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Virtual Fly Brain-An Interactive Atlas of the Nervous System

Abstract

As a model organism, is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.

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References
1.
Shih C, Sporns O, Yuan S, Su T, Lin Y, Chuang C . Connectomics-based analysis of information flow in the Drosophila brain. Curr Biol. 2015; 25(10):1249-58. DOI: 10.1016/j.cub.2015.03.021. View

2.
Costa M, Reeve S, Grumbling G, Osumi-Sutherland D . The Drosophila anatomy ontology. J Biomed Semantics. 2013; 4(1):32. PMC: 4015547. DOI: 10.1186/2041-1480-4-32. View

3.
Pfeiffer B, Ngo T, Hibbard K, Murphy C, Jenett A, Truman J . Refinement of tools for targeted gene expression in Drosophila. Genetics. 2010; 186(2):735-55. PMC: 2942869. DOI: 10.1534/genetics.110.119917. View

4.
Scheffer L, Xu C, Januszewski M, Lu Z, Takemura S, Hayworth K . A connectome and analysis of the adult central brain. Elife. 2020; 9. PMC: 7546738. DOI: 10.7554/eLife.57443. View

5.
Larkin A, Marygold S, Antonazzo G, Attrill H, Dos Santos G, Garapati P . FlyBase: updates to the Drosophila melanogaster knowledge base. Nucleic Acids Res. 2020; 49(D1):D899-D907. PMC: 7779046. DOI: 10.1093/nar/gkaa1026. View