[{"body":"","desc":"Here you will find explanations and tutorial for the concepts and tools that are utilised by Virtual Fly Brain (VFB).","pinned":true,"section":"docs","title":"Documentation","url":"/docs/"},{"body":"","desc":"An overview video showing some examples of what you can do with Virtual Fly Brain.","pinned":true,"section":"docs","title":"Overview","url":"/docs/overview/"},{"body":"","desc":"Descriptions of the various features of the Virtual Fly Brain website and how to get started using them.","pinned":false,"section":"docs","title":"Website Features","url":"/docs/website-features/"},{"body":"","desc":"Details of concepts or terms used in Virtual Fly Brain.","pinned":false,"section":"docs","title":"Concepts","url":"/docs/concepts/"},{"body":"","desc":"How to guides for using the VFB website and other tools to explore the available data and perform tasks.","pinned":true,"section":"docs","title":"Tutorials","url":"/docs/tutorials/"},{"body":"","desc":"Types of data and datasets that are available on Virtual Fly Brain.","pinned":false,"section":"docs","title":"Data","url":"/docs/data/"},{"body":"","desc":"All available VFB APIs and underlying schemas.","pinned":true,"section":"docs","title":"VFB APIs","url":"/docs/apis/"},{"body":"","desc":"Contribute or update Virtual Fly Brain data/sites.","pinned":false,"section":"docs","title":"Contribution Guidelines","url":"/docs/contribution-guidelines/"},{"body":"","desc":"External resources that may be useful to users of Virtual Fly Brain. ","pinned":false,"section":"docs","title":"Resources","url":"/docs/resources/"},{"body":"","desc":"Diagrams associated with anatomy terms from the Drosophila Anatomy Ontology.","pinned":false,"section":"docs","title":"Anatomy Diagrams","url":"/docs/anatomy-diagrams/"},{"body":"","desc":"Tutorials from the Virtual Fly Brain \u0026#34;Hacking the connectome\u0026#34; workshop that was run in collaboration with the Drosophila …","pinned":false,"section":"docs","title":"Workshop Materials","url":"/docs/past-workshops/"},{"body":"Overview The Virtual Fly Brain Model Context Protocol (MCP) Tool enables you to query VFB data through Large Language Models like Claude using natural language. This guide shows you how to get started and provides examples of common queries.\nWhat is MCP? The Model Context Protocol is a standard that allows LLMs to interact with external data sources and tools. The VFB MCP tool follows this standard, providing your LLM with access to VFB\u0026amp;rsquo;s neuroanatomical databases, NBLAST similarity scores, and term information.\nAccessing the Tool The VFB MCP tool is available at: vfb3-mcp.virtualflybrain.org\nQuick Start Use the Live Service (Recommended) The easiest way to use VFB3-MCP is through our hosted service. This requires no installation or setup on your machine.\nClaude Desktop Setup Open Claude Desktop and go to Settings Navigate to the MCP section Add a new MCP server with these …","desc":"Learn how to use the VFB MCP tool to explore Virtual Fly Brain data through Large Language Models","pinned":false,"section":"docs","title":"VFB Model Context Protocol (MCP) Tool Guide","url":"/docs/tutorials/vfb-mcp-guide/"},{"body":"The VFB Production Database (PDB) is the main Neo4j graph database containing integrated neuroanatomical data from multiple sources, including ontology data, expression patterns, image annotations, and connectivity information. It serves as the primary data store for comprehensive queries across VFB\u0026amp;rsquo;s knowledge graph.\nBrowser Access You can explore the PDB interactively using the Neo4j browser:\nhttps://pdb.virtualflybrain.org/browser/\nData Structure Overview The PDB contains over 1.1 million nodes and 43 million relationships, representing a comprehensive knowledge graph of Drosophila neuroanatomy. Below is an overview of the available node labels, relationship types, and property keys.\nNode Labels (Gross Classifications) The PDB uses hierarchical node labels to classify different types of entities:\nCore Ontology Nodes:\nClass, Individual - OWL ontology elements Property - …","desc":"The VFB Production Database (PDB) is a Neo4j graph database containing integrated neuroanatomical data from multiple …","pinned":false,"section":"docs","title":"PDB API","url":"/docs/apis/pdb/"},{"body":"VFB connect API overview The VFB connect API provides programmatic access to the databases underlying Virtual Fly Brain.\nAt the core of Virtual Fly Brain is a set of curated terms for Drosophila neuro-anatomy organised into a queryable classification, including terms for brain regions, e.g. nodulus and neurons e.g. MBON01. These terms are used to annotate and classify individual brain regions and neurons in images and connectomics data. For example the term MBON01 is used to classify individual neurons from sources including the CATMAID-FAFB and Neuprint-HemiBrain databases. VFB stores both registered 3D images and connectomics data (where available) for all of these neurons.\nA single VfbConnect object wraps database connections and canned queries against all open VFB databases. It includes methods for retreiving metadata about anatomy, individual brain regions and neurons including IDs …","desc":"The VFB connect API provides programmatic access to the databases underlying VFB","pinned":false,"section":"docs","title":"VFB connect API overview","url":"/docs/tutorials/apis/vfb_api_overview/"},{"body":"The VFB Knowledge Base (KB) is a specialized Neo4j database that complements the Production Database (PDB). While the PDB handles integrated ontology data, expression patterns, and image annotations, the KB serves specific VFB requirements and workflows.\nBrowser Access Explore the KB using the Neo4j browser:\nhttps://kb.virtualflybrain.org/browser/\nPurpose and Scope The KB serves as:\nVFB-Specific Data Repository: Storage for VFB-specific annotations, metadata, and derived data Workflow Support: Specialized storage supporting VFB curation and integration workflows Note: Ontology data, expression patterns, image annotations, and connectivity data are stored in the Production Database (PDB).\nSchema Requirements The KB schema is designed with specific requirements:\nUpdateability: Ontology terms and FlyBase features must be updateable via CI ID Stability: Derived class IDs (e.g., expression …","desc":"The VFB Knowledge Base (KB) provides specialized storage and query capabilities for VFB-specific data and annotations.","pinned":false,"section":"docs","title":"KB API","url":"/docs/apis/kb/"},{"body":"The Owlery API is VFB\u0026amp;rsquo;s OWL (Web Ontology Language) reasoning service, providing advanced query capabilities over the ontologies used in VFB. It enables Description Logic (DL) queries, SPARQL queries with OWL reasoning, and access to multiple knowledgebases.\nAPI Endpoint Base URL: https://owl.virtualflybrain.org/\nNote: VFB provides a single knowledgebase (vfb) containing all VFB ontologies and reasoning data. All examples in this documentation use the vfb knowledgebase.\nKnowledgebase Operations Note: VFB provides access to a single knowledgebase (vfb) containing all VFB ontologies and reasoning data. The general /kbs endpoint is not accessible - all operations must use /kbs/vfb/.\nGet Knowledgebase Information GET /kbs/vfb Display information and status for the VFB knowledgebase.\nResponse: JSON object with KB information\nDL Queries (Description Logic) Owlery provides standard OWL DL …","desc":"The Owlery API provides OWL reasoning services for VFB\u0026#39;s ontologies, enabling complex queries over class hierarchies and …","pinned":false,"section":"docs","title":"Owlery API","url":"/docs/apis/owlery/"},{"body":"The SOLR Search API powers VFB\u0026amp;rsquo;s search and autocomplete functionality, providing fast text-based queries across all VFB entities including anatomical terms, neurons, datasets, publications, and more.\nNote: Search and autocomplete functionality is also built into VFBconnect by default. The vfb.term() method will automatically attempt to match partial terms and provide autocomplete suggestions. See the VFBconnect documentation for programmatic access.\nAPI Endpoint Base URL: https://solr.virtualflybrain.org/solr/ontology/select\nNote: VFB uses JSON-formatted parameter requests for complex queries.\nSearch Parameters Core Search Parameters Parameter Description VFB Default Example q Main search query (supports SOLR query syntax) Complex expansion (medulla OR medulla* OR *medulla OR *medulla*) defType Query parser type edismax edismax mm Minimum match percentage for multi-term queries …","desc":"The SOLR Search API provides fast text search and autocomplete functionality for VFB entities, datasets, and …","pinned":false,"section":"docs","title":"SOLR API","url":"/docs/apis/solr/"},{"body":" Below are brief descriptions of the libraries/packages. For details, I defer to their respective (excellent) documentations.\nQuerying VFB Queries against VFB\u0026amp;rsquo;s REST API are easiest with vfb_connect for Python. For R there is a vfb_connect wrapper, vfbconnectr. See also David\u0026amp;rsquo;s presentation for details.\nR In R, the natverse is your one-stop-shop for all things neuron: it\u0026amp;rsquo;s a collection of various R packages that are built on top of the neuroanatomy toolbox, nat. Of particular relevance for this workshop:\nnat is a general purpose library for working with morphological neuron data. In this workshop, we make heavy use of nat\u0026amp;rsquo;s plotting capabilities but its capabilities extend far beyond that. If you want to run any morphological analysis, I highly recommend you have a look at the \u0026amp;ldquo;Articles\u0026amp;rdquo; in nat\u0026amp;rsquo;s doc. neuprintr and hemibrainr provide an interface …","desc":"There are multiple tools to query the various data sources. Fortunately they play together nicely. Here is a quick …","pinned":false,"section":"docs","title":"Tool landscape","url":"/docs/past-workshops/connectome/tools/"},{"body":" ","desc":"Introduction session from the Virtual Fly Brain \u0026#34;Hacking the connectome\u0026#34; workshop that was run in collaboration with the …","pinned":false,"section":"docs","title":"Introduction to connectomic data and tools","url":"/docs/past-workshops/connectome/introvideo/"},{"body":"Connectomic datasets are not static: reconstructions are re-released as proofreading improves, neurons are split, merged or re-identified, and the resources that host them may occasionally move or shut down. When VFB ingests a new release, it must decide what happens to the entities from the previous version. This page documents those policies so that users understand why a neuron, link or connection may change, disappear, or persist between releases.\nAffected entities A connectomic dataset is represented in VFB by several types of graph node and edge:\nNode / edge What it represents DataSet nodes A specific released version of a dataset (e.g. a FlyWire release). Neurons are attached to it via has_source. Site (data source) nodes The external resource that hosts the data (e.g. Codex, NeuPrint, CATMAID). Holds the link_base used to build cross-reference links, and is flagged is_data_source …","desc":"How Virtual Fly Brain handles neurons, connectivity, data sources and cross-references when new versions of connectomic …","pinned":false,"section":"docs","title":"Versions \u0026 Deprecation","url":"/docs/data/em/versioning/"},{"body":"Prerequisites Before starting, ensure you have the VFBConnect library installed. The recommended Python version is 3.10.14, as this version is tested against the library.\npip install vfb-connect Importing the VFBConnect Library Start by importing the VFBConnect library. This library provides a simple interface to interact with neuron data from the Virtual Fly Brain.\nfrom vfb_connect import vfb Retrieving Neuron Data To work with specific neurons, you can use the vfb.term() function. This function takes a unique identifier (e.g., ID, label, synonym) for the neuron.\nExample: Retrieving a Single Neuron neuron = vfb.term(\u0026amp;#39;5th s-LNv (FlyEM-HB:511051477)\u0026amp;#39;) The neuron variable now holds data about the neuron identified by the given term.\nWorking with Neuron Data The retrieved neuron object can provide different representations of neuron data, such as its skeleton, mesh, and volume. …","desc":"This guide will help you use the VFBConnect library to interact with Virtual Fly Brain (VFB) data, specifically focusing …","pinned":false,"section":"docs","title":"Neuron display in VFBconnect","url":"/docs/tutorials/apis/vfbconnect_neuron_display/"},{"body":"Most of the queries in VFBTerm are built on previous older implementation queries so can be spread across multiple functions but as I just added a new query I thought this was a good example of a complex query (multiple steps combined) that is all in one function:\nhttps://github.com/VirtualFlyBrain/VFB_connect/blob/master/src/vfb_connect/schema/vfb_term.py#L1709C5-L1735C78\ndef add_anatomy_type_properties(self): @property def transgene_expression(self): \u0026amp;#34;\u0026amp;#34;\u0026amp;#34; Get the transgene expression data associated with this anatomy type term. \u0026amp;#34;\u0026amp;#34;\u0026amp;#34; if self._transgene_expression is None: print(\u0026amp;#34;Loading transgene expression for the first time...\u0026amp;#34;) if self.debug else None subclasses = self.vfb.oc.get_subclasses(query=f\u0026amp;#34;\u0026amp;#39;{self.id}\u0026amp;#39;\u0026amp;#34;, verbose=self.debug) print(\u0026amp;#34;Subclasses: \u0026amp;#34;, subclasses) if self.debug else None overlapping_cells = …","desc":"An exploration of how an example query is added to the VFBTerm object under VFBconnect which provides a template for any …","pinned":false,"section":"docs","title":"Adding new query in VFBconnect","url":"/docs/contribution-guidelines/expanding_vfbconnect_queries/"},{"body":"Scores Availability The similarity scores are calculated using NBLAST or other third-party scores (e.g. NeuronBridge). Only queries with above-threshold scores will appear in the \u0026amp;lsquo;Find similar\u0026amp;hellip;\u0026amp;rsquo; menu.\nStep 1: Select a Neuron or Expression Pattern Navigate to the VFB browser and select a neuron or expression pattern of interest so it\u0026amp;rsquo;s information is shown in the Term Info panel.\nStep 2: Open the \u0026amp;lsquo;Find similar\u0026amp;hellip;\u0026amp;rsquo; Menu under the \u0026amp;lsquo;Query For\u0026amp;rsquo; section Under the \u0026amp;lsquo;Query For\u0026amp;rsquo; section, locate the \u0026amp;lsquo;Find similar\u0026amp;hellip;\u0026amp;rsquo; expandable menu. This menu will only appear if an above-threshold score is available for the selected neuron or expression pattern.\nStep 3: Run a Similarity Score Query Inside the \u0026amp;lsquo;Find similar\u0026amp;hellip;\u0026amp;rsquo; menu, you will find queries to find morphologically similar neurons or expression …","desc":"This guide provides step-by-step instructions on how to use the similarity score queries on VirtualFlyBrain.org. These …","pinned":false,"section":"docs","title":"Similarity Scores","url":"/docs/tutorials/website/similarityscore/"},{"body":"Using the Bulk Image Download Tool Follow these steps to download multiple files from VirtualFlyBrain using the bulk download tool:\nStep 1: Open the Images Navigate to the VFB Browser and open all the images you are interested in downloading. You can do this by searching for specific images and opening their respective pages.\nStep 2: Open the Bulk Download Tool Once you have all the images open, locate the bulk download icon in the top right. Click on this icon to open the bulk download tool.\nStep 3: Select the Data Type In the bulk download tool, select the type of data you want to download. The options are OBJ, SWC, NRRD, and References.\nStep 4: Select the Variables If applicable, select specific variables related to the data type you have chosen.\nStep 5: Download the Data After making your selections, click the \u0026amp;lsquo;Download\u0026amp;rsquo; button to start the download. The data will be …","desc":"This guide provides step-by-step instructions on how to use the bulk download tool on VirtualFlyBrain.org. The tool …","pinned":false,"section":"docs","title":"Bulk Downloader","url":"/docs/tutorials/website/bulkdownloads/"},{"body":"Introduction VFBconnect is a Python package that provides an interface to the Virtual Fly Brain (VFB) API. It allows users to query the VFB database and download data, including images.\nInstallation Before you can use VFBconnect, you need to install it. You can do this using pip:\npip install vfb-connect Downloading Images To download images from VFB using VFBconnect, you need to first import the package and create a client:\nfrom vfb_connect.cross_server_tools import VfbConnect vc = VfbConnect() Next, you can use the get_images method to download images. This method requires the dataset ID as an argument:\ndataset_id = \u0026amp;#39;your_dataset_id\u0026amp;#39; images = vc.get_images(dataset_id) This will return a list of images from the specified dataset. Each image is represented as a dictionary with information such as the image ID, title, and URL.\nTo download the images, you can loop through the list …","desc":"This guide will show you how to use VFBconnect to download images from the Virtual Fly Brain (VFB) based on a dataset.","pinned":false,"section":"docs","title":"Downloading Images via API","url":"/docs/tutorials/apis/apiimagedownload/"},{"body":"Introduction FlyLight makes its primary data available via VFB as well as through dedicated websites for expression patterns of Generation 1 GAL4 and LexA lines and for split-GAL4 lines. Generation 1, split hemidriver, and stable split combination lines are available primarily from the Bloomington Stock Center. Stable split lines can also be requested via the split-GAL4 website.\nFlyLight Data and Resources FlyLight data is integrated and indexed on VFB and direct linkouts are provided from the Term Info tabs to the original images. VFB hosts the original larval images here. External links to the original collections of adult images are provided below:\nGeneration 1 GAL4 expression patterns\nGeneration 1 MCFO (single neuron) patterns\nSplit-GAL4 Expression Patterns\nJanelia Raw Image Collection\nLight and EM neurons can also be searched for on NeuronBridge\nAlso see FlyLight Imaging Tile …","desc":"The FlyLight Project produces large anatomical data sets and highly characterized collections of GAL4, LexA and …","pinned":false,"section":"docs","title":"FlyLight","url":"/docs/data/lm/flylight/"},{"body":"VFB integrates data curated from the literature with image data from many sources. Searches and queries can be used to explore this data.\nSearch for the item you\u0026amp;rsquo;re interested in\nQuery for related terms and images\nThe Search Tool The search system allows you to search for neurons and neuroanatomical structures using almost any name found in the literature. Positive and negative filters can be applied from the drop-down to restrict the results.\nThe Query Tool The query system can identify neurons innervating any specified neuropil or fasciculating with any specified tract. It also allows queries for genes, transgenes and phenotypes expressed in any brain region or neuron.\nQueries from Term Info Some Term Info panes have pre-defined queries relevant to that term. For example, the Term Info pane for a brain region may have queries for neurons innervating that region and drivers …","desc":"Text search and query tools and how to explore the 3D images.","pinned":false,"section":"docs","title":"Search and Query","url":"/docs/website-features/search_query/"},{"body":" Virtual Fly Brain (VFB) and FlyBase curators record information from the literature about the expression of single transgenes using ontology terms and load this into FlyBase. VFB combines curated expression, genetic and publication data from FlyBase with 3D images of the expression patterns aligned to standard templates. These annotated images can then be searched and queried via the web interface or APIs.","desc":"Details of the curation procedure for transgene expression patterns.","pinned":false,"section":"docs","title":"Transgene Expression Curation","url":"/docs/concepts/transgene/"},{"body":"VFB features tens of thousands of 3D images of neurons, clones and expression patterns, registered to standard template brains. Any combination of these can be added to the 3D viewer together.\nPoint and click to select neurons/expression\nClick and drag with the mouse or use the directional icons to rotate/move\nUse the zoom icons or scroll with the mouse to zoom in/out\nHome resets your view\nThe camera icon starts/stops a rotation animation of the scene\nThe sphere icon toggles wireframes on/off","desc":"The 3D Viewer displays 3D models of the template, showing all selected painted anatomy, neurons and expression patterns. …","pinned":false,"section":"docs","title":"3D Viewer","url":"/docs/website-features/3dviewer/"},{"body":"Finding transcriptomics data on VFB Finding cell type clusters Cell types with available scRNAseq data can be identified using the \u0026amp;lsquo;Has scRNAseq data filter\u0026amp;rsquo; when searching.\nClusters for a cell type of interest can be found via the \u0026amp;lsquo;Single cell transcriptomics data for\u0026amp;hellip;\u0026amp;rsquo; query.\nGene expression data After selecting a cluster, gene expression can be retrieved via the \u0026amp;lsquo;Genes expressed in\u0026amp;hellip;\u0026amp;rsquo; query.\nExpression level is the mean counts per million reads of all cells in the cell type cluster that express the given gene. Expression extent is the proportion of cells within the cluster that express the given gene. VFB only shows genes that are expressed in at least 20% of cells in the cluster (Extent \u0026amp;gt; 0.2).\nGene semantic tags We add semantic tags to genes to allow quick searching and filtering of results (\u0026amp;lsquo;Function\u0026amp;rsquo; column of gene …","desc":"How to navigate single cell RNAseq data on VFB. ","pinned":false,"section":"docs","title":"scRNAseq data","url":"/docs/tutorials/website/scrnaseq/"},{"body":"Split drivers comprise at least two partial transcription factors that can reconstitute an active transcription factor when expressed in the same cell. By using different regulatory regions for each component, functional driver expression can be restricted to the intersection of the expression patterns of these regulatory regions. A wide range of regulatory regions have been combined with transcription factor DNA binding domains (DBDs) and Activation Domains (ADs) to form thousands of distinct constructs, referred to as hemidrivers. Millions of combinations are therefore possible, each targeting some precise subset of the many thousands of neurons in the Drosophila nervous system.\nResearchers can identify split drivers for neurons of interest from the Targeting Splits section of a cell type Term Info page or by using similarity scores from a single neuron image. It is also possible to …","desc":"Split driver expression patterns in Virtual Fly Brain.","pinned":false,"section":"docs","title":"Split Driver Expression","url":"/docs/concepts/splits/"},{"body":" Hover to explore, click to list or shift + click to add painted anatomy\nUse the arrow icons or scroll with the mouse to move through the stack\nHome resets your view\nUse the zoom icons or pinch gesture to zoom\nToggles through orthogonal views\nToggles the slice position on the 3D Viewer on/off","desc":"The Slice Viewer displays single slices of the template, showing all selected painted anatomy, neurons and expression …","pinned":false,"section":"docs","title":"Slice Viewer","url":"/docs/website-features/sliceviewer/"},{"body":"What is NBLAST? NBLAST (Costa et al., 2016) is a computational method to quantify morphological similarity between neurons. It provides an objective way to compare neuron shapes and identify morphologically similar cells within and across datasets.\nHow NBLAST works NBLAST operates on \u0026amp;ldquo;dotprops\u0026amp;rdquo; - a representation of neurons as tangent vectors that capture the local geometry of neuronal arbors. The algorithm:\nConverts neurons to dotprops: Each neuron is represented as a set of points with associated directional vectors Compares vector pairs: For each tangent vector in a query neuron, NBLAST finds the closest tangent vector in the target neuron Calculates similarity scores: Scores are computed based on both the distance between vectors and their directional similarity (dot product) Normalizes results: Final scores are typically normalized to a self-self comparison, where a …","desc":"Understanding NBLAST morphological similarity scoring on VFB","pinned":false,"section":"docs","title":"NBLAST","url":"/docs/concepts/nblast/"},{"body":" Names Each entity in VFB has a unique Name. The Name field also shows semantic tags, to provide additional at-a-glance information, and the identifier (e.g. FBbt_00003823) for the entity. Many entities also have a Symbol, which is a more compact name that has been used in the literature. Synonyms are shown in the Alternative Names field along with references, where available.\nOntology terms and Graphs Classification and Relationships fields show parent and related ontology classes for the selected entity. Click to navigate to the Term Info pages for these terms. The location and classification Term Context graphs show partonomy relationships and parentage of the selected entity, respectively.\nClick on terms to select them\nData Thumbnails show available images (as a carousel if multiple). These can be clicked to add the image to the 3D viewer.\nClick on thumbnails to add an image to the …","desc":"Term Info displays information on the currently selected entity.","pinned":false,"section":"docs","title":"Term Info","url":"/docs/website-features/terminfo/"},{"body":"Thumbnails are found in query results and on Term Info pages.\nVFB Thumbnail features Each pixel in a VFB thumbnail corresponds to a specific depth in the image stack. The color bar on the right side of the image shows the range of colours corresponding to the full depth of the template, with blue being shallow and red being deep from the perspective of the viewer. Brighter or more intense colors indicate higher intensity values at that depth. This image uses nearly the whole of the available (Z) stack space, shown by the wide range of colour. This image uses much less of the available (Z) stack space, shown by the more restricted range of colour. ","desc":"VFB (Virtual Fly Brain) thumbnails are color depth maximum projection images that allow users to visualize the …","pinned":false,"section":"docs","title":"Thumbnails","url":"/docs/website-features/thumbnails/"},{"body":" Select a graph from the Term Info to display it in the Term Context pane. The location graph shows partonomy relationships, and the classification graph shows parentage of the selected term. Click on any term in the graph to select it and view its Term Info.\nHome resets your view\nUse the zoom icons or scroll with the mouse to zoom in/out\nClick to refresh to the current focus term\nSelect either the location or the classification for the current term","desc":"Term Context displays graphical information on the currently selected term\u0026#39;s location or classification.","pinned":false,"section":"docs","title":"Term Context","url":"/docs/website-features/termcontext/"},{"body":" The \u0026amp;lsquo;strongest\u0026amp;rsquo; paths are the shortest/highest weighted paths. Paths are arranged from the \u0026amp;lsquo;strongest\u0026amp;rsquo; at the bottom to the \u0026amp;lsquo;weakest\u0026amp;rsquo; at the top. A detailed explanation for the algorithm used to determine path strengths can be found here.\nSearch for the source neuron to start from (Note: query is directional)\nSearch for the target neuron\nMaximum number of paths to return (only the \u0026amp;lsquo;strongest\u0026amp;rsquo; paths will be returned)\nA minimum weight for the synapse count of each connection can be applied, paths containing individual connections below this minimum will not be returned","desc":"The Circuit Browser allows you to find the strongest paths from one neuron (the source neuron) to another (the target …","pinned":false,"section":"docs","title":"Circuit Browser","url":"/docs/website-features/circuitbrowser/"},{"body":"SOLR python example an example using pysolr:\ninstall:\npip install vfb-connect pysolr example looking for label/name match:\nimport pysolr solr = pysolr.Solr(\u0026amp;#39;https://solr.virtualflybrain.org/solr/ontology/\u0026amp;#39;) term = \u0026amp;#39;medulla\u0026amp;#39; results = solr.search(\u0026amp;#39;label:\u0026amp;#34;\u0026amp;#39; + term + \u0026amp;#39;\u0026amp;#34;\u0026amp;#39;) print(results.docs[0]) {\u0026amp;#39;iri\u0026amp;#39;: [\u0026amp;#39;http://purl.obolibrary.org/obo/FBbt_00003748\u0026amp;#39;], \u0026amp;#39;obo_id_autosuggest\u0026amp;#39;: [\u0026amp;#39;FBbt_00003748\u0026amp;#39;, \u0026amp;#39;FBbt:00003748\u0026amp;#39;, \u0026amp;#39;FBbt 00003748\u0026amp;#39;], \u0026amp;#39;label_autosuggest\u0026amp;#39;: [\u0026amp;#39;medulla\u0026amp;#39;, \u0026amp;#39;medulla\u0026amp;#39;, \u0026amp;#39;medulla\u0026amp;#39;], \u0026amp;#39;synonym_autosuggest\u0026amp;#39;: [\u0026amp;#39;ME\u0026amp;#39;, \u0026amp;#39;Med\u0026amp;#39;, \u0026amp;#39;optic medulla\u0026amp;#39;, \u0026amp;#39;m\u0026amp;#39;], \u0026amp;#39;label\u0026amp;#39;: \u0026amp;#39;medulla\u0026amp;#39;, \u0026amp;#39;synonym\u0026amp;#39;: [\u0026amp;#39;ME\u0026amp;#39;, \u0026amp;#39;Med\u0026amp;#39;, \u0026amp;#39;optic medulla\u0026amp;#39;, \u0026amp;#39;m\u0026amp;#39;], \u0026amp;#39;short_form\u0026amp;#39;: \u0026amp;#39;FBbt_00003748\u0026amp;#39;, \u0026amp;#39;autosuggest\u0026amp;#39;: …","desc":"How to programatically search for a term.","pinned":false,"section":"docs","title":"SOLR API","url":"/docs/tutorials/apis/solr/"},{"body":"Canonical templates allow image data to be spatially aligned, and are often painted to make a reference atlas of anatomical regions. Many central nervous system (CNS) templates exist for Drosophila; below is a summary of those used on VFB, grouped by the part of the nervous system and life stage they cover. Each template and its painted domains can be viewed and downloaded from their Term Info pages.\nAdult brain templates JRC2018 unisex adult brain The current standard adult brain template on VFB. An unbiased, averaged template built from many individual brains by Bogovic et al., 2020; most adult brain image data on VFB is aligned to it. Original files: JRC 2018 Brain templates. View on VFB.\nPainted domains (46) ICL, ROB, GOR, SLP, RUB, CRE, SMP, SPS, CAN, SCL, SIP, IB, ATL, AL, EPA, VES, LH, LO, SAD, AME, PVLP, AVLP, WED, PRW, AMMC, aL, LOP, a\u0026#39;L, bL, ME, PED, CA, NO, GA, AOTU, BU, PLP, …","desc":"Reference templates used on Virtual Fly Brain and the painted domains/regions available in each.","pinned":false,"section":"docs","title":"Templates","url":"/docs/data/templates/"},{"body":"Required packages: vfb-connect and python-catmaid (pymaid \u0026amp;amp; navis)\n!pip install vfb-connect --upgrade !pip install python-catmaid --upgrade A note on using these notebooks This is designed as an interactive tutorial. Feel free to add code cells below each example to try out variations of your own.\nHow to find neurons across datasets VirtualFlyBrain integrates images and connectomics profiles of neurons from many sources. It classifies and records their properties using a standard, queryable classification (The Drosophila Anatomy Ontology). This standardises the names of neuron types across sources, so you don\u0026amp;rsquo;t need to worry about differences in nomenclature uses and supports queries for neurons by their classification.\n# Import libs and initialise API objects from vfb_connect.cross_server_tools import VfbConnect import pandas as pd vc = VfbConnect() import pymaid import navis …","desc":"VFB integrates images and connectomics profiles of neurons from many sources.  It classifies and records their …","pinned":false,"section":"docs","title":"Discovery","url":"/docs/past-workshops/connectome/1_discovery/"},{"body":"Overview navis is a Python package for analysing, manipulating and visualizing neurons. Official documentation here.\nBasic datatypes: neurons and neuron lists navis knows three types of neurons:\nTreeNeurons = skeletons, e.g. from CATMAID MeshNeurons = meshes, e.g. from the hemibrain segmentation Dotprops = points + tangent vectors (typically only used for NBLAST) Collections of neurons are typically held in a specialized container: a NeuronList.\nNeurons In this notebook we will focus on skeletons - a.k.a. TreeNeurons - since this is what you get out of CATMAID. Let\u0026amp;rsquo;s kick things off by having a look at what neurons look like once it\u0026amp;rsquo;s loaded:\nimport navis # Load one of the example neurons shipped with navis # (these are olfactory projection neurons from the hemibrain data set) n = navis.example_neurons(1, kind=\u0026amp;#39;skeleton\u0026amp;#39;) # Print some basic info n WARNING: Could not …","desc":"How to explore the properties of `TreeNeurons` using navis.","pinned":false,"section":"docs","title":"Exploring Neurons in Navis","url":"/docs/tutorials/apis/navis/"},{"body":"!pip install vfb-connect --upgrade # Import libs and initialise API objects from vfb_connect.cross_server_tools import VfbConnect import pandas as pd vc = VfbConnect() import pymaid import navis navis.set_pbars(jupyter=False) pymaid.set_pbars(jupyter=False) # Connect to the VFB CATMAID server hosting the FAFB data rm = pymaid.connect_catmaid(server=\u0026amp;#34;https://fafb.catmaid.virtualflybrain.org/\u0026amp;#34;, api_token=None, max_threads=10) # Test call to see if connection works print(f\u0026amp;#39;Server is running CATMAID version {rm.catmaid_version}\u0026amp;#39;) # Many functions return JSON-compatible nested data structures. This function coverts them to DataFrame. def summary_2_df(summary, sort=None): \u0026amp;#34;\u0026amp;#34;\u0026amp;#34;Convert summary to DataFrame. Optionally specify a set of columns to sort as a list of strings\u0026amp;#34;\u0026amp;#34;\u0026amp;#34; if sort: return pd.DataFrame.from_records(summary).sort_values(sort) else: return …","desc":"If you have a single neuron, how can you find other neurons of the same or similar type within or between data sources?","pinned":false,"section":"docs","title":"Mapping","url":"/docs/past-workshops/connectome/2_mapping/"},{"body":"Plotting navis lets you plot neurons in 2D using matplotlib (nice for figures), and in 3D using either plotly when in a notebook environment like Deepnote or using a vispy-based 3D viewer when using a Python terminal.\nimport navis # This is relevant because Deepnote does not (yet) support fancy progress bars navis.set_pbars(jupyter=False) # Load one of the example neurons shipped with navis n = navis.example_neurons(1, kind=\u0026amp;#39;skeleton\u0026amp;#39;) WARNING: Could not load OpenGL library. # Make a 2d plot fig, ax = navis.plot2d(n) # Note that this is equivalent to # fig, ax = n.plot2d() If you have seen an olfactory projection neuron before, you might have noticed that this neuron is upside-down. That\u0026amp;rsquo;s because hemibrain neurons have an odd orienation in that the anterior-posterior axis is not the z- but the y-axis (they were imaged from above).\nFor us that just means we have to turn the …","desc":"How to plot neurons in 2D and 3D using navis.","pinned":false,"section":"docs","title":"Plotting Neurons with Navis","url":"/docs/tutorials/apis/plotting/"},{"body":"Overview pymaid lets you interface with a CATMAID server. It\u0026amp;rsquo;s built on top of navis and returns data (neurons, volumes) in a way that you can plug them straight into navis to use features such as plotting.\nOfficial documentation here.\nConnecting The VFB CATMAID servers (see here for what\u0026amp;rsquo;s available) are public and don\u0026amp;rsquo;t require an API token for read-only access which makes connecting simple:\nimport pymaid import navis navis.set_pbars(jupyter=False) pymaid.set_pbars(jupyter=False) # Connect to the VFB CATMAID server hosting the FAFB data rm = pymaid.connect_catmaid(server=\u0026amp;#34;https://fafb.catmaid.virtualflybrain.org/\u0026amp;#34;, api_token=None, max_threads=10) # Test call to see if connection works print(f\u0026amp;#39;Server is running CATMAID version {rm.catmaid_version}\u0026amp;#39;) WARNING: Could not load OpenGL library. INFO : Global CATMAID instance set. Caching is ON. (pymaid) …","desc":"pymaid (python-catmaid) lets you interface with a CATMAID server such as those provided by VFB.","pinned":false,"section":"docs","title":"pymaid","url":"/docs/tutorials/apis/pymaid/"},{"body":"!pip install vfb-connect --upgrade # Import libs and initialise API objects from vfb_connect.cross_server_tools import VfbConnect import pandas as pd vc = VfbConnect() import pymaid import navis # Needed because deepnote doesn\u0026amp;#39;t support fancy progress bars yet navis.set_pbars(jupyter=False) pymaid.set_pbars(jupyter=False) # Connect to the VFB CATMAID server hosting the FAFB data rm = pymaid.connect_catmaid(server=\u0026amp;#34;https://fafb.catmaid.virtualflybrain.org/\u0026amp;#34;, api_token=None, max_threads=10) # Test call to see if connection works print(f\u0026amp;#39;Server is running CATMAID version {rm.catmaid_version}\u0026amp;#39;) WARNING: Could not load OpenGL library. INFO : Global CATMAID instance set. Caching is ON. (pymaid) Server is running CATMAID version 2020.02.15-905-g93a969b37 Plotting navis lets you plot neurons in 2d using matplotlib (nice for figures), and in 3d using either plotly when in a …","desc":"We show you how to visualise the data you are working with","pinned":false,"section":"docs","title":"Visualisation","url":"/docs/past-workshops/connectome/3_visualisation/"},{"body":"!pip install --upgrade vfb_connect !pip install --upgrade git+git://github.com/schlegelp/navis@master !pip install --upgrade git+git://github.com/schlegelp/pymaid@master !pip install --upgrade ipywidgets !pip install --upgrade jsonpath_rw !pip install --upgrade seaborn !pip install --upgrade neuprint-python !pip install --upgrade flybrains # Import libs and initialise API objects from vfb_connect.cross_server_tools import VfbConnect import pandas as pd vc = VfbConnect() import pymaid import navis navis.set_pbars(jupyter=False) pymaid.set_pbars(jupyter=False) # Connect to the VFB CATMAID server hosting the FAFB data rm = pymaid.connect_catmaid(server=\u0026amp;#34;https://fafb.catmaid.virtualflybrain.org/\u0026amp;#34;, api_token=None, max_threads=10) # Test call to see if connection works print(f\u0026amp;#39;Server is running CATMAID version {rm.catmaid_version}\u0026amp;#39;) WARNING: Could not load OpenGL library. INFO …","desc":"How to explore published connectomic data.","pinned":false,"section":"docs","title":"Connectomics","url":"/docs/past-workshops/connectome/4_connectomics/"},{"body":"Overview NBLAST (Costa et al., 2016) is a method to quantify morphological similarity. It works on \u0026amp;ldquo;dotprops\u0026amp;rdquo; which represent neurons as tangent vectors. For each tangent vector in the query neuron, NBLAST finds the closest tangent vector in the target neuron and calculates a score from the distance between and the dotproduct of the two vectors. The final NBLAST score is the sum over all query-target vector pairs. Typically, this score is normalized to a self-self comparison (i.e. a perfect match would be 1).\nFinding Matching Neurons VFB computes NBLAST scores for all neurons in its database. So if all you want is a list of similar neurons, it\u0026amp;rsquo;s fastest (and easiest) to get those directly from VFB.\n# Import libs and initialise API objects from vfb_connect.cross_server_tools import VfbConnect import navis.interfaces.neuprint as neu import pandas as pd import navis …","desc":"NBLAST is a method to quantify morphological similarity.","pinned":false,"section":"docs","title":"NBLAST","url":"/docs/tutorials/apis/nblast/"},{"body":"The neuprint data and database The Janelia hemibrain (Scheffer et al., 2020), MANC (Takemura et al., 2024) and male-cns (Berg et al., 2025) datasets are accessible via neuprint at https://neuprint.janelia.org. The web interface lets you run a few pre-built queries and you can also run custom queries directly against the underlying neo4j graph data base using cypher queries.\nTo access neuprint programmatically, we will use neuprint-python (link). It requires a free API token, which you can get by logging into the website using a Google account.\nGetting started with neuprint-python First we have to initialize the connection. Substitute {your_token} with your neuprint token.\nimport neuprint as neu client = neu.Client(\u0026amp;#39;https://neuprint.janelia.org\u0026amp;#39;, dataset=\u0026amp;#39;hemibrain:v1.1\u0026amp;#39;, token={your_token}) Most functions in neuprint-python accept neu.NeuronCriteria which is effectively a …","desc":"Several Janelia datasets are accessible via neuprint and can be interfaced with programmatically using neuprint-python.","pinned":false,"section":"docs","title":"neuprint","url":"/docs/tutorials/apis/neuprint/"},{"body":"NBLAST NBLAST (Costa et al., 2016) is a method to quantify morphological similarity. It works on \u0026amp;ldquo;dotprops\u0026amp;rdquo; which represent neurons as tangent vectors. For each tangent vector in the query neuron, NBLAST finds the closest tangent vector in the target neuron and calculates a score from the distance between and the dotproduct of the two vectors. The final NBLAST score is the sum over all query-target vector pairs. Typically, this score is normalized to a self-self comparison (i.e. a perfect match would be 1).\nVFB computes NBLAST scores for all (?) neurons in its database. So if all you want is a list of similar neurons, it\u0026amp;rsquo;s fastest (and easiest) to get those directly from VFB.\n# Import libs and initialise API objects from vfb_connect.cross_server_tools import VfbConnect import navis.interfaces.neuprint as neu import pandas as pd import navis …","desc":"NBLAST is a method to quantify morphological similarity. Here we show you how to use it to programatically classify …","pinned":false,"section":"docs","title":"NBLAST","url":"/docs/past-workshops/connectome/5_nblast/"},{"body":"Many transforms to map between different Drosophila template brains are available.\nYou can see how to use the above in python using navis-flybrains or in R using nat.flybrains","desc":"Transformations to map between different canonical Drosophila templates.","pinned":false,"section":"docs","title":"Bridging Registrations","url":"/docs/concepts/bridging/"},{"body":"Neurons on VFB are annotated with cell types from the Drosophila Anatomy Ontology (FBbt).\nWhy do we use ontology terms? Each term represents a concept of a cell type, with a definition based on referenced publications: As well as a label, each term has a collection of synonyms, facilitating identification even when the same type has been referred to by different names in different sources: Hierarchical – e.g. specific terms for MBON01, MBON02 etc., but also grouped by a general MBON term and all under ‘adult neuron’ Neurons of the same type in multiple datasets can be linked to the same ontology term Persistent, resolvable identifiers to uniquely identify cell types e.g. http://virtualflybrain.org/reports/FBbt_00100234 We also use terms from the Drosophila Anatomy Ontology to annotate CNS regions (for the Template ROI Browser tool and neuron connectivity per region query) and other …","desc":"Cell type annotations on VFB.","pinned":false,"section":"docs","title":"Cell Types","url":"/docs/concepts/cell_types/"},{"body":"Some annotations on VFB are based on predictions, for example, predicted neurotransmitters for neurons in electron microscopy datasets (see below). Where available, we include the confidence of these predictions (as a badge next to the annotation), as well as a link to the publication - in the example below, this would be by clicking on the \u0026amp;lsquo;DOI\u0026amp;rsquo; badge.\nNeurotransmitter Prediction Confidence Values For MANC, optic-lobe and male-cns datasets, predictions are sourced from the neuprint predictedNt field, with data generated by Takemura et al. (2024), Nern et al. (2025) and Berg (2025), respectively. Predictions for FlyWire FAFB and Hemibrain datasets are conf_nt from Eckstein et al. (2024). BANC predictions are sourced from the Predicted NT type field of the Codex neurons.csv download and only used if not in conflict with the Verified NT type field, with data generated by Bates …","desc":"Confidence values for predictions on VFB.","pinned":false,"section":"docs","title":"Confidence Values","url":"/docs/concepts/confidence-value/"},{"body":"While most first-generation FlyLight expression pattern images are single 20x objective acquisitions, many Split-GAL4 lines were imaged using 63x or 40x objectives. This requires multiple image acquisitions to cover the CNS regions of interest. VFB integrates only the combined images derived from these tiles and lists the tiles used to create these combined images in the comment section for each image, for example: \u0026amp;ldquo;tile(s): \u0026amp;rsquo;left_dorsal, right_dorsal, ventral\u0026amp;rdquo;. These tiles do not necessarily cover the entire brain or VNC, which complicates the assessment of the specificity of these lines. VFB notes in the comment where the combined tiles do not provide full coverage. For further information refer to Figure 4 Supplemental File 1 from \u0026amp;lsquo;A split-GAL4 driver line resource for Drosophila CNS cell types\u0026amp;rsquo; direct link. This is a preprint and may be updated.\nSets of …","desc":"Tiles used during FlyLight Split-GAL4 expression pattern imaging.","pinned":false,"section":"docs","title":"FlyLight Imaging Tiles","url":"/docs/concepts/flylight_tiles/"},{"body":"Every figure on this page was checked against its primary paper. Where a figure could not be verified from the original, it is marked.\nThe short answer There isn\u0026amp;rsquo;t one number, and the reason is not evasion. Three things have to be pinned down before the question has an answer at all:\nWhich nervous system. \u0026amp;ldquo;Fly brain\u0026amp;rdquo; can mean the cephalic brain alone, the brain plus the ventral nerve cord (the whole central nervous system), or a subset such as the central brain without the optic lobes. Published figures use all of these boundaries and rarely say which in the headline.\nWhich animal. Every modern count comes from a connectome, and a connectome is one individual — one sex, one developmental stage, one specimen, at one moment in its life. None is a species average.\nCounted how. What a dataset calls a neuron is a decision recorded in its methods: whether boundary-truncated …","desc":"Why there is no single neuron count for Drosophila, what each connectome actually counted, and how to quote a figure …","pinned":false,"section":"docs","title":"Neuron counts","url":"/docs/concepts/neuron-counts/"},{"body":"","desc":"Virtual Fly Brain (VFB) is an interactive tool for neurobiologists to explore the detailed neuroanatomy, neuron …","pinned":false,"section":"about","title":"About","url":"/about/"},{"body":"Welcome to Virtual Fly Brain (VFB) - an interactive tool for neurobiologists to explore the detailed neuroanatomy, neuron connectivity, and gene expression of Drosophila melanogaster. Our goal is to make it easier for researchers to find relevant anatomical information and reagents.\nWe integrate the neuroanatomical and expression data from the published literature, and align image datasets onto the same nervous system templates, making it possible to run cross searches, find similar neurons, and compare image data on our 3D Viewer.","desc":"Virtual Fly Brain (VFB) - an interactive tool for neurobiologists to explore the detailed neuroanatomy, neuron …","pinned":true,"section":"about","title":"What is Virtual Fly Brain?","url":"/about/whatisvfb/"},{"body":"How to Cite Virtual Fly Brain If you use Virtual Fly Brain data, tools, or resources in your research, please cite our work using the citation formats below. Proper citation helps support continued development and funding of this resource.\nPrimary Citation Use this citation when referencing Virtual Fly Brain in general, including use of the website, data, or tools:\nAPA Court, R., Costa, M., Pilgrim, C., Millburn, G., Holmes, A., McLachlan, A., Larkin, A., Matentzoglu, N., Kir, H., Parkinson, H., Brown, N. H., O\u0026amp;rsquo;Kane, C. J., Armstrong, J. D., Jefferis, G. S. X. E., \u0026amp;amp; Osumi-Sutherland, D. (2023). Virtual Fly Brain—An interactive atlas of the Drosophila nervous system. Frontiers in Physiology, 14. https://doi.org/10.3389/fphys.2023.1076533\nMLA Court, Robert, et al. \u0026amp;ldquo;Virtual Fly Brain—An Interactive Atlas of the Drosophila Nervous System.\u0026amp;rdquo; Frontiers in Physiology, vol. …","desc":"Citation information for Virtual Fly Brain including APA, MLA, Chicago, and BibTeX formats. Use these citations when …","pinned":true,"section":"about","title":"How to cite us","url":"/about/citeus/"},{"body":"Virtual Fly Brain Privacy Notice Last updated: May 2026\nThe Virtual Fly Brain (VFB) project is committed to protecting your privacy and keeping you informed about how your personal information is used. This privacy notice explains how we collect, use, and protect your personal data when you visit our website.\nWhat information do we collect? Website analytics (Google Analytics 4). We use Google Analytics 4 to understand how the site is used. When you visit a page, your IP address is sent to Google for the purpose of deriving an approximate geographic location, and is then discarded; the raw IP is not stored against your analytics record. The data we receive is aggregated and includes:\nApproximate geographic location (city/country level) Browser type and version Operating system Referring website Pages visited and time spent on each page Web-server logs. Our front-end load balancers and …","desc":"Virtual Fly Brain Privacy Notice Last updated: May 2026\nThe Virtual Fly Brain (VFB) project is committed to protecting …","pinned":false,"section":"about","title":"Privacy Notice","url":"/about/privacy/"},{"body":"Cookies used by Virtual Fly Brain Last updated: May 2026\nThis page lists the cookies set by www.virtualflybrain.org and the related Virtual Fly Brain (VFB) services. It complements the Privacy Notice.\nWhat is a cookie? A cookie is a small text file that a website saves on your device when you visit. Cookies let a site recognise your browser between requests and on return visits.\nStrictly necessary cookies These cookies are required for the website and applications to work and cannot be turned off through any opt-out mechanism without breaking core features.\nName Set by Purpose Expires JSESSIONID Geppetto (v2.virtualflybrain.org and variants) Maintains your application session in the 3D web client — currently selected neurons, layer visibility and camera state. End of browser session Performance cookies These cookies help us understand how visitors use Virtual Fly Brain so we can improve …","desc":"Cookies used by Virtual Fly Brain Last updated: May 2026\nThis page lists the cookies set by www.virtualflybrain.org and …","pinned":false,"section":"about","title":"Cookies","url":"/about/cookies/"},{"body":"Accessibility statement for Virtual Fly Brain Website accessibility statement in line with the Public Sector Bodies (Websites and Mobile Applications) (No. 2) Accessibility Regulations 2018.\nThis statement applies to the public Virtual Fly Brain (VFB) services hosted at www.virtualflybrain.org and its sub-domains. VFB is run by the University of Edinburgh\u0026amp;rsquo;s School of Informatics as a research data integration platform for Drosophila melanogaster neurobiology.\nThe University of Edinburgh is committed to making its websites and applications accessible in line with the regulations. This website is partially compliant with the Web Content Accessibility Guidelines (WCAG) 2.2 AA standard, with the non-compliances listed below.\nWe want as many people as possible to be able to use this website. Where the underlying content is inherently visual (3D image stacks, neuron skeletons and …","desc":"Accessibility statement for Virtual Fly Brain Website accessibility statement in line with the Public Sector Bodies …","pinned":false,"section":"about","title":"Accessibility","url":"/about/accessibility/"},{"body":"Current Team Members Robert Court (Lead DevOps) [1]\nClare Pilgrim (Ontology Editor/Curator) [2]\nAlex McLachlan (Curator/UX Tester) [2]\nGillian Millburn (Senior Curator) [2]\nDouglas Armstrong (Current Project PI) [1]\nNick Brown (Current Project PI) [2]\nGreg Jefferis (Current Project PI) [4,5]\nDavid Osumi-Sutherland (Current Project Co-I) [3]\nMarta Costa (Current Project Co-I) [4]\nPast Team Members Nestor Milyaev (2009-2012) [1]\nAlex Holmes (2017-2019) [2]\nAoife Larkin (2017-2019) [2]\nHuseyin Kir (2021-2024) [6]\nNico Matentzoglu (2018-2022) [6]\nSimon Reeve (2009-2011) [7]\nNicole Staudt (2015-2016) [7]\nHelen Parkinson (Former PI) [6]\nCahir O\u0026amp;rsquo;Kane (Former PI) [7]\nMichael Ashburner (Original PI and Grant Holder) [7]\nAffiliations Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh Department of Physiology, Development and Neuroscience Wellcome …","desc":"Meet the interdisciplinary team behind Virtual Fly Brain based in leading UK institutions. Our collaborative approach …","pinned":false,"section":"about","title":"The Virtual Fly Brain Team","url":"/about/members/"},{"body":"Current Funding (2022-2027) Wellcome Trust Grant 223741/Z/21/Z\nDuration: April 1, 2022 to March 31, 2026 (extended to March 31, 2027) Supporting ongoing Virtual Fly Brain development and maintenance Historical Funding Wellcome Trust Grant 208379/Z/17/Z\nDuration: October 1, 2017 to October 1, 2021 \u0026amp;ldquo;Virtual Fly Brain: a global informatics hub for Drosophila neurobiology\u0026amp;rdquo;\nGrant 105023/D/14/Z: October 1, 2014 to January 31, 2018 Grant 105023/A/14/Z: October 1, 2014 to January 31, 2018 Grant 105023/C/14/Z: October 1, 2014 to September 30, 2017 Grant 105023/B/14/Z: October 1, 2014 to September 30, 2017 UK Research Councils Biotechnology and Biological Sciences Research Council (BBSRC)\nGrant BB/G02233X/1 (2009): \u0026amp;ldquo;Standardising the representation of Drosophila anatomy and development for databases\u0026amp;rdquo; Recipients: J. Douglas Armstrong, Michael Ashburner, Cahir O\u0026amp;rsquo;Kane, …","desc":"Virtual Fly Brain has been supported by grants from multiple UK research councils and charitable foundations since 2009.","pinned":false,"section":"about","title":"Funding","url":"/about/funding/"},{"body":"Collaborators The IIP3D server, Woolz software and client-side tools are developed by* MRC Human Genetics Unit (MRC HGU): Richard Baldock, Nick Burton, Bill Hill, Zsolt Husz (*) An on-going development of the client-side tools is done in collaboration between the MRC HGU and Edinburgh University\nVisit the EMAGE gene expression database to see other tools the MRC HGU have developed.\nExpression data is collaboratively curated by VFB and FlyBase and stored and maintained at FlyBase.\nPhenotype data is curated, stored, and maintained by FlyBase.","desc":"Collaborators The IIP3D server, Woolz software and client-side tools are developed by* MRC Human Genetics Unit (MRC …","pinned":false,"section":"about","title":"Collaborators","url":"/about/collaborators/"},{"body":"Contributors We would like to thank the following contributors for their help with this project:","desc":"A list of contributors to the VirtualFlyBrain project","pinned":false,"section":"about","title":"Contributors","url":"/about/contributors/"},{"body":"Geppetto is an open source project that VFB is an active developer of in partnership with MetaCell\nFor full details on the project see Geppetto.org\nGeppetto Build robust neuroscience applications.\n| Live demo | Paper | Docs | The visualisation and simulation platform focused on what matters to you.\nNeuroscience software reimagined Geppetto is a web-based visualisation and simulation platform to build neuroscience software applications. Reuse best practices, best compomnents, best design. Don\u0026amp;rsquo;t reinvent the wheel.\nA completely modular platform. Engineered together with scientists, Geppetto lets you integrate different data and models. A modular architecture allows the platform to easily support different standard formats for both experimental and computational data.\nAn open-source revolution. Geppetto is entirely open source and engineers, scientists and developers from different …","desc":"The ultimate neuroscience platform.","pinned":false,"section":"about","title":"Geppetto","url":"/about/geppetto/"},{"body":"Publications For more information on the technology behind the VFB website:\nRobert Court, Marta Costa, Clare Pilgrim, Gillian Millburn, Alex Holmes, Alex McLachlan, Aoife Larkin, Nicolas Matentzoglu, Huseyin Kir, Helen Parkinson, Nicolas H. Brown, Cahir J. O\u0026amp;rsquo;Kane, J. Douglas Armstrong, Gregory S. X. E. Jefferis and David Osumi-Sutherland (2023). Virtual Fly Brain - An interactive atlas of the Drosophila nervous system. Frontiers in Physiology 14. Matteo Cantarelli, Boris Marin, Adrian Quintana, Matt Earnshaw, Robert Court, Padraig Gleeson, Salvador Dura-Bernal, R. Angus Silver, Giovanni Idili (2018). Geppetto: a reusable modular open platform for exploring neuroscience data and models. Philosophical Transactions of the Royal Society B: Biological Sciences 373. Hilmar Lapp, James P. Balhoff, Todd J. Vision (2017). Owlery: A flexible approach for the serving of OWL ontologies. …","desc":"Publications For more information on the technology behind the VFB website:\nRobert Court, Marta Costa, Clare Pilgrim, …","pinned":false,"section":"about","title":"Publications","url":"/about/publications/"},{"body":"Contact Virtual Fly Brain We welcome feedback, questions, and collaboration opportunities from the Drosophila research community. Please choose the most appropriate contact method for your inquiry below.\nTechnical Support \u0026amp;amp; General Questions For help with using Virtual Fly Brain, comments, suggestions, or general questions:\nPublic Support Forum: support@virtualflybrain.org\nNote: This email goes to our public support forum, where questions and responses are visible to the community, helping other users with similar issues.\nBefore emailing: Please check our support forum archives to see if your question has already been answered.\nBug Reports \u0026amp;amp; Feature Requests To report technical issues, bugs, or request new features:\nGitHub Issues: Report an issue\nThis allows our development team to track and address technical problems efficiently.\nData Contributions \u0026amp;amp; Private Inquiries For …","desc":"Get in touch with the Virtual Fly Brain team for technical support, bug reports, data contributions, collaboration …","pinned":false,"section":"about","title":"Contact us","url":"/about/contactus/"},{"body":"","desc":"By maintaining these archives, VFB ensures that valuable scientific resources remain accessible to researchers even …","pinned":true,"section":"hosted","title":"Hosted Sites","url":"/hosted/"},{"body":"Access The ABD1.5 CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of their mission to integrate and preserve key Drosophila neuroscience datasets. The instance is accessible at: https://abd1.5.catmaid.virtualflybrain.org/\nThis resource provides wild-type reference data for the abdominal nerve cord of first instar Drosophila larvae, serving as a baseline for comparative connectomics studies. Virtual Fly Brain ensures its long-term availability to the research community.\nSource Publications This dataset contains neuron reconstructions from multiple foundational connectomics studies:\nOhyama T, Schneider-Mizell CM, Fetter RD, Aleman JV, Franconville R, Rivera-Alba M, et al. (2015). A multilevel multimodal circuit enhances action selection in Drosophila. Nature, 520(7545), 633-639. https://doi.org/10.1038/nature14297\nSchneider-Mizell CM, Gerhard S, Longair M, …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts the neuroanatomical …","pinned":false,"section":"hosted","title":"ABD1.5 CATMAID Data Viewer","url":"/hosted/abd1-5-catmaid/"},{"body":"https://braintrap.virtualflybrain.org/\nOverview BrainTrap was developed as part of a large-scale collaborative research project to collect protein expression location information from protein-trap lines generated by the Cambridge Protein Trap (CPT) project. The database provides detailed 3D confocal datasets showing protein expression patterns throughout the adult Drosophila brain.\nFeatures Interactive web-based viewer for exploring 3D confocal datasets Full-size images throughout the brain volume can be viewed interactively Secondary immunohistochemical label (anti-brp) aids navigation and helps identify brain structures Searchable annotations linked to the FlyBase Drosophila anatomy ontology Anatomical search criteria can be specified using: Automatic term completion Hierarchical browser for the ontology Annotation provenance tracking with highlighted expression locations Downloadable …","desc":"BrainTrap is a database of 3D protein expression patterns in the Drosophila brain, providing interactive visualization …","pinned":false,"section":"hosted","title":"BrainTrap","url":"/hosted/braintrap/"},{"body":"\nAccess The FAFB CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of their mission to integrate and preserve key Drosophila neuroscience datasets. The instance is accessible at: https://fafb.catmaid.virtualflybrain.org/\nThis resource provides access to the FAFB dataset and its associated neural reconstructions. Virtual Fly Brain ensures its long-term availability to the research community.\nSource Publications Primary Resources FAFB Project: https://flyconnecto.me/ EM Dataset: Zheng Z, et al. (2018) A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster. Cell, 174(3), 730-743.e22. https://doi.org/10.1016/j.cell.2018.06.019 Contributing Publications The database includes neurons traced and published in numerous studies. Each neuron is annotated with its source publication. Major contributing publications include:\nCircuit …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts neuroanatomical data …","pinned":false,"section":"hosted","title":"FAFB CATMAID Data Viewer","url":"/hosted/fafb-catmaid/"},{"body":"\nAccess The FANC CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of their mission to integrate and preserve key Drosophila neuroscience datasets. The instance provides two views of the data:\nOriginal EM stack and tracings (Project ID 1): https://fanc.catmaid.virtualflybrain.org/\nNeurons aligned to JRC2018 VNC female template (Project ID 2): https://fanc.catmaid.virtualflybrain.org/\nThis resource provides access to both the original FANC dataset and template-aligned reconstructions. Virtual Fly Brain ensures its long-term availability to the research community.\nSource Publication The data is from the research published in:\nFushiki A, et al. (2021) A circuit mechanism for the propagation of waves of muscle contraction in Drosophila. Cell, 184(3), 759-774.e20. https://doi.org/10.1016/j.cell.2020.12.013\nsee: https://www.lee.hms.harvard.edu/resources\nDataset …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts neuroanatomical data …","pinned":false,"section":"hosted","title":"FANC CATMAID Data Viewer","url":"/hosted/fanc-catmaid/"},{"body":"https://flybrain-ndb.virtualflybrain.org/\nDatabase Structure FLYBRAIN NDB is a relational database searchable by two main criteria:\n1. Neuron Search Search neurons by:\nName (full or partial) Areas of arborizations Distribution of pre- or postsynaptic sites (where known) Keywords in documentation 2. Strain/Antibody Search Search molecular markers including:\nAntibodies Drosophila strains (e.g., GAL4 enhancer-trap lines) Specific neural pathways Data Categories Visual Neurons Olfactory Neurons Auditory Neurons Gustatory Neurons Central Complex Neurons Clonal Units Brain Region Definitions GAL4 and LexAV Strain Collections Brain Explorer Features The online brain browsing system allows users to:\nRotate volume-rendered images Create sections Adjust magnification Browse confocal data Terms of Use Access FLYBRAIN NDB is freely available No login or registration required Copyright All contents © …","desc":"FLYBRAIN NDB is an extension to the Flybrain, an Online Atlas of the Drosophila Nervous System. It collected images and …","pinned":false,"section":"hosted","title":"FLYBRAIN Neuron Database (NDB)","url":"/hosted/flybrainndb/"},{"body":"Access The IAV-ROBO CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of their mission to integrate and preserve key Drosophila neuroscience datasets. The instance is accessible at: https://iav-robo.catmaid.virtualflybrain.org/\nThis resource provides a direct view into the dataset used to study the organization of proprioceptive circuits in Drosophila larvae. Virtual Fly Brain ensures its long-term availability to the research community.\nSource Publication This data is from the research published in:\nValdes-Aleman J, et al. (2021). Comparative Connectomics Reveals How Partner Identity, Location, and Activity Specify Synaptic Connectivity in the Drosophila Motor System. Neuron, 109(1), 105-120.e7. https://doi.org/10.1016/j.neuron.2020.10.004\nDataset Contents The viewer provides access to:\nSerial section electron microscopy data of Drosophila 1st instar larval …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts the neuroanatomical …","pinned":false,"section":"hosted","title":"IAV-ROBO CATMAID Data Viewer","url":"/hosted/iav-robo-catmaid/"},{"body":"Access The IAV-TNT CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of their mission to integrate and preserve key Drosophila neuroscience datasets. The instance is accessible at: https://iav-tnt.catmaid.virtualflybrain.org/\nThis resource provides a direct view into the dataset used to study the organization of proprioceptive circuits in Drosophila larvae. Virtual Fly Brain ensures its long-term availability to the research community.\nSource Publication This data is from the research published in:\nValdes-Aleman J, et al. (2021). Comparative Connectomics Reveals How Partner Identity, Location, and Activity Specify Synaptic Connectivity in the Drosophila Motor System. Neuron, 109(1), 105-120.e7. https://doi.org/10.1016/j.neuron.2020.10.004\nDataset Contents The viewer provides access to:\nSerial section electron microscopy data of Drosophila 1st instar larval CNS …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts the neuroanatomical …","pinned":false,"section":"hosted","title":"IAV-TNT CATMAID Data Viewer","url":"/hosted/iav-tnt-catmaid/"},{"body":"\nAccess The Larva1099 CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of its mission to integrate and preserve key Drosophila neuroscience datasets. The instance is accessible at: https://larva1099.catmaid.virtualflybrain.org/\nThis is a distinct volume from the L1EM serial-section TEM dataset also hosted by VFB: it is a separate first instar larva (sample 1099) imaged by enhanced focused-ion-beam scanning electron microscopy (eFIB-SEM) at 12 × 12 × 12 nm/voxel isotropic resolution.\nSource Publication The volume and reconstructions are from:\nRandel N, Wang C, Clayton MS, Wang K, Pang S, Xu CS, Champion A, Hess HF, Cardona A, Keller PJ, Zlatic M (2026) Combining brain-wide activity imaging with electron microscopy reveals a distributed nociceptive network in the brain. bioRxiv 2025.09.25.678485. https://doi.org/10.1101/2025.09.25.678485 (preprint, in revision) …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts the enhanced …","pinned":false,"section":"hosted","title":"L1 CNS (Larva1099) eFIB-SEM CATMAID Data Viewer","url":"/hosted/larva1099-catmaid/"},{"body":"\nAccess The L1EM CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of their mission to integrate and preserve key Drosophila neuroscience datasets. The instance is accessible at: https://l1em.catmaid.virtualflybrain.org/\nThis resource provides access to the L1EM dataset and its associated neural reconstructions. Virtual Fly Brain ensures its long-term availability to the research community.\nSource Publication Primary Resource L1EM Dataset: Winding M, et al. (2023) The connectome of an insect brain. Science, 379(6636):eadd9330. https://doi.org/10.1126/science.add9330 Contributing Publications The database includes neurons traced and published in numerous studies. Each neuron is annotated with its source publication. Major contributing publications include:\nAndrade et al. (2019) Barnes et al. (2022) Berck, Khandelwal et al. (2016) Burgos et al. (2018) …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts neuroanatomical data …","pinned":false,"section":"hosted","title":"L1EM CATMAID Data Viewer","url":"/hosted/l1em-catmaid/"},{"body":"Access The L3VNC CATMAID instance is hosted and maintained by Virtual Fly Brain (VFB) as part of their mission to integrate and preserve key Drosophila neuroscience datasets. The instance is accessible at: https://l3vnc.catmaid.virtualflybrain.org/\nThis resource provides access to the L3 VNC dataset and its associated neural reconstructions. Virtual Fly Brain ensures its long-term availability to the research community.\nSource Publication Contributing Publications Gerhard S, Andrade I, Fetter RD, Cardona A, Schneider-Mizell CM (2017) Conserved neural circuit structure across Drosophila larval development revealed by comparative connectomics. eLife, 6:e29089. https://doi.org/10.7554/eLife.29089 Dataset Contents The viewer provides access to:\nSerial section electron microscopy volume of a Drosophila third instar larva ventral nerve cord Manually traced neuron reconstructions Synaptic …","desc":"This CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) instance hosts neuroanatomical data …","pinned":false,"section":"hosted","title":"L3VNC CATMAID Data Viewer","url":"/hosted/l3vnc-catmaid/"},{"body":"","desc":"","pinned":true,"section":"blog","title":"News","url":"/blog/news/"},{"body":"We\u0026amp;rsquo;re excited to announce the launch of the Virtual Fly Brain Model Context Protocol (MCP) Tool — a powerful new way to explore VFB data through Large Language Models (LLMs) like Claude, providing natural language access to VFB\u0026amp;rsquo;s comprehensive neuroanatomical database.\nWhat is the VFB MCP Tool? The VFB MCP tool integrates Virtual Fly Brain\u0026amp;rsquo;s rich neuroanatomical data with LLM capabilities, allowing researchers to:\nQuery VFB data conversationally - Ask questions about neurons, brain regions, and connectivity in natural language Discover morphological similarities - Use integrated NBLAST queries to find similar neurons Explore neuroanatomical concepts - Get detailed information about transgenic constructs, neuron types, and anatomical structures Visualize results - Access 3D visualizations of neurons and brain regions directly in your chat interface How It Works The tool …","desc":"Explore Virtual Fly Brain data directly through your favorite LLM with the new VFB Model Context Protocol (MCP) tool - …","pinned":false,"section":"blog","title":"VFB MCP Tool Launch","url":"/blog/2026/06/21/introducing-the-vfb-model-context-protocol-mcp-tool/"},{"body":"The 21st Biennial European Drosophila Neurobiology Conference, NeuroFly 2026, will be held at the University of Cologne, Germany, from September 7th to 11th, 2026. This conference continues the tradition of bringing together researchers from around the world to discuss the latest advancements in Drosophila neurobiology, including studies on neuronal circuits, synapses, connectome, signal transmission, behavior, development, and more. The event emphasizes interdisciplinary interactions in an inclusive environment, featuring a mix of invited talks and abstracts from both junior and senior researchers.\nVirtual Fly Brain (VFB) is excited about the upcoming conference and is exploring opportunities to host a workshop/tutorial on connectomics and transcriptomics data we have available, similar to our previous successful sessions such as the one at NeuroFly 2024 titled \u0026amp;lsquo;Bridging …","desc":"Announcement of NeuroFly 2026 conference in Cologne, Germany, and VFB\u0026#39;s plans for a workshop.","pinned":false,"section":"blog","title":"NeuroFly 2026","url":"/blog/2025/12/17/neurofly-2026-21st-biennial-european-drosophila-neurobiology-conference/"},{"body":"VFB works closely with the FlyBase team and we rely on their hard work to provide many of our features. Due to the current funding situation at FlyBase, we encourage our users to donate and help maintain this essential resource.\nBecause of recent NIH impediments to international collaboration, FlyBase has divided forces:\nEuropean labs can contribute via this crowdfunding link, where you can also request to make a donation from a grant. the FlyBase U.S. fee system will be set up soon. We are grateful to the Wellcome Trust for their continued funding of VFB.","desc":"Please consider donating to support FlyBase","pinned":false,"section":"blog","title":"FlyBase Funding","url":"/blog/2025/06/11/flybase-emergency-funding/"},{"body":"VFB provides precomputed NBLAST scores to assist in the discovery of 1) morphologically similar neurons within and across connectome datasets and 2) potential split-GAL4 driver lines which label neurons from connectomic datasets.\nNBLAST queries can be accessed in the term info panel for individuals (neuron/split-GAL4 images) (at bottom of the example image below). The queries are only available if there are any results for that individual.\nNeuron-to-neuron example match:\nNeuron-to-split-GAL4 expression pattern example match:\nDatasets included VFB NBLAST scores cover all available neurons and expression patterns on VFB, including some of the largest and most comprehensive individual neuron datasets:\nSingle Neuron/Connectome Datasets:\nFAFB-FlyWire (v783) - FlyWire Codex - on VFB Male-CNS optic lobe (v1.0.1) - neuprint optic-lobe - on VFB FlyCircuit (1.0) - site - on VFB FAFB-CATMAID (all …","desc":"VFB neuron-to-neuron and neuron-to-expression pattern similarity scores (NBLAST) have been expanded to include the …","pinned":false,"section":"blog","title":"Updated NBLAST scores","url":"/blog/2025/05/29/new-precomputed-neuron-and-expression-pattern-similarity-scores-on-vfb/"},{"body":"The Drosophila neuroscience community now has access to extensive connectomics data covering the majority of the fly brain, along with a growing collection of transcriptomics data for brain cell types. Virtual Fly Brain integrates Drosophila neuroscience data, providing a platform for bridging these two data types and making them accessible for display, querying, and download.\nThe workshop will introduce the VFB API through practical examples, including querying and retrieving connectomics and transcriptomics data and integrating these data types in custom queries. This session is designed for experienced users of R or Python.\nThank you to everyone who attended our workshop at NeuroFly 2024. Our workshop notebook is available here: tinyurl.com/workshopVFB for anyone who would like to explore the VFB API. We will keep this available and up to date.","desc":"VFB will be running a workshop on integrating Drosophila connectomics and transcriptomics data.","pinned":false,"section":"blog","title":"NeuroFly 2024","url":"/blog/2024/08/25/vfb-workshop-bridging-connectomics-and-transcriptomics-at-neurofly-2024/"},{"body":"Prerequisites Before starting, ensure you have the VFBConnect library installed. The recommended Python version is 3.10.14, as this version is tested against the library.\npip install vfb-connect Importing the VFBConnect Library Start by importing the VFBConnect library. This library provides a simple interface to interact with neuron data from the Virtual Fly Brain.\nfrom vfb_connect import vfb Retrieving Neuron Data To work with specific neurons, you can use the vfb.term() function. This function takes a unique identifier (e.g., ID, label, synonym) for the neuron.\nExample: Retrieving a Single Neuron neuron = vfb.term(\u0026amp;#39;5th s-LNv (FlyEM-HB:511051477)\u0026amp;#39;) The neuron variable now holds data about the neuron identified by the given term.\nWorking with Neuron Data The retrieved neuron object can provide different representations of neuron data, such as its skeleton, mesh, and volume. …","desc":"This guide will help you use the VFBConnect library to interact with Virtual Fly Brain (VFB) data, specifically focusing …","pinned":false,"section":"blog","title":"Neuron display in VFBconnect","url":"/blog/2024/08/25/guide-to-working-with-images-from-virtual-fly-brain-vfb-using-the-vfbconnect-library/"},{"body":"We now incorporate neurotransmitter predictions for Hemibrain neurons from Eckstein et al. (2024) (conf_nt predictions) and MANC neurons from Takemura et al. (2023), with more coming soon.\nNote that these studies only worked with a limited set of neurotransmitters and assumed a single neurotransmitter per neuron (see publications for further detail). Neurons with fewer than 100 presynapses are also excluded.\nPredictions appear under \u0026amp;lsquo;Relationships\u0026amp;rsquo; in the Term Info if available for a particular neuron, giving the confidence level and reference publication.","desc":"Predicted neurotransmitters for EM neurons are now being shown on VFB! They are currently available for Hemibrain and …","pinned":false,"section":"blog","title":"Neurotransmitter Predictions","url":"/blog/2024/07/29/predicted-neurotransmitters-for-em-data-now-available-on-vfb/"},{"body":"FlyBase has added report pages for split driver lines, where two (or more) hemidrivers are combined to restrict functional driver expression to the intersection of the hemidriver expression patterns. These are predominantly \u0026amp;lsquo;split GAL4’ system combinations using a DBD and AD line currently, but more complex combinations may be included in the future.\nThese new ‘split system combinations’ pages (e.g. GAL4DBD.R72B05∩Hsap\\RELAAD.ple), include information specific to that combination. Currently these include the FlyBase identifier (FBco), synonyms, the transgenic hemidriver alleles that make up the combination, expression pattern data and links to stocks corresponding to the combination.\nThe combination pages have been added to hitlists, so if you search for a regulatory region that drives expression of one of the hemidrivers in a combination (e.g. R72B05, VT064569, ple) you will see …","desc":"FlyBase now has \u0026#39;Split System Combination\u0026#39; pages displaying curated information for Split-GAL4 lines.","pinned":false,"section":"blog","title":"FlyBase Split-GAL4 reports","url":"/blog/2024/04/30/new-split-gal4-report-pages-on-flybase/"},{"body":"We now incorporate scRNAseq data from multiple studies, including the Fly Cell Atlas project and other datasets that identify nervous system cell types. These datasets can be found by searching for \u0026amp;lsquo;scRNAseq\u0026amp;rsquo; and filtering to \u0026amp;lsquo;Dataset\u0026amp;rsquo;.\nClusters can be found for particular cell types via the \u0026amp;lsquo;Single cell transcriptomics data for..\u0026amp;rsquo; query on the cell type Term Info pane.\nGenes for a particular cluster can be filtered by function and sorted by expression level and extent (proportion of cells in cluster expressing the gene). As with other VFB search results, these can be exported as a csv. Note that we currently only include genes that have extent \u0026amp;gt; 0.2 in a cluster.\nData can also be retrieved using VFB_connect.\nWe pull scRNAseq data from FlyBase, which takes it from the Single Cell Expression Atlas.","desc":"Transcriptomics data is now available on VFB! Find scRNAseq clusters from the Term Info pane for a cell type of …","pinned":false,"section":"blog","title":"Transcriptomics data","url":"/blog/2024/04/10/transcriptomics-data-available-for-cell-types-on-vfb/"},{"body":"The raw image data for the larval split-GAL4 and Gen 1 LexA lines from Meissner, G. W., et al. \u0026amp;ldquo;A split-GAL4 driver line resource for Drosophila CNS cell types.\u0026amp;rdquo; bioRxiv (2024)._ DOI: 10.1101/2024.01.09.574419 is available on VFB here: https://raw.larval.flylight.virtualflybrain.org/\nFor each image, the confocal stack in TIFF format can be downloaded by selecting an image or all TIFF stacks and z-projections for a line can be downloaded by selecting a line name. Line and image names can be searched at the top right.\nWe are working on fully integrating these images into VFB. This raw data site does not include full genotypes but these will be included in the curated release on the main VFB site in a later release.\nThe larval split-GAL4 line release was annotated and provided via the Zlatic lab","desc":"Raw images from the 1020 Larval split-GAL4 lines and 350 Generation 1 LexA lines included in Meissner et al., 2024 are …","pinned":false,"section":"blog","title":"Larval split-GAL4 raw images","url":"/blog/2024/03/19/larval-split-gal4-line-raw-images-release/"},{"body":"We are aware that VFB\u0026amp;rsquo;s secure websocket connection is failing to connect on the latest version of Safari on MacOS meaning that no data can be pulled from our servers.\nThe issue is down to an \u0026amp;rsquo;experimental feature\u0026amp;rsquo; (NSURLSession WebSocket) being set to ON by default which seams to break most sites using secure websockets.\nTo turn resolve this issue turn this off:\nIn Safari on the desktop: Under \u0026#39;Develop\u0026#39; expand \u0026#39;Experimental Features\u0026#39; untick \u0026#39;NSURLSession WebSocket\u0026#39; If you don’t see the Develop menu in the menu bar, choose Safari \u0026amp;gt; Preferences, click Advanced, then select “Show Develop menu in menu bar”. Restarting Safari may be necessary if the list of features under \u0026amp;lsquo;Experimental Features\u0026amp;rsquo; are greyed out.\nOn IOS devices: Open the Settings app Scroll down and tap on “Safari” Scroll down to the bottom and tap on “Advanced” Tap on “Experimental Features” …","desc":"Virtual Fly Brain 3D browser won\u0026#39;t load on Safari Version 15.3 (17612.4.9.1.8) with an experimental feature enabled. …","pinned":false,"section":"blog","title":"Safari Issue Fix","url":"/blog/2022/03/11/fix-for-problems-with-current-safari-release/"},{"body":"Google Statement:\nDecember 6-14 An internal issue caused an increase of redirect errors during indexing, and associated email notifications. This was not due to any website issues, but was due to an internal Google issue. Pages that were dropped from the index have been scheduled for recrawling. ","desc":"VFB has been affected by an issue with google\u0026#39;s indexing which has affected our ranking in their search results.  We …","pinned":false,"section":"blog","title":"Google Issue","url":"/blog/2021/12/18/google-search-results-issue/"},{"body":"","desc":"Virtual Fly Brain (VFB) is regularly updated with new data and software improvements. Information about the latest …","pinned":false,"section":"blog","title":"Releases","url":"/blog/releases/"},{"body":"VFB Content Report 2026-08-13 Report of content found at http://pdb.virtualflybrain.org on Thu, 13 Aug 2026 01:22:09\nOntology Content\nAnatomy Classes Publications All Terms 28772 2224 All Nervous System Parts 21825 1682 All Neurons 18303 1298 Characterised Neurons 6895 1291 Provisional Neurons 11408 40 All Nervous System Regions 1435 271 Synaptic Neuropils 625 151 Neuron Projection Bundles 600 142 Cell Body Rinds 132 11 Sense Organs 854 183 155175 formal assertions, of which 69533 are SubClassOf assertions and 85642 are other relationship types\n140998 formal assertions on nervous system components, of which 61384 are SubClassOf assertions and 79614 are other relationship types\nImage Content (excludes hemibrain v1.0.1)\n608512 total images from 136 datasets 521957 single neuron images of 14363 cell types 46896 images of expression patterns of 17593 drivers 5947 images of expression …","desc":"An auto generated report detailing the current types and quantity of images and annotations currently publicly available …","pinned":false,"section":"blog","title":"Content Report","url":"/blog/2022/01/01/vfb-content-report/"},{"body":"See VFB GitHub Repo geppetto-vfb releases page for full details\nv2.2.8.6 v2.2.8.5 v2.2.8.4 v2.2.8.3 v2.2.8.2 v2.2.8.1 v2.2.8.0 v2.2.7.2 v2.2.7.1 v2.2.7.0","desc":"Release details for Virtual Fly Brains 3D browser site built on the Geppetto platform","pinned":false,"section":"blog","title":"VFB Geppetto Releases","url":"/blog/1/01/01/vfb-geppetto-releases/"}]