LarvaTagger: manual and automatic tagging of Drosophila larval behaviour

• DOI: 10.1093/bioinformatics/btae441
• Publication Date: 2024-07-06T08:04:01Z
• Authors: François Laurent; Alexandre Blanc; Lilly May; Lautaro Gándara; Benjamin T Cocano...; Benjamin M W Jones; Peter Hague; Chloé Barré; Christian L Veste...; Justin Crocker; Marta Zlatic; Tihana Jovanic; Jean-Baptiste Masson
• Keywords: Ground truth; Interface (matter); Representation; Tracking (education); Graphical user interface; Identification; Code (set theory)

Abstract Motivation As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of archetypal actions a larva regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for larval behaviour must be retrained to learn new representations from data. However, existing cannot transfer knowledge large amounts previously accumulated We introduce LarvaTagger, piece software that combines pre-trained deep neural network, providing continuous latent representation stereotypical identification, with graphical user interface manually tag train taggers updated ground truth. Results reproduced results an tagger high accuracy, we demonstrated pre-training databases accelerates training tagger, achieving similar prediction accuracy using less Availability implementation All code is free open source. Docker images also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.