Improved ontology-based similarity calculations using a study-wise annotation model

A typical use case of ontologies is the calculation of similarity scores between items that are annotated with classes of the ontology. For example, in differential diagnostics and disease gene prioritization, the human phenotype ontology (HPO) is often used to compare a query phenotype profile against gold-standard phenotype profiles of diseases or genes. The latter have long been constructed as flat lists of ontology classes, which, as we show in this work, can be improved by exploiting existing structure and information in annotation datasets or full text disease descriptions. We derive a study-wise annotation model of diseases and genes and show that this can improve the performance of semantic similarity measures. Inferred weights of individual annotations are one reason for this improvement, but more importantly using the study-wise structure further boosts the results of the algorithms according to precision-recall analyses. We test the study-wise annotation model for diseases annotated with classes from the HPO and for genes annotated with gene ontology (GO) classes. We incorporate this annotation model into similarity algorithms and show how this leads to improved performance. This work adds weight to the need for enhancing simple list-based representations of disease or gene annotations. We show how study-wise annotations can be automatically derived from full text summaries of disease descriptions and from the annotation data provided by the GO Consortium and how semantic similarity measure can utilize this extended annotation model

Creation and evaluation of full-text literature-derived, feature-weighted disease models of genetically determined developmental disorders

There are >2500 different genetically determined developmental disorders (DD), which, as a group, show very high levels of both locus and allelic heterogeneity. This has led to the wide-spread use of evidence-based filtering of genome-wide sequence data as a diagnostic tool in DD. Determining whether the association of a filtered variant at a specific locus is a plausible explanation of the phenotype in the proband is crucial and commonly requires extensive manual literature review by both clinical scientists and clinicians. Access to a database of weighted clinical features extracted from rigorously curated literature would increase the efficiency of this process and facilitate the development of robust phenotypic similarity metrics. However, given the large and rapidly increasing volume of published information, conventional biocuration approaches are becoming impractical. Here, we present a scalable, automated method for the extraction of categorical phenotypic descriptors from the full-text literature. Papers identified through literature review were downloaded and parsed using the Cadmus custom retrieval package. Human Phenotype Ontology terms were extracted using MetaMap, with 76–84% precision and 65–73% recall. Mean terms per paper increased from 9 in title + abstract, to 68 using full text. We demonstrate that these literature-derived disease models plausibly reflect true disease expressivity more accurately than widely used manually curated models, through comparison with prospectively gathered data from the Deciphering Developmental Disorders study. The area under the curve for receiver operating characteristic (ROC) curves increased by 5–10% through the use of literature-derived models. This work shows that scalable automated literature curation increases performance and adds weight to the need for this strategy to be integrated into informatic variant analysis pipelines. Database URL: https://doi.org/10.1093/database/baac03

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO\u27s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes

How exome sequencing is shedding light on the complexity of Mendelian disorders: some examples from Sardinia

The total number of Mendelian disorders is estimated to be around 7,000 and while each is individually rare, together, these genetic conditions contribute significantly to morbidity, mortality, and healthcare costs. In the last decade there has been a paradigm shift in their investigation due to the development of powerful new DNA sequencing technologies, such as whole exome sequencing. Although our knowledge of the diversity of Mendelian phenotypes is progressively increasing, substantial gaps remain. Up to 50% of patients affected by a rare genetic disorder never receive a diagnosis. We focused our attention on such Mendelian disorders and in a collaborative effort we studied by WES a cohort of heterogeneous samples affected by Crisponi/Cold-induced sweating syndrome-like, syndromic Intellectual Disabilities and Epileptic Encephalopathies. The results of our work along with others reported in the literature, are contributing to reveal the extensive clinical variability and genetic complexity underlying Mendelian phenotypes and inheritance, to provide insight into study design and approach and analytical strategies and to identify novel mechanisms. Our increasing knowledge on the genetic basis of rare disorders is shedding light on the “complex” nature of the “simple” Mendelian disorders and that “true monogenic” disorders are very rare, underscoring the current challenges of clinical diagnostics and discovery

Systematising and scaling literature curation for genetically determined developmental disorders

The widespread availability of genomic sequencing has transformed the diagnosis of genetically-determined developmental disorders (GDD). However, this type of test often generates a number of genetic variants, which have to be reviewed and related back to the clinical features (phenotype) of the individual being tested. This frequently entails a time-consuming review of the peer-reviewed literature to look for case reports describing variants in the gene(s) of interest. This is particularly true for newly described and/or very rare disorders not covered in phenotype databases. Therefore, there is a need for scalable, automated literature curation to increase the efficiency of this process. This should lead to improvements in the speed in which diagnosis is made, and an increase in the number of individuals who are diagnosed through genomic testing. Phenotypic data in case reports/case series is not usually recorded in a standardised, computationally-tractable format. Plain text descriptions of similar clinical features may be recorded in several different ways. For example, a technical term such as ‘hypertelorism’, may be recorded as its synonym ‘widely spaced eyes’. In addition, case reports are found across a wide range of journals, with different structures and file formats for each publication. The Human Phenotype Ontology (HPO) was developed to store phenotypic data in a computationally-accessible format. Several initiatives have been developed to link diseases to phenotype data, in the form of HPO terms. However, these rely on manual expert curation and therefore are not inherently scalable, and cannot be updated automatically. Methods of extracting phenotype data from text at scale developed to date have relied on abstracts or open access papers. At the time of writing, Europe PubMed Central (EPMC, https://europepmc.org/) contained approximately 39.5 million articles, of which only 3.8 million were open access. Therefore, there is likely a significant volume of phenotypic data which has not been used previously at scale, due to difficulties accessing non-open access manuscripts. In this thesis, I present a method for literature curation which can utilise all relevant published full text through a newly developed package which can download almost all manuscripts licenced by a university or other institution. This is scalable to the full spectrum of GDD. Using manuscripts identified through manual literature review, I use a full text download pipeline and NLP (natural language processing) based methods to generate disease models. These are comprised of HPO terms weighted according to their frequency in the literature. I demonstrate iterative refinement of these models, and use a custom annotated corpus of 50 papers to show the text mining process has high precision and recall. I demonstrate that these models clinically reflect true disease expressivity, as defined by manual comparison with expert literature reviews, for three well-characterised GDD. I compare these disease models to those in the most commonly used genetic disease phenotype databases. I show that the automated disease models have increased depth of phenotyping, i.e. there are more terms than those which are manually-generated. I show that, in comparison to ‘real life’ prospectively gathered phenotypic data, automated disease models outperform existing phenotype databases in predicting diagnosis, as defined by increased area under the curve (by 0.05 and 0.08 using different similarity measures) on ROC curve plots. I present a method for automated PubMed search at scale, to use as input for disease model generation. I annotated a corpus of 6500 abstracts. Using this corpus I show a high precision (up to 0.80) and recall (up to 1.00) for machine learning classifiers used to identify manuscripts relevant to GDD. These use hand-picked domain-specific features, for example utilising specific MeSH terms. This method can be used to scale automated literature curation to the full spectrum of GDD. I also present an analysis of the phenotypic terms used in one year of GDD-relevant papers in a prominent journal. This shows that use of supplemental data and parsing clinical report sections from manuscripts is likely to result in more patient-specific phenotype extraction in future. In summary, I present a method for automated curation of full text from the peer-reviewed literature in the context of GDD. I demonstrate that this method is robust, reflects clinical disease expressivity, outperforms existing manual literature curation, and is scalable. Applying this process to clinical testing in future should improve the efficiency and accuracy of diagnosis