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

Developmental coordination disorder: What can we learn from RI mice using motor learning tasks and QTL analysis

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder of unknown etiology that affects one in 20 children. There is an indication that DCD has an underlying genetic component due to its high heritability. Therefore, we explored the use of a recombinant inbred family of mice known as the BXD panel to understand the genetic basis of complex traits (i.e., motor learning) through identification of quantitative trait loci (QTLs). The overall aim of this study was to utilize the QTL approach to evaluate the genome-to-phenome correlation in BXD strains of mice in order to better understand the human presentation of DCD. Results of this current study confirm differences in motor learning in selected BXD strains and strains with altered cerebellar volume. Five strains - BXD15, BXD27, BXD28, BXD75, and BXD86 - exhibited the most DCD-like phenotype when compared with other BXD strains of interest. Results indicate that BXD15 and BXD75 struggled primarily with gross motor skills, BXD28 primarily had difficulties with fine motor skills, and BXD27 and BXD86 strains struggled with both fine and gross motor skills. The functional roles of genes within significant QTLs were assessed in relation to DCD-like behavior. Only Rab3a (Ras-related protein Rab-3A) emerged as a high likelihood candidate gene for the horizontal ladder rung task. This gene is associated with brain and skeletal muscle development, but lacked nonsynonymous polymorphisms. This study along with Gill et al. (same issue) is the first studies to specifically examine the genetic linkage of DCD using BXD strains of mice.

Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

rRNA biogenesis regulates mouse 2C-like state by 3D structure reorganization of peri-nucleolar heterochromatin

The nucleolus is the organelle for ribosome biogenesis and sensing various types of stress. However, its role in regulating stem cell fate remains unclear. Here, we present evidence that nucleolar stress induced by interfering rRNA biogenesis can drive the 2-cell stage embryo-like (2C-like) program and induce an expanded 2C-like cell population in mouse embryonic stem (mES) cells. Mechanistically, nucleolar integrity maintains normal liquid-liquid phase separation (LLPS) of the nucleolus and the formation of peri-nucleolar heterochromatin (PNH). Upon defects in rRNA biogenesis, the natural state of nucleolus LLPS is disrupted, causing dissociation of the NCL/TRIM28 complex from PNH and changes in epigenetic state and reorganization of the 3D structure of PNH, which leads to release of Dux, a 2C program transcription factor, from PNH to activate a 2C-like program. Correspondingly, embryos with rRNA biogenesis defect are unable to develop from 2-cell (2C) to 4-cell embryos, with delayed repression of 2C/ERV genes and a transcriptome skewed toward earlier cleavage embryo signatures. Our results highlight that rRNA-mediated nucleolar integrity and 3D structure reshaping of the PNH compartment regulates the fate transition of mES cells to 2C-like cells, and that rRNA biogenesis is a critical regulator during the 2-cell to 4-cell transition of murine pre-implantation embryo development.

Murine allele and transgene symbols: ensuring unique, concise, and informative nomenclature

In addition to naturally occurring sequence variation and spontaneous mutations, a wide array of technologies exist for modifying the mouse genome. Standardized nomenclature, including allele, transgene, and other mutation nomenclature, as well as persistent unique identifiers (PUID) are critical for effective scientific communication, comparison of results, and integration of data into knowledgebases such as Mouse Genome Informatics (MGI), Alliance for Genome Resources, and International Mouse Strain Resource (IMSR). As well as being the authoritative source for mouse gene, allele, and strain nomenclature, MGI integrates published and unpublished genomic, phenotypic, and expression data while linking to other online resources for a complete view of the mouse as a valuable model organism. The International Committee on Standardized Genetic Nomenclature for Mice has developed allele nomenclature rules and guidelines that take into account the number of genes impacted, the method of allele generation, and the nature of the sequence alteration. To capture details that cannot be included in allele symbols, MGI has further developed allele to gene relationships using sequence ontology (SO) definitions for mutations that provide links between alleles and the genes affected. MGI is also using (HGVS) variant nomenclature for variants associated with alleles that will enhance searching for mutations and will improve cross-species comparison. With the ability to assign unique and informative symbols as well as to link alleles with more than one gene, allele and transgene nomenclature rules and guidelines provide an unambiguous way to represent alterations in the mouse genome and facilitate data integration among multiple resources such the Alliance of Genome Resources and International Mouse Strain Resource.

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

Developmental Gene Expression Differences between Humans and Mammalian Models

Identifying the molecular programs underlying human organ development and how they differ from model species is key for understanding human health and disease. Developmental gene expression profiles provide a window into the genes underlying organ development and a direct means to compare them across species. We use a transcriptomic resource covering the development of seven organs to characterize the temporal profiles of human genes associated with distinct disease classes and to determine, for each human gene, the similarity of its spatiotemporal expression with its orthologs in rhesus macaque, mouse, rat, and rabbit. We find clear associations between spatiotemporal profiles and the phenotypic manifestations of diseases. We also find that half of human genes differ from their mouse orthologs in their temporal trajectories in at least one of the organs. These include more than 200 genes associated with brain, heart, and liver disease for which mouse models should undergo extra scrutiny.

Genome-wide transcriptome architecture in a mouse model of Gulf War Illness

Gulf War Illness (GWI) is thought to be a chronic neuroimmune disorder caused by in-theater exposure during the 1990-1991 Gulf War. There is a consensus that the illness is caused by exposure to insecticides and nerve agent toxicants. However, the heterogeneity in both development of disease and clinical outcomes strongly suggests a genetic contribution. Here, we modeled GWI in 30 BXD recombinant inbred mouse strains with a combined treatment of corticosterone (CORT) and diisopropyl fluorophosphate (DFP). We quantified transcriptomes from 409 prefrontal cortex samples. Compared to the untreated and DFP treated controls, the combined treatment significantly activated pathways such as cytokine-cytokine receptor interaction and TNF signaling pathway. Protein-protein interaction analysis defined 6 subnetworks for CORT + DFP, with the key regulators being Cxcl1, Il6, Ccnb1, Tnf, Agt, and Itgam. We also identified 21 differentially expressed genes having significant QTLs related to CORT + DFP, but without evidence for untreated and DFP treated controls, suggesting regions of the genome specifically involved in the response to CORT + DFP. We identified Adamts9 as a potential contributor to response to CORT + DFP and found links to symptoms of GWI. Furthermore, we observed a significant effect of CORT + DFP treatment on the relative proportion of myelinating oligodendrocytes, with a QTL on Chromosome 5. We highlight three candidates, Magi2, Sema3c, and Gnai1, based on their high expression in the brain and oligodendrocyte. In summary, our results show significant genetic effects of the CORT + DFP treatment, which mirrors gene and protein expression changes seen in GWI sufferers, providing insight into the disease and a testbed for future interventions.

Novel eye genes systematically discovered through an integrated analysis of mouse transcriptomes and phenome

In the last few decades, reverse genetic and high throughput approaches have been frequently applied to the mouse (Mus musculus) to understand how genes function in tissues/organs and during development in a mammalian system. Despite these efforts, the associated phenotypes for the majority of mouse genes remained to be fully characterized. Here, we performed an integrated transcriptome-phenome analysis by identifying coexpressed gene modules based on tissue transcriptomes profiled with each of various platforms and functionally interpreting these modules using the mouse phenotypic data. Consequently, >15,000 mouse genes were linked with at least one of the 47 tissue functions that were examined. Specifically, our approach predicted >50 genes previously unknown to be involved in mice (Mus musculus) visual functions. Fifteen genes were selected for further analysis based on their potential biomedical relevance and compatibility with further experimental validation. Gene-specific morpholinos were introduced into zebrafish (Danio rerio) to target their corresponding orthologs. Quantitative assessments of phenotypes of developing eyes confirmed predicted eye-related functions of 13 out of the 15 genes examined. These novel eye genes include: Adal, Ankrd33, Car14, Ccdc126, Dhx32, Dkk3, Fam169a, Grifin, Kcnj14, Lrit2, Ppef2, Ppm1n, and Wdr17. The results highlighted the potential for this phenome-based approach to assist the experimental design of mutating and phenotyping mouse genes that aims to fully reveal the functional landscape of mammalian genomes.

Born to Cry: A Genetic Dissection of Infant Vocalization

Infant vocalizations are one of the most fundamental and innate forms of behavior throughout avian and mammalian orders. They have a critical role in motivating parental care and contribute significantly to fitness and reproductive success. Dysregulation of these vocalizations has been reported to predict risk of central nervous system pathologies such as hypoxia, meningitis, or autism spectrum disorder. Here, we have used the expanded BXD family of mice, and a diallel cross between DBA/2J and C57BL/6J parental strains, to begin the process of genetically dissecting the numerous facets of infant vocalizations. We calculate heritability, estimate the role of parent-of-origin effects, and identify novel quantitative trait loci (QTLs) that control ultrasonic vocalizations (USVs) on postnatal days 7, 8, and 9; a stage that closely matches human infants at birth. Heritability estimates for the number and frequency of calls are low, suggesting that these traits are under high selective pressure. In contrast, duration and amplitude of calls have higher heritabilities, indicating lower selection, or their importance for kin recognition. We find suggestive evidence that amplitude of infant calls is dependent on the maternal genotype, independent of shared genetic variants. Finally, we identify two loci on Chrs 2 and 14 influencing call frequency, and a third locus on Chr 8 influencing the amplitude of vocalizations. All three loci contain strong candidate genes that merit further analysis. Understanding the genetic control of infant vocalizations is not just important for understanding the evolution of parent–offspring interactions, but also in understanding the earliest innate behaviors, the development of parent–offspring relations, and the early identification of behavioral abnormalities.

Defining Essentiality Score of Protein-Coding Genes and Long Noncoding RNAs

Measuring the essentiality of genes is critically important in biology and medicine. Here we proposed a computational method, GIC (Gene Importance Calculator), which can efficiently predict the essentiality of both protein-coding genes and long noncoding RNAs (lncRNAs) based on only sequence information. For identifying the essentiality of protein-coding genes, GIC outperformed well-established computational scores. In an independent mouse lncRNA dataset, GIC also achieved an exciting performance (AUC = 0.918). In contrast, the traditional computational methods are not applicable to lncRNAs. Moreover, we explored several potential applications of GIC score. Firstly, we revealed a correlation between gene GIC score and research hotspots of genes. Moreover, GIC score can be used to evaluate whether a gene in mouse is representative for its homolog in human by dissecting its cross-species difference. This is critical for basic medicine because many basic medical studies are performed in animal models. Finally, we showed that GIC score can be used to identify candidate genes from a transcriptomics study. GIC is freely available at http://www.cuilab.cn/gic/.

PregOMICS-Leveraging systems biology and bioinformatics for drug repurposing in maternal-child health

Obstetric diseases remain underserved and understudied. Drug repurposing-utilization of a drug whose use is accepted in one condition for a different condition-could represent a rapid and low-cost way to identify new therapies that are known to be safe. In diseases of pregnancy, the known safety profile is a strong additional incentive. We describe the techniques and steps used in the use of 'omics data for drug repurposing. We illustrate these techniques using case studies of published drug repurposing projects. We provide a set of available databases with low barriers to entry which investigators can use to perform their own projects. The promise of 'omics techniques is unbiased screening, either of all drug targets or of all patients using particular drugs to find which are likely to alter disease risk or progression. However, we caution that reproducibility across the underlying studies, and thus the drugs suggested for repurposing, can be poor. We suggest that improved nosology, for example correlating patient clinical conditions with placental pathology, could yield more robust associations. We conclude that 'omics-driven drug repurposing represents a potential fruitful path to discover new, safe treatments of obstetric diseases.

Sensing the cilium, digital capture of ciliary data for comparative genomics investigations

Background

Cilia are specialized, hair-like structures that project from the cell bodies of eukaryotic cells. With increased understanding of the distribution and functions of various types of cilia, interest in these organelles is accelerating. To effectively use this great expansion in knowledge, this information must be made digitally accessible and available for large-scale analytical and computational investigation. Capture and integration of knowledge about cilia into existing knowledge bases, thus providing the ability to improve comparative genomic data analysis, is the objective of this work.

Methods

We focused on the capture of information about cilia as studied in the laboratory mouse, a primary model of human biology. The workflow developed establishes a standard for capture of comparative functional data relevant to human biology. We established the 310 closest mouse orthologs of the 302 human genes defined in the SYSCILIA Gold Standard set of ciliary genes. For the mouse genes, we identified biomedical literature for curation and used Gene Ontology (GO) curation paradigms to provide functional annotations from these publications.

Results

Employing a methodology for comprehensive capture of experimental data about cilia genes in structured, digital form, we established a workflow for curation of experimental literature detailing molecular function and roles of cilia proteins starting with the mouse orthologs of the human SYSCILIA gene set. We worked closely with the GO Consortium ontology development editors and the SYSCILIA Consortium to improve the representation of ciliary biology within the GO. During the time frame of the ontology improvement project, we have fully curated 134 of these 310 mouse genes, resulting in an increase in the number of ciliary and other experimental annotations.

Conclusions

We have improved the GO annotations available for mouse genes orthologous to the human genes in the SYSCILIA Consortium’s Gold Standard set. In addition, ciliary terminology in the GO itself was improved in collaboration with GO ontology developers and the SYSCILIA Consortium. These improvements to the GO terms for the functions and roles of ciliary proteins, along with the increase in annotations of the corresponding genes, enhance the representation of ciliary processes and localizations and improve access to these data during large-scale bioinformatic analyses.

Electronic supplementary material

The online version of this article (10.1186/s13630-018-0057-0) contains supplementary material, which is available to authorized users.

Offspring genes indirectly influence sibling and maternal behavioural strategies over resource share

Family members show behavioural strategies predicted to maximize individual fitness. These behaviours depend directly on genes expressed in focal individuals but also indirectly on genes expressed in other family members. However, how sibling and parental behavioural strategies are modified by genes expressed in family members, and to what degree, remains unclear. To answer this question, we have used a split litter design in an experimental population of genetically variable mouse families, and identified loci that indirectly affected sibling and maternal behaviour simultaneously. These loci map to genomic regions that also show a direct effect on offspring behaviour. Directly and indirectly affected traits were significantly correlated at the phenotypic level, illustrating how indirect effects are caused. Genetic variants in offspring that influence solicitation also impacted their siblings' and maternal behaviour. However, in contrast to predictions from sibling competition, unrelated litter mates benefited from increased solicitation. Overall, such indirect genetic effects explained a large proportion of variation seen in behaviours, with candidate genes involved in metabolism to neuronal development. These results reveal that we need to view behavioural strategies as the result of conjoint selection on genetic variation in all interacting family members.

Discovery of Variants Underlying Host Susceptibility to Virus Infection Using Whole-Exome Sequencing

The clinical course of any viral infection greatly differs in individuals. This variation results from various viral, host, and environmental factors. The identification of host genetic factors influencing inter-individual variation in susceptibility to several pathogenic viruses has tremendously increased our understanding of the mechanisms and pathways required for immunity. Next-generation sequencing of whole exomes represents a powerful tool in biomedical research. In this chapter, we briefly introduce whole-exome sequencing in the context of genetic approaches to identify host susceptibility genes to viral infections. We then describe general aspects of the workflow for whole-exome sequence analysis together with the tools and online resources that can be used to identify and annotate variant calls, and then prioritize them for their potential association to phenotypes of interest.

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.

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.

Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures

Genetic variants identified by genome-wide association studies explain only a modest proportion of heritability, suggesting that meaningful associations lie 'hidden' below current thresholds. Here, we integrate information from association studies with epigenomic maps to demonstrate that enhancers significantly overlap known loci associated with the cardiac QT interval and QRS duration. We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies. We demonstrate that these 'sub-threshold' signals represent novel loci, and that epigenomic maps are effective at discriminating true biological signals from noise. We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse. Our work provides a general approach for improving the detection of novel loci associated with complex human traits.

Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures

Genetic variants identified by genome-wide association studies explain only a modest proportion of heritability, suggesting that meaningful associations lie 'hidden' below current thresholds. Here, we integrate information from association studies with epigenomic maps to demonstrate that enhancers significantly overlap known loci associated with the cardiac QT interval and QRS duration. We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies. We demonstrate that these 'sub-threshold' signals represent novel loci, and that epigenomic maps are effective at discriminating true biological signals from noise. We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse. Our work provides a general approach for improving the detection of novel loci associated with complex human traits.

DOI:http://dx.doi.org/10.7554/eLife.10557.001

Most complex traits are governed by a large number of genetic contributors, each playing only a modest effect. This makes it difficult to identify the genetic variants that increase disease risk, hindering the discovery of new drug targets and the development of new therapeutics.

To overcome this limitation in discovery power, the field of human genetics has traditionally sought increasingly large groups, or cohorts, of afflicted and non-afflicted individuals. Studies of large cohorts are a powerful approach for discovering new disease genes, but such groups are often impractical and sometimes impossible to obtain. However, it has become possible to complement the genetic evidence found in disease association studies with biological evidence of the effects of disease-associated genetic variants.

Wang et al. focus specifically on genetic sites, or loci, that do not affect protein sequence but instead affect the non-coding control regions. These are known as enhancer elements, as they can enhance the expression of nearby genes. These loci constitute the majority of disease regions, and thus are extremely important, but their discovery has been hindered by our relatively poor understanding of the human genome.

Chemical modifications known as epigenomic marks are indicative of enhancer regions. By studying the factors that affect heart rhythm, Wang et al. show that specific combinations of epigenomic marks are enriched in known trait-associated regions. This knowledge was then used to prioritize the further investigation of genetic regions that genome-wide association studies had only weakly linked to heart rhythm alterations. Wang et al. directly confirmed that genetic differences in “sub-threshold” regions indeed alter the activity of these regulatory regions in human heart cells. Furthermore, mutating or perturbing the predicted target genes of the sub-threshold enhancers caused heart defects in mouse and zebrafish.

Wang et al. have demonstrated that epigenome maps can help to distinguish which sub-threshold regions from genome-wide association studies are more likely to contribute to a disease. This allows for the discovery of new disease genes with much smaller cohorts than would be needed otherwise, thus speeding up the development of new therapeutics by many years.

DOI:http://dx.doi.org/10.7554/eLife.10557.002

ToxEvaluator: an integrated computational platform to aid the interpretation of toxicology study-related findings

Attempts are frequently made to investigate adverse findings from preclinical toxicology studies in order to better understand underlying toxicity mechanisms. These efforts often begin with limited information, including a description of the adverse finding, knowledge of the structure of the chemical associated with its cause and the intended pharmacological target. ToxEvaluator was developed jointly by Pfizer and the Comparative Toxicogenomics Database (http://ctdbase.org) team at North Carolina State University as an in silico platform to facilitate interpretation of toxicity findings in light of prior knowledge. Through the integration of a diverse set of in silico tools that leverage a number of public and proprietary databases, ToxEvaluator streamlines the process of aggregating and interrogating diverse sources of information. The user enters compound and target identifiers, and selects adverse event descriptors from a safety lexicon and mapped MeSH disease terms. ToxEvaluator provides a summary report with multiple distinct areas organized according to what target or structural aspects have been linked to the adverse finding, including primary pharmacology, structurally similar proprietary compounds, structurally similar public domain compounds, predicted secondary (i.e. off-target) pharmacology and known secondary pharmacology. Similar proprietary compounds and their associated in vivo toxicity findings are reported, along with a link to relevant supporting documents. For similar public domain compounds and interacting targets, ToxEvaluator integrates relationships curated in Comparative Toxicogenomics Database, returning all direct and inferred linkages between them. As an example of its utility, we demonstrate how ToxEvaluator rapidly identified direct (primary pharmacology) and indirect (secondary pharmacology) linkages between cerivastatin and myopathy.

Mouse Genome Informatics (MGI): reflecting on 25 years

From its inception in 1989, the mission of the Mouse Genome Informatics (MGI) resource remains to integrate genetic, genomic, and biological data about the laboratory mouse to facilitate the study of human health and disease. This mission is ever more feasible as the revolution in genetics knowledge, the ability to sequence genomes, and the ability to specifically manipulate mammalian genomes are now at our fingertips. Through major paradigm shifts in biological research and computer technologies, MGI has adapted and evolved to become an integral part of the larger global bioinformatics infrastructure and honed its ability to provide authoritative reference datasets used and incorporated by many other established bioinformatics resources. Here, we review some of the major changes in research approaches over that last quarter century, how these changes are reflected in the MGI resource you use today, and what may be around the next corner.