  Cryo-electron tomography (cryo-ET) is an emerging technology for the 3D
visualization of structural organizations and interactions of subcellular
components at near-native state and sub-molecular resolution. Tomograms
captured by cryo-ET contain heterogeneous structures representing the complex
and dynamic subcellular environment. Since the structures are not purified or
fluorescently labeled, the spatial organization and interaction between both
the known and unknown structures can be studied in their native environment.
The rapid advances of cryo-electron tomography (cryo-ET) have generated
abundant 3D cellular imaging data. However, the systematic localization,
identification, segmentation, and structural recovery of the subcellular
components require efficient and accurate large-scale image analysis methods.
We introduce AITom, an open-source artificial intelligence platform for cryo-ET
researchers. AITom provides many public as well as in-house algorithms for
performing cryo-ET data analysis through both the traditional template-based or
template-free approach and the deep learning approach. AITom also supports
remote interactive analysis. Comprehensive tutorials for each analysis module
are provided to guide the user through. We welcome researchers and developers
to join this collaborative open-source software development project.
Availability: https://github.com/xulabs/aitom
