BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.

SUMMARY: Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time. AVAILABILITY AND IMPLEMENTATION: More information can be found at https://explorer.biothings.io and code is available at https://github.com/biothings/biothings_explorer.

Ecological network structure in response to community assembly processes over evolutionary time.

The dynamic structure of ecological communities results from interactions among taxa that change with shifts in species composition in space and time. However, our ability to study the interplay of ecological and evolutionary processes on community assembly remains relatively unexplored due to the difficulty of measuring community structure over long temporal scales. Here, we made use of a geological chronosequence across the Hawaiian Islands, representing 50 years to 4.15 million years of ecosystem development, to sample 11 communities of arthropods and their associated plant taxa using semiquantitative DNA metabarcoding. We then examined how ecological communities changed with community age by calculating quantitative network statistics for bipartite networks of arthropod-plant associations. The average number of interactions per species (linkage density), ratio of plant to arthropod species (vulnerability) and uniformity of energy flow (interaction evenness) increased significantly in concert with community age. The index of specialization H 2 ' has a curvilinear relationship with community age. Our analyses suggest that younger communities are characterized by fewer but stronger interactions, while biotic associations become more even and diverse as communities mature. These shifts in structure became especially prominent on East Maui (~0.5 million years old) and older volcanos, after enough time had elapsed for adaptation and specialization to act on populations in situ. Such natural progression of specialization during community assembly is probably impeded by the rapid infiltration of non-native species, with special risk to younger or more recently disturbed communities that are composed of fewer specialized relationships.

Richness and resilience in the Pacific: DNA metabarcoding enables parallelized evaluation of biogeographic patterns.

Islands make up a large proportion of Earth's biodiversity, yet are also some of the most sensitive systems to environmental perturbation. Biogeographic theory predicts that geologic age, area, and isolation typically drive islands' diversity patterns, and thus potentially impact non-native spread and community homogenization across island systems. One limitation in testing such predictions has been the difficulty of performing comprehensive inventories of island biotas and distinguishing native from introduced taxa. Here, we use DNA metabarcoding and statistical modelling as a high throughput method to survey community-wide arthropod richness, the proportion of native and non-native species, and the incursion of non-natives into primary habitats on three archipelagos in the Pacific - the Ryukyus, the Marianas and Hawaii - which vary in age, isolation and area. Diversity patterns largely match expectations based on island biogeography theory, with the oldest and most geographically connected archipelago, the Ryukyus, showing the highest taxonomic richness and lowest proportion of introduced species. Moreover, we find evidence that forest habitats are more resilient to incursions of non-natives in the Ryukyus than in the less taxonomically rich archipelagos. Surprisingly, we do not find evidence for biotic homogenization across these three archipelagos: the assemblage of non-native species on each island is highly distinct. Our study demonstrates the potential of DNA metabarcoding to facilitate rapid estimation of biogeographic patterns, the spread of non-native species, and the resilience of ecosystems.

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.

Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThing Explorer is distributed as a lightweight application that dynamically retrieves information at query time. More information can be found at https://explorer.biothings.io, and code is available at https://github.com/biothings/biothings_explorer.