The Monarch Initiative

Last updated
The Monarch Initiative
Content
DescriptionBioinformatics database of genetic disease data
Data types
captured
genotype, phenotype, variant, disease, species
Organisms Metazoa
Contact
Primary citationMungall et al. 2017
Release dateJuly 12, 2015
Access
Website https://monarchinitiative.org

The Monarch Initiative [1] is a large scale bioinformatics web resource focused on leveraging existing biomedical knowledge to connect genotypes with phenotypes in an effort to aid research that combats genetic diseases. Monarch does this by integrating multi-species genotype, phenotype, genetic variant and disease knowledge from various existing biomedical data resources into a centralized and structured database. While this integration process has been traditionally done manually by basic researchers and clinicians on a case-by-case basis, The Monarch Initiative provides an aggregated and structured collection of data and tools that make biomedical knowledge exploration more efficient and effective.

Contents

Mondo ontology

Mondo ontology is product of the Monarch Initiative and provides harmonized disease content for diseases and disorders, both rare and common. [2] The rare disease subset has been published with >10.5 rare diseases [3] , and is maintained by the community [4] .

Related Research Articles

An allele, or allelomorph, is a variant of the sequence of nucleotides at a particular location, or locus, on a DNA molecule.

<span class="mw-page-title-main">Genotype–phenotype distinction</span> Distinction made in genetics

The genotype–phenotype distinction is drawn in genetics. The "genotype" is an organism's full hereditary information. The "phenotype" is an organism's actual observed properties, such as morphology, development, or behavior. This distinction is fundamental in the study of inheritance of traits and their evolution.

A rare disease is a disease that affects a small percentage of the population. In some parts of the world, the term orphan disease describes a rare disease whose rarity results in little or no funding or research for treatments, without financial incentives from governments or other agencies. Orphan drugs are medications targeting orphan diseases.

Online Mendelian Inheritance in Man (OMIM) is a continuously updated catalog of human genes and genetic disorders and traits, with a particular focus on the gene-phenotype relationship. As of 28 June 2019, approximately 9,000 of the over 25,000 entries in OMIM represented phenotypes; the rest represented genes, many of which were related to known phenotypes.

The Gene Ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. More specifically, the project aims to: 1) maintain and develop its controlled vocabulary of gene and gene product attributes; 2) annotate genes and gene products, and assimilate and disseminate annotation data; and 3) provide tools for easy access to all aspects of the data provided by the project, and to enable functional interpretation of experimental data using the GO, for example via enrichment analysis. GO is part of a larger classification effort, the Open Biomedical Ontologies, being one of the Initial Candidate Members of the OBO Foundry.

The Rat Genome Database (RGD) is a database of rat genomics, genetics, physiology and functional data, as well as data for comparative genomics between rat, human and mouse. RGD is responsible for attaching biological information to the rat genome via structured vocabulary, or ontology, annotations assigned to genes and quantitative trait loci (QTL), and for consolidating rat strain data and making it available to the research community. They are also developing a suite of tools for mining and analyzing genomic, physiologic and functional data for the rat, and comparative data for rat, mouse, human, and five other species.

The Open Biological and Biomedical Ontologies (OBO) Foundry is a group of people who build and maintain ontologies related to the life sciences. The OBO Foundry establishes a set of principles for ontology development for creating a suite of interoperable reference ontologies in the biomedical domain. Currently, there are more than a hundred ontologies that follow the OBO Foundry principles.

<span class="mw-page-title-main">Zebrafish Information Network</span> Model organism database on zebrafish

The Zebrafish Information Network is an online biological database of information about the zebrafish. The zebrafish is a widely used model organism for genetic, genomic, and developmental studies, and ZFIN provides an integrated interface for querying and displaying the large volume of data generated by this research. To facilitate use of the zebrafish as a model of human biology, ZFIN links these data to corresponding information about other model organisms and to human disease databases. Abundant links to external sequence databases and to genome browsers are included. Gene product, gene expression, and phenotype data are annotated with terms from biomedical ontologies. ZFIN is based at the University of Oregon in the United States, with funding provided by the National Institutes of Health (NIH).

Genotype to Phenotype Databases: a Holistic Approach (GEN2PHEN) is a European project aiming to develop a knowledge web portal integrating information from the genotype to the phenotype in a unifying portal: The Knowledge Centre].

Xenbase is a Model Organism Database (MOD), providing informatics resources, as well as genomic and biological data on Xenopus frogs. Xenbase has been available since 1999, and covers both X. laevis and X. tropicalis Xenopus varieties. As of 2013 all of its services are running on virtual machines in a private cloud environment, making it one of the first MODs to do so. Other than hosting genomics data and tools, Xenbase supports the Xenopus research community though profiles for researchers and laboratories, and job and events postings.

Suzanna (Suzi) E. Lewis was a scientist and Principal investigator at the Berkeley Bioinformatics Open-source Project based at Lawrence Berkeley National Laboratory until her retirement in 2019. Lewis led the development of open standards and software for genome annotation and ontologies.

The Uber-anatomy ontology (Uberon) is a comparative anatomy ontology representing a variety of structures found in animals, such as lungs, muscles, bones, feathers and fins. These structures are connected to other structures via relationships such as part-of and develops-from. One of the uses of this ontology is to integrate data from different biological databases, and other species-specific ontologies such as the Foundational Model of Anatomy.

<span class="mw-page-title-main">International Mouse Phenotyping Consortium</span>

The International Mouse Phenotyping Consortium (IMPC) is an international scientific endeavour to create and characterize the phenotype of 20,000 knockout mouse strains. Launched in September 2011, the consortium consists of over 15 research institutes across four continents with funding provided by the NIH, European national governments and the partner institutions.

The Human Phenotype Ontology (HPO) is a formal ontology of human phenotypes. Developed as part of the Monarch Initiative in collaboration with members of the Open Biomedical Ontologies Foundry, HPO currently contains over 13,000 terms and over 156,000 annotations to hereditary diseases. Data from Online Mendelian Inheritance in Man and medical literature were used to generate the terms currently in the HPO. The ontology contains over 50,000 annotations between phenotypes and hereditary disease.

<span class="mw-page-title-main">Bgee</span> Gene expression database

Bgee is a database maintained by the SIB Swiss Institute of Bioinformatics and the University of Lausanne for retrieval and comparison of gene expression patterns from RNA-Seq, scRNA-Seq, Microarray, In situ hybridization and EST studies, across multiple animal species. Bgee provides an intuitive answer to the question where is a gene expressed? and supports research in cancer and agriculture, as well as evolutionary biology.

In bioinformatics, a Gene Disease Database is a systematized collection of data, typically structured to model aspects of reality, in a way to comprehend the underlying mechanisms of complex diseases, by understanding multiple composite interactions between phenotype-genotype relationships and gene-disease mechanisms. Gene Disease Databases integrate human gene-disease associations from various expert curated databases and text mining derived associations including Mendelian, complex and environmental diseases.

DisGeNET is a discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET is one of the largest and comprehensive repositories of human gene-disease associations (GDAs) currently available. It also offers a set of bioinformatic tools to facilitate the analysis of these data by different user profiles. It is maintained by the Integrative Biomedical Informatics (IBI) GroupArchived 2016-11-26 at the Wayback Machine, of the (GRIB)-IMIM/UPF, based at the Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.

PathoPhenoDB is a biological database. The database connects pathogens to their phenotypes using multiple databases such as NCBI, Human Disease Ontology Human Phenotype Ontology, Mammalian Phenotype Ontology, PubChem, SIDER and CARD. Pathogen-disease associations were gathered mainly through the CDC and the List of Infectious Diseases page on Wikipedia. The manner by which they assigned taxonomy was semi-automatic. When mapped against NCBI Taxonomy, if the pathogen was not an exact match, it was then mapped to the parent class. PathoPhenoDB employs NPMI in order to filter pairs based on their co-occurrence statistics.

<span class="mw-page-title-main">Melissa Haendel</span> American bioinformaticist

Melissa Anne Haendel is an American bioinformaticist who is the Chief Research Informatics Officer of the Anschutz Medical Campus of the University of Colorado as well as a Professor of Biochemistry and Molecular Genetics and the Marsico Chair in Data Science. She serves as Director of the Center for Data to Health (CD2H). Her research makes use of data to improve the discovery and diagnosis of diseases. During the COVID-19 pandemic, Haendel joined with the National Institutes of Health to launch the National COVID Cohort Collaborative (N3C), which looks to identify the risk factors that can predict severity of disease outcome and help to identify treatments.

References

  1. Mungall, Christopher J.; McMurry, Julie A.; Köhler, Sebastian; Balhoff, James P.; Borromeo, Charles; Brush, Matthew; Carbon, Seth; Conlin, Tom; Dunn, Nathan (2017-01-04). "The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species". Nucleic Acids Research. 45 (D1): D712–D722. doi:10.1093/nar/gkw1128. ISSN   1362-4962. PMC   5210586 . PMID   27899636.
  2. Shefchek, Kent A; Harris, Nomi L; Gargano, Michael; Matentzoglu, Nicolas; Unni, Deepak; Brush, Matthew; Keith, Daniel; Conlin, Tom; Vasilevsky, Nicole; Zhang, Xingmin Aaron; Balhoff, James P; Babb, Larry; Bello, Susan M; Blau, Hannah; Bradford, Yvonne; Carbon, Seth; Carmody, Leigh; Chan, Lauren E; Cipriani, Valentina; Cuzick, Alayne; Rocca, Maria D; Dunn, Nathan; Essaid, Shahim; Fey, Petra; Grove, Chris; Gourdine, Jean-Phillipe; Hamosh, Ada; Harris, Midori; Helbig, Ingo; Hoatlin, Maureen; Joachimiak, Marcin; Jupp, Simon; Lett, Kenneth B; Lewis, Suzanna E; McNamara, Craig; Pendlington, Zoë M; Pilgrim, Clare; Putman, Tim; Ravanmehr, Vida; Reese, Justin; Riggs, Erin; Robb, Sofia; Roncaglia, Paola; Seager, James; Segerdell, Erik; Similuk, Morgan; Storm, Andrea L; Thaxon, Courtney; Thessen, Anne; Jacobsen, Julius O B; McMurry, Julie A; Groza, Tudor; Köhler, Sebastian; Smedley, Damian; Robinson, Peter N; Mungall, Christopher J; Haendel, Melissa A; Munoz-Torres, Monica C; Osumi-Sutherland, David (8 November 2019). "The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species". Nucleic Acids Research. 48 (D1): D704–D715. doi: 10.1093/nar/gkz997 . PMC   7056945 . PMID   31701156.
  3. Haendel, Melissa; Vasilevsky, Nicole; Unni, Deepak; Bologa, Cristian; Harris, Nomi; Rehm, Heidi; Hamosh, Ada; Baynam, Gareth; Groza, Tudor; McMurry, Julie; Dawkins, Hugh; Rath, Ana; Thaxon, Courtney; Bocci, Giovanni; Joachimiak, Marcin P. (February 2020). "How many rare diseases are there?". Nature Reviews. Drug Discovery. 19 (2): 77–78. doi:10.1038/d41573-019-00180-y. ISSN   1474-1776. PMC   7771654 . PMID   32020066.
  4. "Rare disease subset - Mondo Documentation". mondo.readthedocs.io. Retrieved 2024-09-05.