ModelMatcher: A scientist-centric online platform to facilitate collaborations between stakeholders of rare and undiagnosed disease research

Integrating non-mammalian model organisms in the diagnosis of rare genetic diseases in humans

Next-generation sequencing technology has rapidly accelerated the discovery of genetic variants of interest in individuals with rare diseases. However, showing that these variants are causative of the disease in question is complex and may require functional studies. Use of non-mammalian model organisms - mainly fruitflies (Drosophila melanogaster), nematode worms (Caenorhabditis elegans) and zebrafish (Danio rerio) - enables the rapid and cost-effective assessment of the effects of gene variants, which can then be validated in mammalian model organisms such as mice and in human cells. By probing mechanisms of gene action and identifying interacting genes and proteins in vivo, recent studies in these non-mammalian model organisms have facilitated the diagnosis of numerous genetic diseases and have enabled the screening and identification of therapeutic options for patients. Studies in non-mammalian model organisms have also shown that the biological processes underlying rare diseases can provide insight into more common mechanisms of disease and the biological functions of genes. Here, we discuss the opportunities afforded by non-mammalian model organisms, focusing on flies, worms and fish, and provide examples of their use in the diagnosis of rare genetic diseases.

Finding information about uncharacterized Drosophila melanogaster genes

Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.

Promoting validation and cross-phylogenetic integration in model organism research

Model organism (MO) research provides a basic understanding of biology and disease due to the evolutionary conservation of the molecular and cellular language of life. MOs have been used to identify and understand the function of orthologous genes, proteins, cells and tissues involved in biological processes, to develop and evaluate techniques and methods, and to perform whole-organism-based chemical screens to test drug efficacy and toxicity. However, a growing richness of datasets and the rising power of computation raise an important question: How do we maximize the value of MOs? In-depth discussions in over 50 virtual presentations organized by the National Institutes of Health across more than 10 weeks yielded important suggestions for improving the rigor, validation, reproducibility and translatability of MO research. The effort clarified challenges and opportunities for developing and integrating tools and resources. Maintenance of critical existing infrastructure and the implementation of suggested improvements will play important roles in maintaining productivity and facilitating the validation of animal models of human biology and disease.

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in the years to come.