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Hydraulic fracturing operations are generally associated with induced seismic events with magnitudes that are typically lower than those of tectonic-based earthquakes. Although fluid migration and the resulting pore-pressure increase appear to be the primary driving mechanisms, one must also understand fault activation and the secondary interevent triggering process(es) leading to the generation of aftershock sequences. This requires distinguishing between seismic events that are a direct consequence of fluid injections and those that predominantly arise because of secondary mechanisms such as static or dynamic stress triggering caused by preceding earthquakes. Focusing on a case study in the Montney area in western Alberta, Canada, where both surface array and borehole geophone data are available, we (1) establish the importance of secondary mechanisms and aftershock sequences in this area and (2) show that the statistical properties of the aftershock sequences can allow one to establish whether significant fault activation is at play and whether the activated fault zone was critically prestressed. Specifically, we find evidence for aftershock sequences using different statistical methodologies, although the presence of aftershocks in the studied hydraulic-fracturing-induced context is significantly less dominant compared with tectonic seismicity. The identified aftershock sequences are characterized by long-range spatial aftershock zones, setting them apart from other cases in fluid-driven settings but comparable to tectonic aftershock sequences and indicative of fault activation. Despite this observation, we find overall a strong dominance of smaller triggers for aftershock sequences and an aftershock production associated with large-magnitude events that is significantly lower than what has been observed in tectonic settings. This suggests that the activated fault zone as a whole was not critically prestressed, and the associated initial seismic hazard was significantly lower compared with tectonic cases.

Funding Information: This research was funded by the European Union Horizon 2020 research and innovation program under grant agreement no. 857470 and from the European Regional Development Fund via Foundation for Polish Science International Research Agenda PLUS program grant no. MAB PLUS/2018/8 . We wish to acknowledge fruitful discussions with Daniel Cieslinski. | openaire: EC/H2020/857470/EU//NOMATEN Nanoscale hardness in polycrystalline metals is strongly dependent on microstructural features that are believed to be influenced from polycrystallinity — namely, grain orientations and neighboring grain properties. We train a graph neural networks (GNN) model, with grain centers as graph nodes, to assess the predictability of micromechanical responses of nano-indented 310S steel surfaces, based on surface polycrystallinity, captured by electron backscatter diffraction maps. The grain size distribution ranges between 1–100 μm, with mean size at 18μm. The GNN model is trained on nanomechanical load-displacement curves to make predictions of nano-hardness, with sole input being the grain locations and orientations. We explore model performance and its dependence on various structural/topological grain-level descriptors (e.g. grain size and number of neighbors). Analogous GNN-based frameworks may be utilized for quick, inexpensive hardness estimates, for guidance to detailed nanoindentation experiments, akin to cartography tool developments in the world exploration era.

Xenbase (https://www.xenbase.org/), the Xenopus model organism knowledgebase, is a web-accessible resource that integrates the diverse genomic and biological data from research on the laboratory frogs Xenopus laevis and Xenopus tropicalis. The goal of Xenbase is to accelerate discovery and empower Xenopus research, to enhance the impact of Xenopus research data, and to facilitate the dissemination of these data. Xenbase also enhances the value of Xenopus data through high-quality curation, data integration, providing bioinformatics tools optimized for Xenopus experiments, and linking Xenopus data to human data, and other model organisms. Xenbase also plays an indispensable role in making Xenopus data interoperable and accessible to the broader biomedical community in accordance with FAIR principles. Xenbase provides annotated data updates to organizations such as NCBI, UniProtKB, Ensembl, the Gene Ontology consortium, and most recently, the Alliance of Genomic Resources, a common clearing house for data from humans and model organisms. This article provides a brief overview of key and recently added features of Xenbase. New features include processing of Xenopus high-throughput sequencing data from the NCBI Gene Expression Omnibus; curation of anatomical, physiological, and expression phenotypes with the newly created Xenopus Phenotype Ontology; Xenopus Gene Ontology annotations; new anatomical drawings of the Normal Table of Xenopus development; and integration of the latest Xenopus laevis v10.1 genome annotations. Finally, we highlight areas for future development at Xenbase as we continue to support the Xenopus research community.

Funding Information: This research was funded by the European Union Horizon 2020 research and innovation program under Grant Agreement No. 857470 and from the European Regional Development Fund via Foundation for Polish Science International Research Agenda PLUS program Grant No. MAB PLUS/2018/8. | openaire: EC/H2020/857470/EU//NOMATEN Equiatomic NiCoCr solid solutions have been shown by recent experiments and atomistic simulations to display exceptional mechanical properties that have been suggested to be linked to nanostructural short-range order (SRO) features that may arise from thermal treatments, such as annealing or/and aging. Here we use hybrid Monte Carlo-molecular dynamics simulations to gain further insights of thermal effects on the SRO formation as well as the edge dislocation plasticity mechanisms of equiatomic NiCoCr face-centered cubic solid solution. For that purpose, we utilize two well-known NiCoCr interatomic potentials, one of which displays well-documented SRO, believed to be linked to experimental evidence and labeled as the Li-Sheng-Ma potential, while the other (Farkas-Caro) does not. We use these two potentials to discern short-range ordering (from inherent randomness in random solid solutions) and understand how SROs influence dislocation depinning dynamics in various thermal annealing scenarios. In this context, we used robust, scale-dependent metrics to infer a characteristic SRO size in the Li-Sheng-Ma case by probing local concentration fluctuations which otherwise indicate uncorrelated patterns in the Farkas-Caro case in a close agreement with random alloys. Our Voronoi-based analysis shows meaningful variations of local misfit properties owing to the presence of SROs. Using relevant order parameters, we also report on the drastic increase of chemical ordering within the stacking fault region. More importantly, we find that the Li-Sheng-Ma potential leads to excellent edge dislocation depinning strength with low stacking fault width. Our findings indicate an enhanced roughening mechanism due to the SROs-misfit synergy that leads to significant improvements in dislocation glide resistance. We argue that the improvements in alloy strength have their atomistic origins in the interplay between nanoscopic SROs and atomic-level misfit properties.

The frictional instability associated with earthquake initiation and earthquake dynamics is believed to be mainly controlled by the dynamics of fragmented rocks within the fault gauge. Principal features of the emerging seismicity (e.g., intermittent dynamics and broad time and/or energy scales) have been replicated by simple experimental setups, which involve a slowly driven slider on top of granular matter, for example. Yet these setups are often physically limited and might not allow one to determine the underlying nature of specific features and, hence, the universality and generality of the experimental observations. Here, we address this challenge by a numerical study of a spring-slider experiment based on two-dimensional discrete element method simulations, which allows us to control the properties of the granular matter and of the surface of the slider, for example. Upon quasistatic loading, stick-slip-type behavior emerges which is contrasted by a stable sliding regime at finite driving rates, in agreement with experimental observations. Across large parameter ranges for damping, interparticle friction, particle polydispersity, etc., the earthquake-like dynamics associated with the former regime results in several robust scale-free statistical features also observed in experiments. At first sight, these closely resemble the main empirical relations of tectonic seismicity at geological scales. This includes the Gutenberg-Richter distribution of event sizes, the Omori-Utsu-type decay of aftershock rates, as well as the aftershock productivity relation and broad recurrence time distributions. Yet, we show that the correlations associated with tectonic aftershocks are absent such that the origin of the Omori-Utsu relation, the aftershock productivity relation, and Båth's relation in the simulations is fundamentally different from the case of tectonic seismicity. This, we believe, is mainly due to a lack of macroscale relaxation processes that are closely tied to the generation of real aftershocks. We argue that the same is true for previous laboratory experiments.

BackgroundOntologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease.ResultsHere we present the Xenopus Phenotype Ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated.ConclusionsThe XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype-phenotype data that can be directly related to other uPheno compliant resources.

Rhythms of various periodicities drive cyclical processes in organisms ranging from single cells to the largest mammals on earth, and on scales from cellular physiology to global migrations. The molecular mechanisms that generate circadian behaviours in model organisms have been well studied, but longer phase cycles and interactions between cycles with different periodicities remain poorly understood. Broadcast spawning corals are one of the best examples of an organism integrating inputs from multiple environmental parameters, including seasonal temperature, the lunar phase and hour of the day, to calibrate their annual reproductive event. We present a deep RNA‐sequencing experiment utilizing multiple analyses to differentiate transcriptomic responses modulated by the interactions between the three aforementioned environmental parameters. Acropora millepora was sampled over multiple 24‐hr periods throughout a full lunar month and at two seasonal temperatures. Temperature, lunar and diurnal cycles produce distinct transcriptomic responses, with interactions between all three variables identifying a core set of genes. These core genes include mef2, a developmental master regulator, and two heterogeneous nuclear ribonucleoproteins, one of which is known to post‐transcriptionally interact with mef2 and with biological clock‐regulating mRNAs. Interactions between diurnal and temperature differences impacted a range of core processes ranging from biological clocks to stress responses. Genes involved with developmental processes and transcriptional regulation were impacted by the lunar phase and seasonal temperature differences. Lastly, there was a diurnal and lunar phase interaction in which genes involved with RNA‐processing and translational regulation were differentially regulated. These data illustrate the extraordinary levels of transcriptional variation across time in a simple radial cnidarian in response to the environment under normal conditions.

A keyword-based search of comprehensive databases such as PubMed may return irrelevant papers, especially if the keywords are used in multiple fields of study. In such cases, domain experts (curators) need to verify the results and remove the irrelevant articles. Automating this filtering process will save time, but it has to be done well enough to ensure few relevant papers are rejected and few irrelevant papers are accepted. A good solution would be fast, work with the limited amount of data freely available (full paper body may be missing), handle ambiguous keywords and be as domain-neutral as possible. In this paper, we evaluate a number of classification algorithms for identifying a domain-specific set of papers about echinoderm species and show that the resulting tool satisfies most of the abovementioned requirements. Echinoderms consist of a number of very different organisms, including brittle stars, sea stars (starfish), sea urchins and sea cucumbers. While their taxonomic identifiers are specific, the common names are used in many other contexts, creating ambiguity and making a keyword search prone to error. We try classifiers using Linear, Naïve Bayes, Nearest Neighbor, Tree, SVM, Bagging, AdaBoost and Neural Network learning models and compare their performance. We show how effective the resulting classifiers are in filtering irrelevant articles returned from PubMed. The methodology used is more dependent on the good selection of training data and is a practical solution that can be applied to other fields of study facing similar challenges. Database URL: The code and date reported in this paper are freely available at http://xenbaseturbofrog.org/pub/Text-Topic-Classifier/

Echinobase (https://echinobase.org) is a central online platform that generates, manages and hosts genomic data relevant to echinoderm research. While the resource primarily serves the echinoderm research community, the recent release of an excellent quality genome for the frequently studied purple sea urchin (Strongylocentrotus purpuratus genome, v5.0) has provided an opportunity to adapt to the needs of a broader research community across other model systems. To this end, establishing pipelines to identify orthologous genes between echinoderms and other species has become a priority in many contexts including nomenclature, linking to data in other model organisms, and in internal functionality where data gathered in one hosted species can be associated with genes in other hosted echinoderms. This paper describes the orthology pipelines currently employed by Echinobase and how orthology data are processed to yield 1:1 ortholog mappings between a variety of echinoderms and other model taxa. We also describe functions of interest that have recently been included on the resource, including an updated developmental time course for S.purpuratus, and additional tracks for genome browsing. These data enhancements will increase the accessibility of the resource to non-echinoderm researchers and simultaneously expand the data quality and quantity available to core Echinobase users. Database URL: https://echinobase.org

Seismic hazard due to fluid invasion in hydraulic fracturing, wastewater disposal, and enhanced geothermal systems have become a concern for industry and nearby residents. Some of the challenges as...

Background and Aims Vascular access dysfunction is one of the leading causes of morbidity and a major contributor to healthcare costs in hemodialysis (HD) patients. Inexpensive, non-invasive tools for routine assessment of vascular access function are needed. Hemodynamically relevant stenoses in arteriovenous fistulas (AVF) lead to a reduction in access flow rate (Qa) and changes in blood flow patterns in the AVF that may be picked up by palpation and auscultation. We hypothesized that these changes in blood flow patterns can not only be felt and heard but also seen, i.e., that they may be detectable in video recordings done with commercially available smartphones after digital motion augmentation. Methods We studied HD patients with AVF dysfunction requiring balloon angioplasty and/or stenting. One-minute video recordings of the skin above the AVF and Qa measurements were conducted before and after the endovascular intervention. Videos were recorded with an iPhone 6S (Apple Inc., Cupertino, CA, USA). Qa was measured by HVT100 endovascular flowmeter (Transonic Systems Inc., Ithaca, NY, USA). Significant access stenosis was defined as a >50% reduction of luminal diameter. Degree of stenosis was assessed by angiography. Frame-to-frame pixel changes in video images were amplified using “Eulerian Video Magnification” (Massachusetts Institute of Technology, MA, USA; http://people.csail.mit.edu/mrub/evm/#code). The time-domain data were then transformed into the frequency-domain signals. Fifty random 10-second segments were sampled per one-minute video, and the frequency with the lowest magnitude (Fmin) was determined in each sample (example shown in Fig. 1). The average Fmin was then assessed for its association with the degree of AVF stenosis. Results Ninety subjects were studied (Table 1). AVF interventions were successful in all patients. Post-intervention Qa (1638 ± 714 ml/min) was on average 1.23-fold higher than pre-intervention Qa (1373 ± 684 ml/min; P Conclusion Simple smartphone video recordings of AVF appear to contain frequency-domain information that correlates with hemodynamic changes caused by AVF stenoses. While the Fmin metric employed in our analysis is not ideal, these results should encourage the quest for other parameters that exhibit higher correlations with vascular access dysfunction. If successful, this would allow commercially-available smartphones to be used as ubiquitous tools for quick, non-invasive, ambulatory surveillance of AVF function, thereby allowing timely referrals and avoidance of emergency interventions.

Seismic hazard due to fluid invasion in hydraulic fracturing, wastewater disposal, and enhanced geothermal systems has become a concern for industry and nearby residents. One of the challenges associated with this seismic hazard is the estimation of the spatial effects of these industry operations. Based on a large set of real-world fluid-induced seismicity catalogs, it was recently found that the spatial decay of seismic activity with distance from injection wells exhibits two typical behaviors: short-range decay and long-range decay. The distinction between the two groups can be captured by the exponent in the seismicity density but the underlying origin remains unknown. Here, we introduce a novel conceptual model that not only can capture the observed frequency magnitude distribution of fluid-induced seismic events but also explains different spatial decay exponents observed. In particular, previous models of fluid-induced seismicity have assumed that the permeability and porosity field is either uniform or random and spatially uncorrelated. However, power-law scaling in the spatial frequency power spectrum of well-logs, S(k)∝1/k^β, has been observed for many different physical properties of rocks such as sonic velocity, porosity, and log(permeability). Our model takes advantage of this by introducing a spatially correlated field for porosity and permeability. Our analysis shows that increasing β can decrease the spatial decay exponent, leading to more seismic activity at larger distances from the injection site. In particular, our model explains the two different types of behavior in the spatial distribution of fluid-induced microseismic events as a consequence of different correlations in permeability.

Aftershock cascades and aftershock zones play an important role in forecasting seismic activity in both natural and human-made situations. While their behavior including the spatial aftershock zone scaling has been the focus of many studies in tectonic settings finding, for example, long-range earthquake-earthquake triggering in the near-field, this is not the case in situations where the seismic activity is primarily driven by fluids and the diffusion of excessive pore pressure. Here, we probe three different seismic settings that are believed to be influenced by fluid diffusion. The natural swarm in i) the Long Valley Caldera and the suspected swarms in ii) the Yuha Desert, both located in California, and associated earthquake-earthquake triggering behavior are compared against induced seismicity related to large scale wastewater disposal in iii) Oklahoma and southern Kansas. All settings exhibit a significant amount of event-event triggering highlighting the importance of secondary processes for the overall seismicity. We find an almost identical temporal event-event triggering behavior including the Omori-Utsu relation and the associated productivity relation. In terms of the spatial triggering density, both cases i) and iii) show a rapid decay beyond their rupture length. This proves that narrow spatial “aftershock” zones are not specific to induced seismicity but also occur in natural settings. Typical of most tectonic settings, a relatively long-range behavior is observed in case ii) suggesting that fluid migration might not be the dominant driving mechanism of the seismic activity and/or that the underlying structure of the fault network may control the secondary earthquake-earthquake triggering and its spatial evolution.

Xenbase (www.xenbase.org) is an online resource for researchers utilizing Xenopus laevis and Xenopus tropicalis, and for biomedical scientists seeking access to data generated with these model systems. Content is aggregated from a variety of external resources and also generated by in-house curation of scientific literature and bioinformatic analyses. Over the past two years many new types of content have been added along with new tools and functionalities to reflect the impact of high-throughput sequencing. These include new genomes for both supported species (each with chromosome scale assemblies), new genome annotations, genome segmentation, dynamic and interactive visualization for RNA-Seq data, updated ChIP-Seq mapping, GO terms, protein interaction data, ORFeome support, and improved connectivity to other biomedical and bioinformatic resources.

Xenbase (www.xenbase.org) is a knowledge base for researchers and biomedical scientists that employ the amphibian Xenopus as a model organism in biomedical research to gain a deeper understanding of developmental and disease processes. Through expert curation and automated data provisioning from various sources Xenbase strives to integrate the body of knowledge on Xenopus genomics and biology together with the visualization of biologically significant interactions. Most current studies utilize next generation sequencing (NGS) but until now the results of different experiments were difficult to compare and not integrated with other Xenbase content. Xenbase has developed a suite of tools, interfaces and data processing pipelines that transforms NCBI Gene Expression Omnibus (GEO) NGS content into deeply integrated gene expression and chromatin data, mapping all aligned reads to the most recent genome builds. This content can be queried and visualized via multiple tools and also provides the basis for future automated ‘gene expression as a phenotype’ and gene regulatory network analyses.

SummaryWe present an unprecedentedly comprehensive characterization of protein dynamics across early development inXenopus laevis, available immediately via a convenient Web portal. This resource allows interrogation of the protein expression data in conjunction with other data modalities such as genome wide mRNA expression. This study provides detailed data for absolute levels of ∼14K uniqueXenopusproteins representing homologues of ∼9K unique human genes – a rich resource for developmental biologists. The purpose of this manuscript is limited to presenting and releasing the data browser.HighlightsRelative protein expression from stage IV oocyte, blastula, gastrula, neurula, and early organogenesisBiological triplicates with confidence intervals on protein expression reflect certainty in dynamic patternsConvenient time-series Web-browser integrated with the multi-media Xenbase portalGene-symbol search and multi-gene protein/mRNA juxtaposition capabilities

At a fundamental level most genes, signaling pathways, biological functions and organ systems are highly conserved between man and all vertebrate species. Leveraging this conservation, researchers are increasingly using the experimental advantages of the amphibian Xenopus to model human disease. The online Xenopus resource, Xenbase, enables human disease modeling by curating the Xenopus literature published in PubMed and integrating these Xenopus data with orthologous human genes, anatomy, and more recently with links to the Online Mendelian Inheritance in Man resource (OMIM) and the Human Disease Ontology (DO). Here we review how Xenbase supports disease modeling and report on a meta-analysis of the published Xenopus research providing an overview of the different types of diseases being modeled in Xenopus and the variety of experimental approaches being used. Text mining of over 50,000 Xenopus research articles imported into Xenbase from PubMed identified approximately 1,000 putative disease- modeling articles. These articles were manually assessed and annotated with disease ontologies, which were then used to classify papers based on disease type. We found that Xenopus is being used to study a diverse array of disease with three main experimental approaches: cell-free egg extracts to study fundamental aspects of cellular and molecular biology, oocytes to study ion transport and channel physiology and embryo experiments focused on congenital diseases. We integrated these data into Xenbase Disease Pages to allow easy navigation to disease information on external databases. Results of this analysis will equip Xenopus researchers with a suite of experimental approaches available to model or dissect a pathological process. Ideally clinicians and basic researchers will use this information to foster collaborations necessary to interrogate the development and treatment of human diseases.

We report on a particle-based numerical study of sheared amorphous solids in the dense slow flow regime. In this framework, deformation and flow are accompanied by critical fluctuation patterns associated with the macroscopic plastic response and single particle kinematics. The former is commonly attributed to the collective slip patterns that relax internal stresses within the bulk material and give rise to an effective mechanical noise governing the latter particle-level process. In this work, the avalanche-type dynamics between plastic events is shown to have a strong relevance on the self-diffusion of tracer particles in the Fickian regime. As a consequence, strong size effects emerge in the effective diffusion coefficient that is rationalized in terms of avalanche size distributions and the relevant temporal occurrence.

Recent experiments (Le Bouil et al., Phys. Rev. Lett., 2014, 112, 246001) have analyzed the statistics of local deformation in a granular solid undergoing plastic deformation. Experiments report strongly anisotropic correlation between events, with a characteristic angle that was interpreted using elasticity theory and the concept of Eshelby transformations with dilation; interestingly, the shear bands that characterize macroscopic failure occur at an angle that is different from the one observed in microscopic correlations. Here, we interpret this behavior using a mesoscale elastoplastic model of solid flow that incorporates a local Mohr-Coulomb failure criterion. This differs from the interpretation of Le Bouil et al., which is based on purely elastic considerations ignoring the potential role of local friction on deformation patterns. We show that the angle observed in the microscopic correlations can be understood by combining the elastic interactions associated with Eshelby transformation with the local failure criterion. At large strains, we also induce permanent shear bands at an angle that is different from the one observed in the correlation pattern. We interpret this angle as the one that leads to the maximal instability of slip lines.

With the advent of whole transcriptome and genome analysis methods, classifying samples containing multiple origins has become a significant task. Nucleotide sequences can be allocated to a genome or transcriptome by aligning sequences to multiple target sequence sets, but this approach requires extensive computational resources and also depends on target sequence sets lacking contaminants, which is often not the case. Here, we demonstrate that raw sequences can be rapidly sorted into groups, in practice corresponding to genera, by exploiting differences in nucleotide GC content. To do so, we introduce GCSpeciesSorter, which uses classification, specifically Support Vector Machines (SVM) and the C4.5 decision tree generator, to differentiate sequences. It also implements a secondary BLAST feature to identify known outliers. In the test case presented, a hermatypic coral holobiont, the cnidarian host includes various endosymbionts. The best characterized and most common of these symbionts are zooxanthellae of the genus Symbiodinium. GCSpeciesSorter separates cnidarian from Symbiodinium sequences with a high degree of accuracy. We show that if the GC contents of the species differ enough, this method can be used to accurately distinguish the sequences of different species when using high-throughput sequencing technologies.

Xenbase is the Xenopus model organism database ( www.xenbase.org ), a web-accessible resource that integrates the diverse genomic and biological data for Xenopus research. It hosts a variety of content including current and archived genomes for both X. laevis and X. tropicalis, bioinformatic tools for comparative genetic analyses including BLAST and GBrowse, annotated Xenopus literature, and catalogs of reagents including antibodies, ORFeome clones, morpholinos, and transgenic lines. Xenbase compiles gene-specific pages which include manually curated gene expression images, functional information including gene ontology (GO), disease associations, and links to other major data sources such as NCBI:Entrez, UniProtKB, and Ensembl. We also maintain the Xenopus Anatomy Ontology (XAO) which describes anatomy throughout embryonic development. This chapter provides a full description of the many features of Xenbase, and offers a guide on how to use various tools to perform a variety of common tasks such as identifying nucleic acid or protein sequences, finding gene expression patterns for specific genes, stages or tissues, identifying literature on a specific gene or tissue, locating useful reagents and downloading our extensive content, including Xenopus gene-Human gene disease mapping files.

Summary Xenbase, the Xenopus model organism database (www.xenbase.org), is a cloud-based, web-accessible resource that integrates the diverse genomic and biological data from Xenopus research. Xenopus frogs are one of the major vertebrate animal models used for biomedical research, and Xenbase is the central repository for the enormous amount of data generated using this model tetrapod. The goal of Xenbase is to accelerate discovery by enabling investigators to make novel connections between molecular pathways in Xenopus and human disease. Our relational database and user-friendly interface make these data easy to query and allows investigators to quickly interrogate and link different data types in ways that would otherwise be difficult, time consuming, or impossible. Xenbase also enhances the value of these data through high-quality gene expression curation and data integration, by providing bioinformatics tools optimized for Xenopus experiments, and by linking Xenopus data to other model organisms and to human data. Xenbase draws in data via pipelines that download data, parse the content, and save them into appropriate files and database tables. Furthermore, Xenbase makes these data accessible to the broader biomedical community by continually providing annotated data updates to organizations such as NCBI, UniProtKB, and Ensembl. Here, we describe our bioinformatics, genome-browsing tools, data acquisition and sharing, our community submitted and literature curation pipelines, text-mining support, gene page features, and the curation of gene nomenclature and gene models. genesis 53:486–497, 2015. © 2015 Wiley Periodicals, Inc.