Ais: streamlining segmentation of cryo-electron tomography datasets

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Segmentation is a critical data processing step in many applications of cryo-electron tomography. Downstream analyses, such as subtomogram averaging, are often based on segmentation results, and are thus critically dependent on the availability of open-source software for accurate as well as high-throughput tomogram segmentation. There is a need for more user-friendly, flexible and comprehensive segmentation software that offers an insightful overview of all steps involved in preparing automated segmentations. Here, we present Ais: a dedicated tomogram segmentation package that is geared towards both high performance and accessibility, available at github.com/bionanopatterning/Ais . In this report, we demonstrate two common processing steps that can be greatly accelerated with Ais: particle picking for subtomogram averaging, and generating many-feature segmentations of cellular architecture based on in situ tomography data. Featuring comprehensive annotation, segmentation, and rendering functionality, as well as an open repository for trained models at aiscryoet.org, we hope that Ais will help accelerate research and dissemination of data involving cryoET.

Authors 4

1. Mart G.F. Last corresponding Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center;

   Leiden University Medical Center

2. Leoni Abendstein Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center;

   Leiden University Medical Center

3. Lenard M. Voortman Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center;

   Leiden University Medical Center

4. Thomas Harry Sharp

   University of Bristol

   Affiliation as printed

   University of Bristol