TY - JOUR TI - Visualizing anatomically registered data with brainrender AU - Claudi, Federico AU - Tyson, Adam L AU - Petrucco, Luigi AU - Margrie, Troy W AU - Portugues, Ruben AU - Branco, Tiago A2 - Mathis, Mackenzie W A2 - Wassum, Kate M A2 - Nunez-Iglesias, Juan VL - 10 PY - 2021 DA - 2021/03/19 SP - e65751 C1 - eLife 2021;10:e65751 DO - 10.7554/eLife.65751 UR - https://doi.org/10.7554/eLife.65751 AB - Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data. KW - software KW - data visualization KW - open source KW - anatomy JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -