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

Mungall, Christopher J.; McMurry, Julie A.; Köhler, Sebastian; Balhoff, James P.; Borromeo, Charles; Brush, Matthew; Carbon, Seth; Conlin, Tom; Dunn, Nathan; Engelstad, Mark; Foster, Erin D.; Gourdine, Jean-Philippe; Jacobsen, Julius O. B.; Keith, Dan; Laraway, Bryan; Lewis, Suzanna; Nguyen-Xuan, Jeremy; Shefchek, Kent; Vasilevsky, Nicole; Zhou, Yuan; Washington, Nicole; Hochheiser, Harry; Groza, Tudor; Smedley, Damian; Robinson, Peter N.; Haendel, Melissa

doi:10.1093/nar/gkw1128

Cited by 357 publications

(240 citation statements)

References 58 publications

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“…For example, PhenoHM [51] uses the Unified Medical Language System (UMLS) MetaMap service [52] to map classes from MP to UMLS concepts describing disorders and phenotypes. The Uberpheno ontology [53] as well as the Monarch knowledge graph [54] use a combination of lexical mappings and ontology axioms to generate mappings between MP and HPO. The main difference to our work lies in the representation patterns that are used to represent phenotype classes and the way in which lexical mappings are generated.…”

Section: Discussionmentioning

confidence: 99%

Integrating phenotype ontologies with PhenomeNET

et al. 2017

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BackgroundIntegration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.ResultsHere, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.ConclusionsPhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-017-0167-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Integrating phenotype ontologies with PhenomeNET

et al. 2017

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“…The Alliance of Genome Resources (AGR; http://home.alliancegenome.org), an intersection between six major model organism databases (MGI, RGD, SGD, ZFIN, FlyBase and WormBase), aims to allow for cross species comparison for analysis of genetic, phenotypic, and disease-related annotations. The Monarch Initiative (monarchinitiative.org; Mungall et al 2017) integrates genotype to phenotype data across multiple species to support biomedical research. MouseNet2 (http://www.inetbio.org/mousenet/; Kim et al 2016) integrates gene network data, particularly from microarray experiments, for the discovery of novel disease genes and disease pathways.…”

Section: Discussionmentioning

confidence: 99%

Naming CRISPR alleles: endonuclease-mediated mutation nomenclature across species

Knowlton

Smith

2017

Mamm Genome

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The widespread use of CRISPR/Cas and other targeted endonuclease technologies in many species has led to an explosion in the generation of new mutations and alleles. The ability to generate many different mutations from the same target sequence either by homology-directed repair with a donor sequence or non-homologous end joining-induced insertions and deletions necessitates a means for representing these mutations in literature and databases. Standardized nomenclature can be used to generate unambiguous, concise, and specific symbols to represent mutations and alleles. The research communities of a variety of species using CRISPR/Cas and other endonuclease-mediated mutation technologies have developed different approaches to naming and identifying such alleles and mutations. While some organism-specific research communities have developed allele nomenclature that incorporates the method of generation within the official allele or mutant symbol, others use metadata tags that include method of generation or mutagen. Organism-specific research community databases together with organism-specific nomenclature committees are leading the way in providing standardized nomenclature and metadata to facilitate the integration of data from alleles and mutations generated using CRISPR/Cas and other targeted endonucleases.

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“…The Monarch annotation service [12] provides the SciGraph annotator that annotates user provided free text with ontology concepts and biological entities. SciGraph annotator marks the text with concepts from the Monarch knowledge graph that includes gene ontology terms, genes, diseases, and phenotypes.…”

Section: ) Scigraph Annotatormentioning

confidence: 99%

“…We present a comparison of four state of the art concept recognition tools (MetaMap [10], NCBO Annotator [1], Textpresso [11], and SciGraph [12]). We use the Colorado Richly Annotated Full-Text (CRAFT) corpus [13] as a Gold Standard reference to compare and assess the performance of these four NLP tools.…”

Section: Introductionmentioning

confidence: 99%

Comparison of natural language processing tools for automatic gene ontology annotation of scientific literature

Beasley¹,

Manda²

2018

Preprint

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Abstract-Manual curation of scientific literature for ontologybased knowledge representation has proven infeasible and unscalable to the large and growing volume of scientific literature. Automated annotation solutions that leverage text mining and Natural Language Processing (NLP) have been developed to ameliorate the problem of literature curation. These NLP approaches use parsing, syntactical, and lexical analysis of text to recognize and annotate pieces of text with ontology concepts. Here, we conduct a comparison of four state of the art NLP tools at the task of recognizing Gene Ontology concepts from biomedical literature using the Colorado Richly Annotated Full-Text (CRAFT) corpus as a gold standard reference. We demonstrate the use of semantic similarity metrics to compare NLP tool annotations to the gold standard.

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

Cited by 357 publications

References 58 publications

Integrating phenotype ontologies with PhenomeNET

Integrating phenotype ontologies with PhenomeNET

Naming CRISPR alleles: endonuclease-mediated mutation nomenclature across species

Comparison of natural language processing tools for automatic gene ontology annotation of scientific literature

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