Document Type

Article

Publication Date

3-2-2026

Keywords

JMG, Animals, Mice, Behavior, Animal, Phenotype, Software, Machine Learning

JAX Source

Elife. 2026;14:RP107259.

ISSN

2050-084X

PMID

41770219

DOI

https://doi.org/10.7554/eLife.107259

Grant

National Institutes of Health DA041668 Vivek Kumar, Jackson Laboratory Director's Innovation Fund Vivek Kumar, National Institutes of Health DA048634 Vivek Kumar, National Institutes of Health MH138309 Vivek Kumar, National Institutes of Health AG078530 Vivek Kumar

Abstract

Automated detection of complex animal behavior remains a challenge in neuroscience. Developments in computer vision have greatly advanced automated behavior detection and allow high-throughput preclinical and mechanistic studies. An integrated hardware and software solution is necessary to facilitate the adoption of these advances in the field of behavioral neurogenetics, particularly for non-computational laboratories. We have published a series of papers using an open field arena to annotate complex behaviors such as grooming, posture, and gait as well as higher-level constructs such as biological age and pain. Here, we present our integrated rodent phenotyping platform, JAX Animal Behavior System (JABS), to the community for data acquisition, machine learning-based behavior annotation and classification, classifier sharing, and genetic analysis. The JABS Data Acquisition Module (JABS-DA) enables uniform data collection with its combination of 3D hardware designs and software for real-time monitoring and video data collection. JABS-Active Learning Module (JABS-AL) allows behavior annotation, classifier training, and validation. We introduce a novel graph-based framework (ethograph) that enables efficient boutwise comparison of JABS-AL classifiers. JABS-Analysis and Integration Module (JABS-AI), a web application, facilitates users to deploy and share any classifier that has been trained on JABS, reducing the effort required for behavior annotation. It supports the inference and sharing of the trained JABS classifiers and downstream genetic analyses (heritability and genetic correlation) on three curated datasets spanning 168 mouse strains that we are publicly releasing alongside this study. This enables the use of genetics as a guide to proper behavior classifier selection. This open-source tool is an ecosystem that allows the neuroscience and genetics community to share advanced behavior analysis and reduces the barrier to entry into this new field.

Download

Share

COinS