poses can be easily converted into Relion STAR file format or Dynamo table format

Running napari-tomoslice

napari-tomoslice can simply be run using uvx with:

uvx napari-tomoslice

uv can be installed with:

curl -LsSf https://astral.sh/uv/install.sh | sh

How to install and use uv ?

More detailed information on how to install uv and how to use uvx can be found on the uv documentation.

Examples

• Points
• Spheres
• Path: Backbone, Helix, Rings
• Dipoles: Direct, Disk

Example Data

Data used in the examples can be downloaded from zenodo

Visualize the results

Results from generate-poses and convert-poses can be visualized with their corresponding tomogram with the example script below:

# /// script
# dependencies = [
#   "mrcfile",
#   "starfile",
#   "napari[pyqt5]",
#   "numpy",
#   "scipy",
# ]
# ///
import mrcfile
import napari
import numpy as np
import starfile
from scipy.spatial.transform import Rotation as R

tomogram_file = 'tomograms/microtubule.mrc'
particles_file = 'tomograms/backbone.star' # STAR file output from generate-poses or convert-poses

with mrcfile.open(tomogram_file) as mrc:
    tomogram = mrc.data.copy()
df = starfile.read(particles_file)

xyz = df[['x', 'y', 'z']].to_numpy()
# xyz_relion = df[['rlnCoordinateX', 'rlnCoordinateY', 'rlnCoordinateZ']].to_numpy()

euler_angles = df[['rot', 'tilt', 'psi']].to_numpy()
# euler_angles_relion = df[['rlnAngleRot', 'rlnAngleTilt', 'rlnAnglePsi']].to_numpy()

rotations = R.from_euler(seq='ZYZ', angles=euler_angles, degrees=True).inv()
rotated_z_vectors = rotations.apply([0, 0, 1])

viewer = napari.Viewer(ndisplay=3)

vectors = np.zeros((rotated_z_vectors.shape[0], 2, 3))

vectors[:, 0, :] = xyz[:, ::-1]
vectors[:, 1, :] = rotated_z_vectors[:, ::-1]

viewer.add_image(tomogram, blending='translucent_no_depth', colormap='gray_r')
viewer.add_points(xyz[:, ::-1], face_color='cornflowerblue', size=10)
viewer.add_vectors(vectors, length=12, edge_color='orange', edge_width=4)

napari.run()