Expanding the mammalian phenotype ontology to support automated exchange of high throughput mouse phenotyping data generated by large-scale mouse knockout screens

Improving prenatal diagnosis through standards and aggregation

Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype-driven tools.

Hyperbolic hierarchical knowledge graph embeddings for biological entities

Predicting relationships between biological entities can greatly benefit important biomedical problems. Previous studies have attempted to represent biological entities and relationships in Euclidean space using embedding methods, which evaluate their semantic similarity by representing entities as numerical vectors. However, the limitation of these methods is that they cannot prevent the loss of latent hierarchical information when embedding large graph-structured data into Euclidean space, and therefore cannot capture the semantics of entities and relationships accurately. Hyperbolic spaces, such as Poincaré ball, are better suited for hierarchical modeling than Euclidean spaces. This is because hyperbolic spaces exhibit negative curvature, causing distances to grow exponentially as they approach the boundary. In this paper, we propose HEM, a hyperbolic hierarchical knowledge graph embedding model to generate vector representations of bio-entities. By encoding the entities and relations in the hyperbolic space, HEM can capture latent hierarchical information and improve the accuracy of biological entity representation. Notably, HEM can preserve rich information with a low dimension compared with the methods that encode entities in Euclidean space. Furthermore, we explore the performance of HEM in protein-protein interaction prediction and gene-disease association prediction tasks. Experimental results demonstrate the superior performance of HEM over state-of-the-art baselines. The data and code are available at : https://github.com/Nan-ll/HEM.

Rare disease research resources at the Rat Genome Database

Rare diseases individually affect relatively few people, but as a group they impact considerable numbers of people. The Rat Genome Database (https://rgd.mcw.edu) is a knowledgebase that offers resources for rare disease research. This includes disease definitions, genes, quantitative trail loci (QTLs), genetic variants, annotations to published literature, links to external resources, and more. One important resource is identifying relevant cell lines and rat strains that serve as models for disease research. Diseases, genes, and strains have report pages with consolidated data, and links to analysis tools. Utilizing these globally accessible resources for rare disease research, potentiating discovery of mechanisms and new treatments, can point researchers toward solutions to alleviate the suffering of those afflicted with these diseases.

Altered hair root gene expression profiles highlight calcium signaling and lipid metabolism pathways to be associated with curly hair initiation and maintenance in Mangalitza pigs

Hair types have been under strong targeted selection in domestic animals for their impact on skin protection, thermoregulation and exterior morphology, and subsequent economic importance. In pigs, a very special hair phenotype was observed in Mangalitza, who expresses a thick coat of curly bristles and downy hair. Two breed-specific missense variants in TRPM2 and CYP4F3 were suggested to be associated with the Mangalitza pig's hair shape due to their role in hair follicle morphogenesis reported for human and mice. However, the mechanism behind this expression of a curly hair type is still unclear and needs to be explored. In our study, hair shafts were measured and investigated for the curvature of the hair in Mangalitza and crossbreeds in comparison to straight-coated pigs. For molecular studies, hair roots underwent RNA sequencing for a differential gene expression analysis using DESeq2. The output matrix of normalized counts was then used to construct weighted gene co-expression networks. The resulting hair root gene expression profiles highlighted 454 genes to be significantly differentially expressed for initiation of curly hair phenotype in newborn Mangalitza piglets versus post-initiation in later development. Furthermore, 2,554 genes showed a significant differential gene expression in curly hair in comparison to straight hair. Neither TRPM2 nor CYP4F3 were identified as differentially expressed. Incidence of the genes in weighted co-expression networks associated with TRPM2 and CYP4F3, and prominent interactions of subsequent proteins with lipids and calcium-related pathways suggested calcium signaling and/or lipid metabolism as essential players in the induction of the curly hair as well as an ionic calcium-dependency to be a prominent factor for the maintenance of this phenotype. Subsequently, our study highlights the complex interrelations and dependencies of mutant genes TRPM2 and CYP4F3 and associated gene expression patterns, allowing the initiation of curly hair type during the development of a piglet as well as the maintenance in adult individuals.

IAnimal: a cross-species omics knowledgebase for animals

With the exponential growth of multi-omics data, its integration and utilization have brought unprecedented opportunities for the interpretation of gene regulation mechanisms and the comprehensive analyses of biological systems. IAnimal (https://ianimal.pro/), a cross-species, multi-omics knowledgebase, was developed to improve the utilization of massive public data and simplify the integration of multi-omics information to mine the genetic mechanisms of objective traits. Currently, IAnimal provides 61 191 individual omics data of genome (WGS), transcriptome (RNA-Seq), epigenome (ChIP-Seq, ATAC-Seq) and genome annotation information for 21 species, such as mice, pigs, cattle, chickens, and macaques. The scale of its total clean data has reached 846.46 TB. To better understand the biological significance of omics information, a deep learning model for IAnimal was built based on BioBERT and AutoNER to mine 'gene' and 'trait' entities from 2 794 237 abstracts, which has practical significance for comprehending how each omics layer regulates genes to affect traits. By means of user-friendly web interfaces, flexible data application programming interfaces, and abundant functional modules, IAnimal enables users to easily query, mine, and visualize characteristics in various omics, and to infer how genes play biological roles under the influence of various omics layers.

WhichTF is functionally important in your open chromatin data?

We present WhichTF, a computational method to identify functionally important transcription factors (TFs) from chromatin accessibility measurements. To rank TFs, WhichTF applies an ontology-guided functional approach to compute novel enrichment by integrating accessibility measurements, high-confidence pre-computed conservation-aware TF binding sites, and putative gene-regulatory models. Comparison with prior sheer abundance-based methods reveals the unique ability of WhichTF to identify context-specific TFs with functional relevance, including NF-κB family members in lymphocytes and GATA factors in cardiac cells. To distinguish the transcriptional regulatory landscape in closely related samples, we apply differential analysis and demonstrate its utility in lymphocyte, mesoderm developmental, and disease cells. We find suggestive, under-characterized TFs, such as RUNX3 in mesoderm development and GLI1 in systemic lupus erythematosus. We also find TFs known for stress response, suggesting routine experimental caveats that warrant careful consideration. WhichTF yields biological insight into known and novel molecular mechanisms of TF-mediated transcriptional regulation in diverse contexts, including human and mouse cell types, cell fate trajectories, and disease-associated cells.

Transcription factors (TFs), a class of DNA binding proteins, regulate tissue- and cell-type-specific expression of genes. Identifying the critical TFs in a given cellular context leads to investigating molecular regulatory mechanisms in development, differentiation, and disease. Because there are more than 1,500 human TFs, experimental measurements of genome-wide occupancy across all TFs have been challenging. While computational approaches play pivotal roles, most existing methods rely on statistical enrichment, focusing either on sequence motif similarity recognized by TFs or the similarity of the genomic region of interest with the previously characterized TF occupancy profile. Here we propose WhichTF as an alternative, incorporating curated biomedical knowledge from ontology and integrating it with the high-confidence prediction of conserved TF binding sites in user-provided genomic regions of interest. We develop a new WhichTF score to rank TFs and demonstrate its applicability across human and mouse cell types, cellular differentiation trajectories, and disease-associated cells.

The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development

Background

Ontologies 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.

Results

Here 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.

Conclusions

The 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04636-8.

The Human Disease Ontology 2022 update

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO’s 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO’s complex disease classification. The DO’s updated website provides multifaceted etiology searching, enhanced documentation and educational resources.

Dimensional reduction of phenotypes from 53 000 mouse models reveals a diverse landscape of gene function

Animal models have long been used to study gene function and the impact of genetic mutations on phenotype. Through the research efforts of thousands of research groups, systematic curation of published literature and high-throughput phenotyping screens, the collective body of knowledge for the mouse now covers the majority of protein-coding genes. We here collected data for over 53 000 mouse models with mutations in over 15 000 genomic markers and characterized by more than 254 000 annotations using more than 9000 distinct ontology terms. We investigated dimensional reduction and embedding techniques as means to facilitate access to this diverse and high-dimensional information. Our analyses provide the first visual maps of the landscape of mouse phenotypic diversity. We also summarize some of the difficulties in producing and interpreting embeddings of sparse phenotypic data. In particular, we show that data preprocessing, filtering and encoding have as much impact on the final embeddings as the process of dimensional reduction. Nonetheless, techniques developed in the context of dimensional reduction create opportunities for explorative analysis of this large pool of public data, including for searching for mouse models suited to study human diseases.

AVAILABILITY AND IMPLEMENTATION

Source code for analysis scripts is available on GitHub at https://github.com/tkonopka/mouse-embeddings. The data underlying this article are available in Zenodo at https://doi.org/10.5281/zenodo.4916171.

CONTACT

t.konopka@qmul.ac.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

A representation model for biological entities by fusing structured axioms with unstructured texts

MOTIVATION

Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge.

RESULTS

We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein-protein interactions and gene-disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation.

AVAILABILITY AND IMPLEMENTATION

The source code is available at the gitlab repository https://gitlab.com/BioAI/erbk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mouse Genome Database (MGD): Knowledgebase for mouse-human comparative biology

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism knowledgebase for the laboratory mouse, a widely used animal model for comparative studies of the genetic and genomic basis for human health and disease. MGD is the authoritative source for biological reference data related to mouse genes, gene functions, phenotypes and mouse models of human disease. MGD is the primary source for official gene, allele, and mouse strain nomenclature based on the guidelines set by the International Committee on Standardized Nomenclature for Mice. MGD's biocuration scientists curate information from the biomedical literature and from large and small datasets contributed directly by investigators. In this report we describe significant enhancements to the content and interfaces at MGD, including (i) improvements in the Multi Genome Viewer for exploring the genomes of multiple mouse strains, (ii) inclusion of many more mouse strains and new mouse strain pages with extended query options and (iii) integration of extensive data about mouse strain variants. We also describe improvements to the efficiency of literature curation processes and the implementation of an information portal focused on mouse models and genes for the study of COVID-19.

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier

Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases.

Gene–phenotype associations can help to understand the underlying mechanisms of many genetic diseases. However, experimental identification, often involving animal models, is time consuming and expensive. Computational methods that predict gene–phenotype associations can be used instead. We developed DeepPheno, a novel approach for predicting the phenotypes resulting from a loss of function of a single gene. We use gene functions and gene expression as information to prediction phenotypes. Our method uses a neural network classifier that is able to account for hierarchical dependencies between phenotypes. We extensively evaluate our method and compare it with related approaches, and we show that DeepPheno results in better performance in several evaluations. Furthermore, we found that many of the new predictions made by our method have been added to phenotype association databases released one year later. Overall, DeepPheno simulates some aspects of human physiology and how molecular and physiological alterations lead to abnormal phenotypes.

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

Motivation

Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns and encode domain background knowledge. The domain knowledge of biomedical ontologies may have also the potential to provide background knowledge for machine learning and predictive modelling.

Results

We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein–protein interactions and gene–disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies.

Availability and implementation

https://github.com/bio-ontology-research-group/tsoe.

Supplementary information

Supplementary data are available at Bioinformatics online.

DDIEM: drug database for inborn errors of metabolism

BACKGROUND

Inborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of the changing landscape of therapeutic approaches is helpful in assessing strategic patterns in the approach to therapy, but the information is scattered throughout the literature and public data resources.

RESULTS

We gathered data on therapeutic strategies for 300 diseases into the Drug Database for Inborn Errors of Metabolism (DDIEM). Therapeutic approaches, including both successful and ineffective treatments, were manually classified by their mechanisms of action using a new ontology.

CONCLUSIONS

We present a manually curated, ontologically formalized knowledgebase of drugs, therapeutic procedures, and mitigated phenotypes. DDIEM is freely available through a web interface and for download at http://ddiem.phenomebrowser.net.

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

New models for human disease from the International Mouse Phenotyping Consortium

The International Mouse Phenotyping Consortium (IMPC) continues to expand the catalogue of mammalian gene function by conducting genome and phenome-wide phenotyping on knockout mouse lines. The extensive and standardized phenotype screens allow the identification of new potential models for human disease through cross-species comparison by computing the similarity between the phenotypes observed in the mutant mice and the human phenotypes associated to their orthologous loci in Mendelian disease. Here, we present an update on the novel disease models available from the most recent data release (DR10.0), with 5861 mouse genes fully or partially phenotyped and a total number of 69,982 phenotype calls reported. With approximately one-third of human Mendelian genes with orthologous null mouse phenotypes described, the range of available models relevant for human diseases keeps increasing. Among the breadth of new data, we identify previously uncharacterized disease genes in the mouse and additional phenotypes for genes with existing mutant lines mimicking the associated disorder. The automated and unbiased discovery of relevant models for all types of rare diseases implemented by the IMPC constitutes a powerful tool for human genetics and precision medicine.

Xenbase: Facilitating the Use of Xenopus to Model Human Disease

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.

The anatomy of phenotype ontologies: principles, properties and applications

The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

Ontology-based validation and identification of regulatory phenotypes

Motivation

Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations.

Results

We developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. We also apply our method to the rule-based prediction of regulatory phenotypes from functions and demonstrate that we can predict these phenotypes with Fmax of up to 0.647.

Availability and implementation

https://github.com/bio-ontology-research-group/phenogocon.

The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation

The International Mouse Phenotyping Consortium (IMPC) is building a catalogue of mammalian gene function by producing and phenotyping a knockout mouse line for every protein-coding gene. To date, the IMPC has generated and characterised 5186 mutant lines. One-third of the lines have been found to be non-viable and over 300 new mouse models of human disease have been identified thus far. While current bioinformatics efforts are focused on translating results to better understand human disease processes, IMPC data also aids understanding genetic function and processes in other species. Here we show, using gorilla genomic data, how genes essential to development in mice can be used to help assess the potentially deleterious impact of gene variants in other species. This type of analyses could be used to select optimal breeders in endangered species to maintain or increase fitness and avoid variants associated to impaired-health phenotypes or loss-of-function mutations in genes of critical importance. We also show, using selected examples from various mammal species, how IMPC data can aid in the identification of candidate genes for studying a condition of interest, deliver information about the mechanisms involved, or support predictions for the function of genes that may play a role in adaptation. With genotyping costs decreasing and the continued improvements of bioinformatics tools, the analyses we demonstrate can be routinely applied.

Mouse Genome Informatics (MGI) Is the International Resource for Information on the Laboratory Mouse

Mouse Genome Informatics (MGI, http://www.informatics.jax.org/ ) web resources provide free access to meticulously curated information about the laboratory mouse. MGI's primary goal is to help researchers investigate the genetic foundations of human diseases by translating information from mouse phenotypes and disease models studies to human systems. MGI provides comprehensive phenotypes for over 50,000 mutant alleles in mice and provides experimental model descriptions for over 1500 human diseases. Curated data from scientific publications are integrated with those from high-throughput phenotyping and gene expression centers. Data are standardized using defined, hierarchical vocabularies such as the Mammalian Phenotype (MP) Ontology, Mouse Developmental Anatomy and the Gene Ontologies (GO). This chapter introduces you to Gene and Allele Detail pages and provides step-by-step instructions for simple searches and those that take advantage of the breadth of MGI data integration.

Exploring human disease using the Rat Genome Database

Rattus norvegicus, the laboratory rat, has been a crucial model for studies of the environmental and genetic factors associated with human diseases for over 150 years. It is the primary model organism for toxicology and pharmacology studies, and has features that make it the model of choice in many complex-disease studies. Since 1999, the Rat Genome Database (RGD; http://rgd.mcw.edu) has been the premier resource for genomic, genetic, phenotype and strain data for the laboratory rat. The primary role of RGD is to curate rat data and validate orthologous relationships with human and mouse genes, and make these data available for incorporation into other major databases such as NCBI, Ensembl and UniProt. RGD also provides official nomenclature for rat genes, quantitative trait loci, strains and genetic markers, as well as unique identifiers. The RGD team adds enormous value to these basic data elements through functional and disease annotations, the analysis and visual presentation of pathways, and the integration of phenotype measurement data for strains used as disease models. Because much of the rat research community focuses on understanding human diseases, RGD provides a number of datasets and software tools that allow users to easily explore and make disease-related connections among these datasets. RGD also provides comprehensive human and mouse data for comparative purposes, illustrating the value of the rat in translational research. This article introduces RGD and its suite of tools and datasets to researchers - within and beyond the rat community - who are particularly interested in leveraging rat-based insights to understand human diseases.

Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium

Although next-generation sequencing has revolutionized the ability to associate variants with human diseases, diagnostic rates and development of new therapies are still limited by a lack of knowledge of the functions and pathobiological mechanisms of most genes. To address this challenge, the International Mouse Phenotyping Consortium is creating a genome- and phenome-wide catalog of gene function by characterizing new knockout-mouse strains across diverse biological systems through a broad set of standardized phenotyping tests. All mice will be readily available to the biomedical community. Analyzing the first 3,328 genes identified models for 360 diseases, including the first models, to our knowledge, for type C Bernard-Soulier, Bardet-Biedl-5 and Gordon Holmes syndromes. 90% of our phenotype annotations were novel, providing functional evidence for 1,092 genes and candidates in genetically uncharacterized diseases including arrhythmogenic right ventricular dysplasia 3. Finally, we describe our role in variant functional validation with The 100,000 Genomes Project and others.

Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse

The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided.

Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research

The Mouse Genome Informatics (MGI), resource ( www.informatics.jax.org ) has existed for over 25 years, and over this time its data content, informatics infrastructure, and user interfaces and tools have undergone dramatic changes (Eppig et al., Mamm Genome 26:272-284, 2015). Change has been driven by scientific methodological advances, rapid improvements in computational software, growth in computer hardware capacity, and the ongoing collaborative nature of the mouse genomics community in building resources and sharing data. Here we present an overview of the current data content of MGI, describe its general organization, and provide examples using simple and complex searches, and tools for mining and retrieving sets of data.

Best Practices in Manual Annotation with the Gene Ontology

The Gene Ontology (GO) is a framework designed to represent biological knowledge about gene products' biological roles and the cellular location in which they act. Biocuration is a complex process: the body of scientific literature is large and selection of appropriate GO terms can be challenging. Both these issues are compounded by the fact that our understanding of biology is still incomplete; hence it is important to appreciate that GO is inherently an evolving model. In this chapter, we describe how biocurators create GO annotations from experimental findings from research articles. We describe the current best practices for high-quality literature curation and how GO curators succeed in modeling biology using a relatively simple framework. We also highlight a number of difficulties when translating experimental assays into GO annotations.

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

High-throughput discovery of novel developmental phenotypes

Approximately one-third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.

Special issue on bio-ontologies and phenotypes

The bio-ontologies and phenotypes special issue includes eight papers selected from the 11 papers presented at the Bio-Ontologies SIG (Special Interest Group) and the Phenotype Day at ISMB (Intelligent Systems for Molecular Biology) conference in Boston in 2014. The selected papers span a wide range of topics including the automated re-use and update of ontologies, quality assessment of ontological resources, and the systematic description of phenotype variation, driven by manual, semi- and fully automatic means.

Use of model organism and disease databases to support matchmaking for human disease gene discovery

The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.

Allele, phenotype and disease data at Mouse Genome Informatics: improving access and analysis

A core part of the Mouse Genome Informatics (MGI) resource is the collection of mouse mutations and the annotation phenotypes and diseases displayed by mice carrying these mutations. These data are integrated with the rest of data in MGI and exported to numerous other resources. The use of mouse phenotype data to drive translational research into human disease has expanded rapidly with the improvements in sequencing technology. MGI has implemented many improvements in allele and phenotype data annotation, search, and display to facilitate access to these data through multiple avenues. For example, the description of alleles has been modified to include more detailed categories of allele attributes. This allows improved discrimination between mutation types. Further, connections have been created between mutations involving multiple genes and each of the genes overlapping the mutation. This allows users to readily find all mutations affecting a gene and see all genes affected by a mutation. In a similar manner, the genes expressed by transgenic or knock-in alleles are now connected to these alleles. The advanced search forms and public reports have been updated to take advantage of these improvements. These search forms and reports are used by an expanding number of researchers to identify novel human disease genes and mouse models of human disease.