TY - JOUR TI - anTraX, a software package for high-throughput video tracking of color-tagged insects AU - Gal, Asaf AU - Saragosti, Jonathan AU - Kronauer, Daniel JC A2 - Berman, Gordon J A2 - Dulac, Catherine A2 - Shaevitz, Joshua W A2 - Perez-Escudero, Alfonso VL - 9 PY - 2020 DA - 2020/11/19 SP - e58145 C1 - eLife 2020;9:e58145 DO - 10.7554/eLife.58145 UR - https://doi.org/10.7554/eLife.58145 AB - Recent years have seen a surge in methods to track and analyze animal behavior. Nevertheless, tracking individuals in closely interacting, group-living organisms remains a challenge. Here, we present anTraX, an algorithm and software package for high-throughput video tracking of color-tagged insects. anTraX combines neural network classification of animals with a novel approach for representing tracking data as a graph, enabling individual tracking even in cases where it is difficult to segment animals from one another, or where tags are obscured. The use of color tags, a well-established and robust method for marking individual insects in groups, relaxes requirements for image size and quality, and makes the software broadly applicable. anTraX is readily integrated into existing tools and methods for automated image analysis of behavior to further augment its output. anTraX can handle large-scale experiments with minimal human involvement, allowing researchers to simultaneously monitor many social groups over long time periods. KW - ants KW - social insects KW - collective behavior KW - ethology KW - social behavior KW - formicidae KW - tracking KW - machine vision KW - machine learning JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -