February 20, 2019
Francisco Romero Ferrero and colleagues have developed idtracker.ai, an algorithm and software for tracking individuals in large collectives of unmarked animals, recently described in Nature Methods.
Tracking individual animals in large collective groups can give interesting insights to behavior, but has proven to be a challenge for analysis. With advances in artificial intelligence and tracking software, it has become increasingly easier to collect such information from video data. Ferrero et al. have developed an algorithm and tracking software that features two deep networks. The first tracks animal identification and the second tracks when animals touch or cross paths in front of one another. The software has been validated to track individuals with high accuracy in cohorts of up to 100 animals with diverse species from rodents to zebrafish to ants. This software is free, fully-documented and available online with additional jupyter notebooks for data analysis.
Check out their website with full documentation, the recent Nature Methods article, BioRXiv preprint, and a great video of idtracker.ai tracking 100 zebrafish!
Romero-Ferrero, F., Bergomi, M. G., Hinz, R. C., Heras, F. J., & Polavieja, G. G. (2019). Idtracker.ai: Tracking all individuals in small or large collectives of unmarked animals. Nature Methods, 16(2), 179-182. doi:10.1038/s41592-018-0295-5
February 13, 2019
In Nature Methods, Avelino Javer and colleagues developed and shared an open-source platform for analyzing and sharing worm behavioral data.
Collecting behavioral data is important and analyzing this data is just as crucial. Sharing this data is also important because it can further our understanding of behavior and increase replicability of worm behavioral studies. This is achieved by allowing many scientists to re-analyze available data, as well as develop new methods for analysis. Javer and colleagues developed an open resource in an effort to streamline the steps involved in this process — from storing and accessing video files to creating software to read and analyze the data. This platform features: an open-access repository for storing, accessing, and filtering data; an interchange format for notating single or multi-worm behavior; and file formats written in Python for feature extraction, review, and analysis. Together, these tools serve as an accessible suite for quantitative behavior analysis that can be used by experimentalists and computational scientists alike.
Read more about this platform from Nature Methods! (the preprint is also available from bioRxiv!)
Javer, A., Currie, M., Lee, C. W., Hokanson, J., Li, K., Martineau, C. N., . . . Brown, A. E. (2018). An open source platform for analyzing and sharing worm behavior data. Nature Methods. doi:10.1101/377960