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

BACKGROUND: A vast array of data is about to emerge from the large scale high-throughput mouse knockout phenotyping projects worldwide. It is critical that this information is captured in a standardized manner, made accessible, and is fully integrated with other phenotype data sets for comprehensive querying and analysis across all phenotype data types. The volume of data generated by the high-throughput phenotyping screens is expected to grow exponentially, thus, automated methods and standards to exchange phenotype data are required. RESULTS: The IMPC (International Mouse Phenotyping Consortium) is using the Mammalian Phenotype (MP) ontology in the automated annotation of phenodeviant data from high throughput phenotyping screens. 287 new term additions with additional hierarchy revisions were made in multiple branches of the MP ontology to accurately describe the results generated by these high throughput screens. CONCLUSIONS: Because these large scale phenotyping data sets will be reported using the MP as the common data standard for annotation and data exchange, automated importation of these data to MGI (Mouse Genome Informatics) and other resources is possible without curatorial effort. Maximum biomedical value of these mutant mice will come from integrating primary high-throughput phenotyping data with secondary, comprehensive phenotypic analyses combined with published phenotype details on these and related mutants at MGI and other resources

New models for human disease from the International Mouse Phenotyping Consortium

International audienceThe 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

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

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

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

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

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\u27s 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