A massive e ort is underway to map the structure of the Drosophila nervous system and to genetically dissect its function. Virtual Fly Brain (VFB; http://www.virtualflybrain.org) is a popular, OWL-based resource providing neuroinformatics support for this work. It provides: curated descriptions of brain regions and neurons; queries for neurons based on their relationship to gross neuroanatomy; and queries for reagents based on their expression patterns. Query results are enriched by OWL axiomatisation allowing basic mereological reasoning. To keep reasoning fast and scalable, VFB con nes expressiveness to the EL pro le of OWL. As a result, VFB does not provide queries involving negation, despite there being both demand and su cient information to support them. Recent developments in reasoning technology may make more expressive queries practical. Here we present design patterns to support queries with negation that are compatible with the mereological reasoning used in VFB.
1.1
A massive e ort is underway to map the neural circuitry of the Drosophila
nervous system and to genetically dissect its function. New microscopy and image
analysis techniques are facilitating the collection and integration of the large
3D image data sets required to map the structure and connectivity of the
nervous system down to the single neuron level [
The adult Drosophila nervous system contains an estimated 200,000 neurons. These can be grouped into classes that share characteristics such as similar lineage, morphology and location. Each brain includes multiple members of most classes, so the number of such classes is likely to be much lower than the number of neurons - proabably by at least an order of magnitude.
Mapping the neural circuitry of Drosophila requires ways to track the classi cation of these neurons and their properties, including their relationships to each other and to the gross anatomy of the nervous system, musculature, sense organs and neuro-endocrine system. This work requires the synthesis of many qualitative assertions from the literature and their integration with information from bulk data sources, much of it quantitative. OWL is an ideal technology for building and maintaining these queryable classi cations. Although there will always be a need for direct mathematical access to quantitative data, if suitable cuto s can be chosen to make qualitative assertions from quantitative data, OWL provides a means to integrate qualitative and quantitative data into a queryable whole.
Modulating the activity of particular neuron classes requires nding reagents whose expression is su ciently speci c. Finding such reagents frequently requires mining 3D image data of expression patterns. Integrating the phenotypic results of modulating neuronal activity into the bigger picture of nervous system function requires ways to keep track of the phenotypes associated with modulating the neuronal activity of connected neurons. Annotation with OWL ontology terms - either semi-formalised in a database or fully formalised in an OWL knowledgebase provides a means of storing this information in queryable form. 1.2
The Drosophila anatomy ontology. Virtual Fly Brain is built around the
Drosophila anatomy ontology (DAO) [
OWL queries and design patterns for neuroanatomy. The DAO uses an
integrated set of relations and design patterns to classify neurons according to
their location, connectivity, lineage and function [
The typical mereological relationship between a neuron and gross neuroanatomy is overlap: most neurons have parts in many parts of the brain. In an insect brain, each neuron has a cell body (soma) in the cortex and many have long, branching projections that extend to multiple brain regions. Projections bundle (fasciculate) together to form tracts. On exiting a tract, the projection enters a region called neuropil where it typically branches extensively and connects to other neuron projections via synapses.
The Drosophila brain contains many neuron classes that can be de ned via some combination of: soma location, tracts fasciculated with; neuropils in which they form input or output synaptic connections with other neurons; neuron classes synapsed with; the developmental origin of the neuron. The DAO takes advantage of this to automate classi cation of neurons based on these properties via EquivalentClass expressions.
Central to the basic mereological reasoning on VFB is an overlaps relation de ned using part of and its inverse has part
X overlaps Y i : exists some Z and X part of Z and Y has part Z2 part of subPropertyOf overlaps has part subPropertyOf overlaps has part o part of subPropertyOf overlaps overlaps o part of subPropertyOf overlaps has part o overlaps subPropertyOf overlaps
The property chains allow inference over partonomy. This is central to the function of the query system on VFB - allowing queries for overlap from any level of granularity in the partonomy. 2 part of and has part are both transitive and re exive; part of inverseOf has part
Typically, overlaps is too abstract to be directly useful in class restrictions. Instead we use a range of subproperties of overlaps that record something useful about the nature of the overlap, such as which tract(s) a neuron fasciculates with and which neuropils it forms synapses in. Like overlaps, relations recording synaptic terminal location also propagate over partonomy via property chains, allowing queries from any level of the partonomy. For example: has synaptic terminal in o part of subPropertyOf has synaptic terminal in has part o has synaptic terminal in subPropertyOf has synaptic terminal in
VFB also provides combinatorial query functionality, via its query builder
tool3 , allowing users to query for neurons based on their pattern of synapsing.
This functionality is currently limited to query legs combined with `and ', and
does not support negation. (See [
Neurobiology is a very visual subject. While it is useful to read both informal
and formal descriptions of neuron classes and brain regions, there is no substitute
for being able to see images of them. VFB is built around a standard 3D adult
brain template. Major brain regions are de ned as 3D painted regions on this
template according to an expert-de ned standard [
This indicates that the individual provides a standard reference for the boundaries of a brain region. A simple DL query is used to nd images to illustrate term pages for these brain regions.
VFB also incorporates large datasets of 3D images of single neurons (>16,000), neuron clones (>200) and expression patterns (>3500). As for painted brain regions, structures depicted in these images are modelled as OWL individuals. Importantly, all of these images are registered (morphed) onto the standard brain. This allows direct comparison - both automated and manual - of registered images.
From image analysis, we can determine which gross brain regions a neuron, clone or expression pattern overlaps, recording this using a Type statement on the individual. These axioms drive queries for single neuron images by location ( gure 1). A more sophisticated form of image analysis, developed by G Je eris [unpublished], compares pairs of neurons, giving each pair a similarity score based on morphology and location. A clustering algorithm is then used to group neurons with similar morphology and location and to assign an exemplar neuron for each cluster.
We treat clusters as individuals, with single neurons standing in a member of relationship to a cluster. A subproperty of member of, exemplar of, is used to relate exemplars to clusters. This simple formalism allows VFB to group the very large numbers of images that often result from queries of brain regions for overlapping neurons into a much smaller number of clusters of similar neurons (see gure 1).
In many cases, the resulting clusters correspond largely or completely to well characterized neurons from the literature, for which the DAO has classes de ned by lineage, tract and location of synaptic connections. Where this is the case, we add manual typing statements. In other cases, manual annotation of neurons with Type statements provides su cient information for automated classi cation in the ontology. 2.1
A key aim of VFB is to provide a means for biologists to nd candidate transgenes with expression patterns suitable for targeting expression to speci c neurons or brain regions.
To formalise what we mean by expression pattern, we rst de ne a relation, expresses: expresses: This relation holds between an anatomical entity (a) and a gene or transgene (g) where the anatomical entity is either a cell or has cells as a part and in all of those cells, some instance of GO:gene expression that has input g is occuring.
We use this relation to record expression in any type of cell or multicellular anatomical entity including single neurons, neuron clones and complete expression patterns. Images of single neurons and neuron clones typically depict only fragments of expression patterns. To keep these separable from images of complete expression patterns, we de ne an expression pattern as an anatomical entity consisting of mereological sum of all cells that express a particular gene or transgene. This is axiomatised using the following pattern: `gene B expression pattern' EquivalentTo: `expression pattern' that expresses some `gene B'
sion pattern'
Classi cation time for the full ontology combined with the knowledgebase (> 21,000 expression patterns, clones and neurons) is 1500ms. DL query answering time, taking advantage of incremental reasoning with ELK, is under 100ms.
Representation of expression patterns in the knowledgebase is currently used on VFB to provide images of transgene expression patterns found via SQL queries. The above formalisation provides an obvious way to convert the semiformalised annotations in SQL to OWL.
For brain regions: `expression pattern of X' overlaps some `brain region Y' (see gure 3 for an example)
`expression pattern of X' has part4 some `cell Y'
We can then nd anatomical structures in which there is some expression via \overlaps some X"
As discussed in the next section, this formalisation can be used to as part of a pattern that allows safe queries for expression patterns involving negation. 4 has part entails overlaps
In order to remain computationally tractable and scalable, VFB restricts
expressiveness to the EL pro le of OWL and uses the ELK reasoner [
Some types of queries that would be extremely useful to our users require more expressiveness. In particular, there are a number of cases where queries involving negation would be useful. For example, for some neurons, we know all of the brain regions overlapped, all of the tracts fasciculated with and the location of all synaptic terminals. It would be useful, in such cases, to allow users to add negative legs to the compound queries for neuron classes that VFB already supports. For some transgene expression patterns in the adult brain, we have both negative and positive assertions about where a transgene is expressed. It would be very useful for researchers to be able to add negative clauses to queries for expression as this can be critical for choosing speci c reagents that can be used to modulate the activity of particular neurons to assess their function.
The most e cient way to support queries involving negation is to combine closure axioms and disjointness declarations. For example, the neuron DL1 adPN fasciculates with only one tract, the mALT. We currently record this as: `DL1 adPN' subClassOf fasciculates with some mALT
`DL1 adPN' subClassOf fasciculates with only mALT `great commissure' disjointWith mALT
neuron and not (fasciculates with some `great commissure')
For cases where a neuron fasciculates with multiple tracts, the closure axioms can simply combine multiple classes using or. Unfortunately, our use of inference over partonomy rules out this pattern of closure axioms for many important relations used in querying. For example: overlaps o part of subPropertyOf overlaps
Z disjointWith X =>inconsistency: X overlaps some Z, X not (overlaps some Z)
We can get around this by using closure axioms of the form \rel only (has part some X)" and declaring spatial disjointness between brain regions (which also provides a useful integrity check). Spatial disjointness can be declared using a simple GCI: part of some X disjointWith part of some Y
For example, we can represent that the neuron DL1 adPN only has synaptic terminals in DL1 (part of the antennal lobe) and the lateral horn5 with: `DL1 adPN' subClassOf : has synaptic terminals in some DL1 subClassOf : has synaptic terminals in some `lateral horn' subClassOf : has synaptic terminals in only (has part only (DL1 or `lateral horn')) `fan-shaped body' subClassOf : part of some `central complex' DL1 subClassOf : part of some `antennal lobe'
With `antennal lobe', `lateral horn' and `central complex' declared spatially disjoint , DL1 adPN is returned by the query: neuron that (has synaptic terminal in some `antennal lobe') and not (has synaptic terminal in some `fan-shaped body')
An explanation is shown in gure 2B. There is no need to assert has part relationships. The inverseOf axiom between has part and part of is su cient to infer not has part from spatial disjointness axioms ( gure 2A).
This pattern is also dependent on the re exive nature of part of and has part ( gure 2B).
Negative query legs in compound queries for expression patterns would be
especially useful to our users. Our ability to provide these is limited by the extent
to which it is possible to specify which regions lack expression. It is generally
not possible to provide an exhaustive list of all regions lacking expression to use
to de ne a closure axiom. However some datasets come with explicit assertions
about regions not overlapped. For example, the largest transgene expression
dataset that VFB currently hosts [
Virtual Fly Brain uses OWL to provide a unique service to the Drosophila neurobiology community, integrating a wealth of information from the literature 5 There is actually one additional region, but we simplify here in order to provide a more compact example and bulk datasets into an easily queryable resource. Much of this would be difcult or impossible to provide using a conventional relational database. OWL provides a sustainable way to develop and maintain a queryable classi cation of anatomical structures and neurons. OWL axiomatisation allowing inference over partonomy drives queries that return complete information about neuronal overlap and synaptic terminal location from any level of the partonomy. OWL reasoning also provides a way to group annotations of expression and phenotypes based on classi cation, partonomy and cell overlap. This massively enriches the results of annotation queries.
VFB has so far avoided taking advantage of the full expressiveness of OWL.
Restricting expressiveness to the EL pro le allows us to use the ELK reasoner,
which gives classi cation and query answering times suitable for live use on
the web. Reasoners such as HermiT[
There are two major barriers to achieving this. The most serious barrier is
the ability to query across an ontology or combined ontology and knowledgeBase
with DL expressiveness. Zhou and colleagues have recently published impressive
results for fast query answering by combining triple store based RL reasoning
with a HermiT DL reasoner [
A more clearly surmountable barrier is the lack of tooling support for some of the axiomatisation required in the design patterns we propose. In particular, adding GCIs to record spatial disjointness is currently very tedious to do by hand in Protege 5. This may be accomplished by scripting, but in order for the approach to be accessible for any ontology builder this would ideally be achieved via a plugin for a popular editor such as Protege. By analogy with support for the addition of class disjointness axioms in Protege, this could work by allowing users to navigate down a partonomy tree, adding disjointness axioms to whole sets of sibling terms at once. 5
For details of construction and maintenance of the Drosophila anatomy ontology
please see Costa et al., 2013 [
VFB is an open source project. All code is available from https://github. com/VirtualFlyBrain OWL individuals les used on VFB are available from https://github.com/VirtualFlyBrain/VFB_owl/tree/master/src/owl. A test ontology illustrating implementation of the DL patterns for negative queries can be found at: http://purl.obolibrary.org/obo/fbbt/vfb/demo/owled2014_ demo.owl.
All queries for anatomical classes or individuals on VFB are live DL queries via the elk OWL reasoner. All queries of annotation begin with a DL query for subclasses, parts and overlapping cells. The resulting list is then used to query annotations store in the FlyBase Postgresql database. More details of the overall architecture of the project cen be found at https://github.com/ VirtualFlyBrain/VFB#overall-architecture-of-project 5.2
Details of individuals are maintained in a SQL database (https://github.com/ VirtualFlyBrain/VFB_owl/wiki/Individuals-DB) and programmatically converted to OWL using the OWL-API (https://github.com/VirtualFlyBrain/ VFB_owl/). A standard DB representation of OWL ontologies/individuals would be preferable to our bespoke solution, which limits axiom expressiveness in order to keep the DB structure simple. We are currently unaware of any viable, non-proprietary alternatives.
The OWL design patterns and queries presented in this paper were designed and tested by DOS He also designed the database representation of OWL individuals and wrote the code the translates this representation into OWL. DOS, MC, and RC all contributed to the representation of neuroanatomy in the DAO. MC was also responsible for annotation of individual neurons in the VFB knowledgeBase.
We thank all those who have contributed to the development of VFB who are not authors on this paper: FlyBase, Michael Ashburner, J. Douglas Armstrong, Nestor Milyaev, Simon Reeve and Gregory S.X.E Je eris.
Funding This work was largely supported by:`Standardising the representation of Drosophila anatomy and development for databases' BBBSRC:BB/G02233X/1 awarded 2009 to J.Douglas Armstrong, Michael Ashburner, Cahir O'Kane and David Osumi-Sutherland; An Isaac Newton Trust grant to Cahir O'Kane to fund the work of Marta Costa, awarded 2012:`Neuroinformatic identi cation of new types of neuron in the Drosophila brain.' VFB is currently supported by a Wellcome Trust grant to Cahir O'Kane, J. Douglas Armstrong, Gregory S.X.E Je eris, Helen Parkinson and David Osumi-Sutherland: `Virtual Fly Brain: a global informatics hub for Drosophila neurobiology' WT105023MA.