Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays, precisely locating and seamlessly averaging such particles across many tomograms present major challenges. Here, we developed TomoNet, a software package with a modern graphical user interface to carry out the entire pipeline of cryoET and subtomogram averaging to achieve high resolution. TomoNet features built-in automatic particle picking and three-dimensional (3D) classification functions and integrates commonly used packages to streamline high-resolution subtomogram averaging for structures in 1D, 2D, or 3D arrays. Automatic particle picking is accomplished in two complementary ways: one based on template matching and the other using deep learning. TomoNet’s hierarchical file organization and visual display facilitate efficient data management as required for large cryoET datasets. Applications of TomoNet to three types of datasets demonstrate its capability of efficient and accurate particle picking on flexible and imperfect lattices to obtain high-resolution 3D biological structures: virus-like particles, bacterial surface layers within cellular lamellae, and membranes decorated with nuclear egress protein complexes. These results demonstrate TomoNet’s potential for broad applications to various cryoET projects targeting high-resolution in situ structures.

A fully automatic approach to locate polyomavirus particles in transmission electron microscopy images is presented that can localize intact particles, many damaged capsids, and an acceptable percentage of superposed ones. Performance of the approach is quantified in 25 electron micrographs containing nearly 390 particles and compared with the interpretation of the micrographs by two independent electron microscopy experts. All parameterization is based on the particle expected dimensions. This approach uses indicators calculated from the local co-occurrence matrix of gray levels to assess the textured pattern typical of polyomavirus and prune the initial set of candidates. In more complicated backgrounds, about 2–10% of the elements survive. A restricted set of the accepted points is used to evaluate the typical average and variance and to reduce the set of survivors accordingly. These intermediate points are evaluated using (i) a statistical index concerning the radiometric distribution of a circular neighborhood around the centroid of each candidate and (ii) a structural index resuming the expected morphological characteristics of eight radial intensity profiles encompassing the area of the possible particle. This hierarchical approach attains 90% efficiency in the detection of entire virus particles, tolerating a certain lack of differentiation in the borders and a certain amount of shape alterations.