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

Artificial intelligence and the hunt for immunological disorders

PURPOSE OF REVIEW

Artificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential.

RECENT FINDINGS

Artificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement.

SUMMARY

This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.

Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease

The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other hum an disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current know ledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.

PAWER: protein array web exploreR

Background

Protein microarray is a well-established approach for characterizing activity levels of thousands of proteins in a parallel manner. Analysis of protein microarray data is complex and time-consuming, while existing solutions are either outdated or challenging to use without programming skills. The typical data analysis pipeline consists of a data preprocessing step, followed by differential expression analysis, which is then put into context via functional enrichment. Normally, biologists would need to assemble their own workflow by combining a set of unrelated tools to analyze experimental data. Provided that most of these tools are developed independently by various bioinformatics groups, making them work together could be a real challenge.

Results

Here we present PAWER, the online web tool dedicated solely to protein microarray analysis. PAWER enables biologists to carry out all the necessary analysis steps in one go. PAWER provides access to state-of-the-art computational methods through the user-friendly interface, resulting in publication-ready illustrations. We also provide an R package for more advanced use cases, such as bespoke analysis workflows.

Conclusions

PAWER is freely available at https://biit.cs.ut.ee/pawer.

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result. However, given that the overall success rate of genomic diagnostics is only around 25%-50% or less in many cohorts, a good ranking cannot be taken to imply that the gene or disease at rank one is necessarily a good candidate. Here, we present an approach to genomic diagnostics that exploits the likelihood ratio (LR) framework to provide an estimate of (1) the posttest probability of candidate diagnoses, (2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes. LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) placed the correct diagnosis within the first three ranks in 92.9% of 384 case reports comprising 262 Mendelian diseases, and the correct diagnosis had a mean posttest probability of 67.3%. Simulations show that LIRICAL is robust to many typically encountered forms of genomic and phenomic noise. In summary, LIRICAL provides accurate, clinically interpretable results for phenotype-driven genomic diagnostics.

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management

Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. Clarifying the mechanisms of toxicity to evaluate and manage drug safety during drug development is extremely important. However, toxicity mechanisms, especially hepatotoxic mechanisms, are very complex. The significant exposure of liver cells to drugs can cause dysfunction, cell injury, and organ failure in the liver. To clarify potential risks in drug safety management, it is necessary to systematize knowledge from a consistent viewpoint. In this study, we adopt an ontological approach. Ontology provides a controlled vocabulary for sharing and reusing of various data with a computer-friendly manner. We focus on toxic processes, especially hepatotoxic processes, and construct the toxic process ontology (TXPO). The TXPO systematizes knowledge concerning hepatotoxic courses with consistency and no ambiguity. In our application study, we developed a toxic process interpretable knowledge system (TOXPILOT) to bridge the gaps between basic science and medicine for drug safety management. Using semantic web technology, TOXPILOT supports the interpretation of toxicity mechanisms and provides visualizations of toxic courses with useful information based on ontology. Our system will contribute to various applications for drug safety evaluation and management.

Genome Sequencing as a Diagnostic Test in Children With Unexplained Medical Complexity

Importance

Children with medical complexity (CMC) represent a growing population in the pediatric health care system, with high resource use and associated health care costs. A genetic diagnosis can inform prognosis, anticipatory care, management, and reproductive planning. Conventional genetic testing strategies for CMC are often costly, time consuming, and ultimately unsuccessful.

Objective

To evaluate the analytical and clinical validity of genome sequencing as a comprehensive diagnostic genetic test for CMC.

Design, Setting, and Participants

In this cohort study of the prospective use of genome sequencing and comparison with standard-of-care genetic testing, CMC were recruited from May 1, 2017, to November 30, 2018, from a structured complex care program based at a tertiary care pediatric hospital in Toronto, Canada. Recruited CMC had at least 1 chronic condition, technology dependence (child is dependent at least part of each day on mechanical ventilators, and/or child requires prolonged intravenous administration of nutritional substances or drugs, and/or child is expected to have prolonged dependence on other device-based support), multiple subspecialist involvement, and substantial health care use. Review of the care plans for 545 CMC identified 143 suspected of having an undiagnosed genetic condition. Fifty-four families met inclusion criteria and were interested in participating, and 49 completed the study. Probands, similarly affected siblings, and biological parents were eligible for genome sequencing.

Exposures

Genome sequencing was performed using blood-derived DNA from probands and family members using established methods and a bioinformatics pipeline for clinical genome annotation.

Main Outcomes and Measures

The primary study outcome was the diagnostic yield of genome sequencing (proportion of CMC for whom the test result yielded a new diagnosis).

Results

Genome sequencing was performed for 138 individuals from 49 families of CMC (29 male and 20 female probands; mean [SD] age, 7.0 [4.5] years). Genome sequencing detected all genomic variation previously identified by conventional genetic testing. A total of 15 probands (30.6%; 95% CI 19.5%-44.6%) received a new primary molecular genetic diagnosis after genome sequencing. Three individuals had novel diseases and an additional 9 had either ultrarare genetic conditions or rare genetic conditions with atypical features. At least 11 families received diagnostic information that had clinical management implications beyond genetic and reproductive counseling.

Conclusions and Relevance

This study suggests that genome sequencing has high analytical and clinical validity and can result in new diagnoses in CMC even in the setting of extensive prior investigations. This clinical population may be enriched for ultrarare and novel genetic disorders. Genome sequencing is a potentially first-tier genetic test for CMC.

The Male Fertility Gene Atlas: a web tool for collecting and integrating OMICS data in the context of male infertility

STUDY QUESTION

How can one design and implement a system that provides a comprehensive overview of research results in the field of epi-/genetics of male infertility and germ cells?

SUMMARY ANSWER

Working at the interface of literature search engines and raw data repositories, the newly developed Male Fertility Gene Atlas (MFGA) provides a system that can represent aggregated results from scientific publications in a standardized way and perform advanced searches, for example based on the conditions (phenotypes) and genes related to male infertility.

WHAT IS KNOWN ALREADY

PubMed and Google Scholar are established search engines for research literature. Additionally, repositories like Gene Expression Omnibus and Sequence Read Archive provide access to raw data. Selected processed data can be accessed by visualization tools like the ReproGenomics Viewer.

STUDY DESIGN, SIZE, DURATION

The MFGA was developed in a time frame of 18 months under a rapid prototyping approach.

PARTICIPANTS/MATERIALS, SETTING, METHODS

In the context of the Clinical Research Unit 'Male Germ Cells' (CRU326), a group of around 50 domain experts in the fields of male infertility and germ cells helped to develop the requirements engineering and feedback loops. They provided a set of 39 representative and heterogeneous publications to establish a basis for the system requirements.

MAIN RESULTS AND THE ROLE OF CHANCE

The MFGA is freely available online at https://mfga.uni-muenster.de. To date, it contains 115 data sets corresponding to 54 manually curated publications and provides an advanced search function based on study conditions, meta-information and genes, whereby it returns the publications' exact tables and figures that fit the search request as well as a list of the most frequently investigated genes in the result set. Currently, study data for 31 different tissue types, 32 different cell types and 20 conditions are available. Also, ∼8000 and ∼1000 distinct genes have been found to be mentioned in at least 10 and 15 of the publications, respectively.

LARGE SCALE DATA

Not applicable because no novel data were produced.

LIMITATIONS, REASONS FOR CAUTION

For the most part, the content of the system currently includes the selected publications from the development process. However, a structured process for the prospective literature search and inclusion into the MFGA has been defined and is currently implemented.

WIDER IMPLICATIONS OF THE FINDINGS

The technical implementation of the MFGA allows for accommodating a wide range of heterogeneous data from aggregated research results. This implementation can be transferred to other diseases to establish comparable systems and generally support research in the medical field.

STUDY FUNDING/COMPETING INTEREST(S)

This work was carried out within the frame of the German Research Foundation (DFG) Clinical Research Unit 'Male Germ Cells: from Genes to Function' (CRU326). The authors declare no conflicts of interest.

MITO-FIND: A study in 390 patients to determine a diagnostic strategy for mitochondrial disease

Mitochondrial diseases, due to nuclear or mitochondrial genome mutations causing mitochondrial dysfunction, have a wide range of clinical features involving neurologic, muscular, cardiac, hepatic, visual, and auditory symptoms. Making a diagnosis of a mitochondrial disease is often challenging since there is no gold standard and traditional testing methods have required tissue biopsy which presents technical challenges and most patients prefer a non-invasive approach. Since a diagnosis invariably involves finding a disease-causing DNA variant, new approaches such as next generation sequencing (NGS) have the potential to make it easier to make a diagnosis. We evaluated the ability of our traditional diagnostic pathway (metabolite analysis, tissue neuropathology and respiratory chain enzyme activity) in 390 patients. The traditional diagnostic pathway provided a diagnosis of mitochondrial disease in 115 patients (29.50%). Analysis of mtDNA, tissue neuropathology, skin electron microscopy, respiratory chain enzyme analysis using inhibitor assays, blue native polyacrylamide gel electrophoresis were all statistically significant in distinguishing patients between a mitochondrial and non-mitochondrial diagnosis. From these 390 patients who underwent traditional analysis, we recruited 116 patients for the NGS part of the study (36 patients who had a mitochondrial diagnosis (MITO) and 80 patients who had no diagnosis (No-Dx)). In the group of 36 MITO patients, nuclear whole exome sequencing (nWES) provided a second diagnosis in 2 cases who already had a pathogenic variant in mtDNA, and a revised diagnosis (GLUL) in one case that had abnormal pathology but no pathogenic mtDNA variant. In the 80 NO-Dx patients, nWES found non-mitochondrial diagnosis in 26 patients and a mitochondrial diagnosis in 1 patient. A genetic diagnosis was obtained in 53/116 (45.70%) cases that were recruited for NGS, but not in 11/116 (9.48%) of cases with abnormal mitochondrial neuropathology. Our results show that a non-invasive, bigenomic sequencing (BGS) approach (using both a nWES and optimized mtDNA analysis to include large deletions) should be the first step in investigating for mitochondrial diseases. There may still be a role for tissue biopsy in unsolved cases or when the diagnosis is still not clear after NGS studies.

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.

New Diagnostic Approaches for Undiagnosed Rare Genetic Diseases

Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.

Computational Methods and Software Tools for Functional Analysis of miRNA Data

miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.

Study on Intervention Mechanism of Yiqi Huayu Jiedu Decoction on ARDS Based on Network Pharmacology

Background

Yiqi Huayu Jiedu (YQHYJD) is a traditional Chinese medicine decoction made up of eight traditional Chinese medicines. Although YQHYJD is effectively used to prevent and treat ARDS/acute lung injury (ALI) in rats, the molecular mechanisms supporting its clinical application remain elusive. The purpose of the current study was to understand its lung protective effects at the molecular level using network pharmacology approach.

Methods

In an ARDS animal model, the beneficial pharmacological activities of YQHYJD were confirmed by reduced lung tissue damage levels observed on drug treated rats versus control group. We then proposed a network analysis to discover the key nodes based on drugs and disease network. Subsequently, we analyzed interaction networks and screened key targets. Using Western blot to detect the expression level of key targets, the intervention effect of changes in expression level of key targets on ARDS was evaluated.

Results

Pathway enrichment analysis of highly ranked genes showed that ErbB pathways were highly related to ARDS. Finally, western blot results showed decreased level of the AKT1 and KRAS/NRAS/HRAS protein in the lung after treatment which confirmed the hypothesis.

Conclusion

In conclusion, our results suggest that YQHYJD can exert lung tissue protective effect against the severe injury through multiple pathways, including the endothelial cells permeability improvement, inflammatory reaction inhibition, edema, and lung tissue hemorrhage reduction.

Systems Biology Approaches Reveal Potential Phenotype-Modifier Genes in Neurofibromatosis Type 1

Neurofibromatosis type (NF1) is a syndrome characterized by varied symptoms, ranging from mild to more aggressive phenotypes. The variation is not explained only by genetic and epigenetic changes in the NF1 gene and the concept of phenotype-modifier genes in extensively discussed in an attempt to explain this variability. Many datasets and tools are already available to explore the relationship between genetic variation and disease, including systems biology and expression data. To suggest potential NF1 modifier genes, we selected proteins related to NF1 phenotype and NF1 gene ontologies. Protein–protein interaction (PPI) networks were assembled, and network statistics were obtained by using forward and reverse genetics strategies. We also evaluated the heterogeneous networks comprising the phenotype ontologies selected, gene expression data, and the PPI network. Finally, the hypothesized phenotype-modifier genes were verified by a random-walk mathematical model. The network statistics analyses combined with the forward and reverse genetics strategies, and the assembly of heterogeneous networks, resulted in ten potential phenotype-modifier genes: AKT1, BRAF, EGFR, LIMK1, PAK1, PTEN, RAF1, SDC2, SMARCA4, and VCP. Mathematical models using the random-walk approach suggested SDC2 and VCP as the main candidate genes for phenotype-modifiers.

Consequences of 22q11.2 Microdeletion on the Genome, Individual and Population Levels

Chromosomal 22q11.2 deletion syndrome (22q11.2DS) (ORPHA: 567) caused by microdeletion in chromosome 22 is the most common chromosomal microdeletion disorder in humans. Despite the same change on the genome level, like in the case of monozygotic twins, phenotypes are expressed differently in 22q11.2 deletion individuals. The rest of the genome, as well as epigenome and environmental factors, are not without influence on the variability of phenotypes. The penetrance seems to be more genotype specific than deleted locus specific. The transcript levels of deleted genes are not usually reduced by 50% as assumed due to haploinsufficiency. 22q11.2DS is often an undiagnosed condition, as each patient may have a different set out of 180 possible clinical manifestations. Diverse dysmorphic traits are present in patients from different ethnicities, which makes diagnosis even more difficult. 22q11.2 deletion syndrome serves as an example of a genetic syndrome that is not easy to manage at all stages: diagnosis, consulting and dealing with.

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

Objectives : To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning.

Methods : A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning.

Results : Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology ; (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs.

Conclusion : Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research.

Expanding the phenotype of CRYAA nucleotide variants to a complex presentation of anterior segment dysgenesis

Background

Mutations in CRYAA, which encodes the α-crystallin protein, are associated with a spectrum of congenital cataract–microcornea syndromes.

Results

In this study, we performed clinical examination and subsequent genetic analysis in two unrelated sporadic cases of different geographical origins presenting with a complex phenotype of ocular malformation. Both cases manifested bilateral microphthalmia and severe anterior segment dysgenesis, primarily characterized by congenital aphakia, microcornea, and iris hypoplasia/aniridia. NGS-based analysis revealed two novel single nucleotide variants occurring de novo and affecting the translation termination codon of the CRYAA gene, c.520T > C and c.521A > C. Both variants are predicted to elongate the C-terminal protein domain by one-third of the original length.

Conclusions

Our report not only expands the mutational spectrum of CRYAA but also identifies the genetic cause of the unusual ocular phenotype described in this report.

Phexpo: a package for bidirectional enrichment analysis of phenotypes and chemicals

Phenotypes are the result of the complex interplay between environmental and genetic factors. To better understand the interactions between chemical compounds and human phenotypes, and further exposome research we have developed “phexpo,” a tool to perform and explore bidirectional chemical and phenotype interactions using enrichment analyses. Phexpo utilizes gene annotations from 2 curated public repositories, the Comparative Toxicogenomics Database and the Human Phenotype Ontology. We have applied phexpo in 3 case studies linking: (1) individual chemicals (a drug, warfarin, and an industrial chemical, chloroform) with phenotypes, (2) individual phenotypes (left ventricular dysfunction) with chemicals, and (3) multiple phenotypes (covering polycystic ovary syndrome) with chemicals. The results of these analyses demonstrated successful identification of relevant chemicals or phenotypes supported by bibliographic references. The phexpo R package (https://github.com/GHLCLab/phexpo) provides a new bidirectional analyses approach covering relationships from chemicals to phenotypes and from phenotypes to chemicals.

gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

g:Profiler ( https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

g:Profiler ( https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

Choosing preferable labels for the Japanese translation of the Human Phenotype Ontology

The Human Phenotype Ontology (HPO) is the de facto standard ontology to describe human phenotypes in detail, and it is actively used, particularly in the field of rare disease diagnoses. For clinicians who are not fluent in English, the HPO has been translated into many languages, and there have been four initiatives to develop Japanese translations. At the Biomedical Linked Annotation Hackathon 6 (BLAH6), a rule-based approach was attempted to determine the preferable Japanese translation for each HPO term among the candidates developed by the four approaches. The relationship between the HPO and Mammalian Phenotype translations was also investigated, with the eventual goal of harmonizing the two translations to facilitate phenotype-based comparisons of species in Japanese through cross-species phenotype matching. In order to deal with the increase in the number of HPO terms and the need for manual curation, it would be useful to have a dictionary containing word-by-word correspondences and fixed translation phrases for English word order. These considerations seem applicable to HPO localization into other languages.

Improving accessibility and distinction between negative results in biomedical relation extraction

Accessible negative results are relevant for researchers and clinicians not only to limit their search space but also to prevent the costly re-exploration of research hypotheses. However, most biomedical relation extraction datasets do not seek to distinguish between a false and a negative relation among two biomedical entities. Furthermore, datasets created using distant supervision techniques also have some false negative relations that constitute undocumented/unknown relations (missing from a knowledge base). We propose to improve the distinction between these concepts, by revising a subset of the relations marked as false on the phenotype-gene relations corpus and give the first steps to automatically distinguish between the false (F), negative (N), and unknown (U) results. Our work resulted in a sample of 127 manually annotated FNU relations and a weighted-F1 of 0.5609 for their automatic distinction. This work was developed during the 6th Biomedical Linked Annotation Hackathon (BLAH6).

Transcriptome-Wide Regulation of Key Developmental Pathways in the Mouse Neural Tube by Prenatal Alcohol Exposure

BACKGROUND

Early gestational alcohol exposure is associated with severe craniofacial and CNS dysmorphologies and behavioral abnormalities during adolescence and adulthood. Alcohol exposure during the formation of the neural tube (gestational day [GD] 8 to 10 in mice; equivalent to4th week of human pregnancy) disrupts development of ventral midline brain structures such as the pituitary, septum, and ventricles. This study identifies transcriptomic changes in the rostroventral neural tube (RVNT), the region of the neural tube that gives rise to the midline structures sensitive to alcohol exposure during neurulation.

METHODS

Female C57BL/6J mice were administered 2 doses of alcohol (2.9 g/kg) or vehicle 4 hours apart on GD 9.0. The RVNTs of embryos were collected 6 or 24 hours after the first dose and processed for RNA-seq.

RESULTS

Six hours following GD 9.0 alcohol exposure (GD 9.25), over 2,300 genes in the RVNT were determined to be differentially regulated by alcohol. Enrichment analysis determined that PAE affected pathways related to cell proliferation, p53 signaling, ribosome biogenesis, and immune activation. In addition, over 100 genes involved in primary cilia formation and function and regulation of morphogenic pathways were altered 6 hours after alcohol exposure. The changes to gene expression were largely transient, as only 91 genes identified as differentially regulated by prenatal alcohol at GD 10 (24 hours postexposure). Functionally, the differentially regulated genes at GD 10 were related to organogenesis and cell migration.

CONCLUSIONS

These data give a comprehensive view of the changing landscape of the embryonic transcriptome networks in regions of the neural tube that give rise to brain structures impacted by a neurulation-stage alcohol exposure. Identification of gene networks dysregulated by alcohol will help elucidate the pathogenic mechanisms of alcohol's actions.

Biomedical named entity recognition and linking datasets: survey and our recent development

Natural language processing (NLP) is widely applied in biological domains to retrieve information from publications. Systems to address numerous applications exist, such as biomedical named entity recognition (BNER), named entity normalization (NEN) and protein–protein interaction extraction (PPIE). High-quality datasets can assist the development of robust and reliable systems; however, due to the endless applications and evolving techniques, the annotations of benchmark datasets may become outdated and inappropriate. In this study, we first review commonlyused BNER datasets and their potential annotation problems such as inconsistency and low portability. Then, we introduce a revised version of the JNLPBA dataset that solves potential problems in the original and use state-of-the-art named entity recognition systems to evaluate its portability to different kinds of biomedical literature, including protein–protein interaction and biology events. Lastly, we introduce an ensembled biomedical entity dataset (EBED) by extending the revised JNLPBA dataset with PubMed Central full-text paragraphs, figure captions and patent abstracts. This EBED is a multi-task dataset that covers annotations including gene, disease and chemical entities. In total, it contains 85000 entity mentions, 25000 entity mentions with database identifiers and 5000 attribute tags. To demonstrate the usage of the EBED, we review the BNER track from the AI CUP Biomedical Paper Analysis challenge. Availability: The revised JNLPBA dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/Re vised_JNLPBA.zip. The EBED dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/AICUP _EBED_dataset.rar. Contact: Email: thtsai@g.ncu.edu.tw, Tel. 886-3-4227151 ext. 35203, Fax: 886-3-422-2681 Email: hsu@iis.sinica.edu.tw, Tel. 886-2-2788-3799 ext. 2211, Fax: 886-2-2782-4814 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Drosophila models of pathogenic copy-number variant genes show global and non-neuronal defects during development

While rare pathogenic copy-number variants (CNVs) are associated with both neuronal and non-neuronal phenotypes, functional studies evaluating these regions have focused on the molecular basis of neuronal defects. We report a systematic functional analysis of non-neuronal defects for homologs of 59 genes within ten pathogenic CNVs and 20 neurodevelopmental genes in Drosophila melanogaster. Using wing-specific knockdown of 136 RNA interference lines, we identified qualitative and quantitative phenotypes in 72/79 homologs, including 21 lines with severe wing defects and six lines with lethality. In fact, we found that 10/31 homologs of CNV genes also showed complete or partial lethality at larval or pupal stages with ubiquitous knockdown. Comparisons between eye and wing-specific knockdown of 37/45 homologs showed both neuronal and non-neuronal defects, but with no correlation in the severity of defects. We further observed disruptions in cell proliferation and apoptosis in larval wing discs for 23/27 homologs, and altered Wnt, Hedgehog and Notch signaling for 9/14 homologs, including AATF/Aatf, PPP4C/Pp4-19C, and KIF11/Klp61F. These findings were further supported by tissue-specific differences in expression patterns of human CNV genes, as well as connectivity of CNV genes to signaling pathway genes in brain, heart and kidney-specific networks. Our findings suggest that multiple genes within each CNV differentially affect both global and tissue-specific developmental processes within conserved pathways, and that their roles are not restricted to neuronal functions.

Rare copy-number variants (CNVs), or large deletions and duplications in the genome, are associated with both neuronal and non-neuronal clinical features. Previous functional studies for these disorders have primarily focused on understanding the cellular mechanisms for neurological and behavioral phenotypes. To understand how genes within these CNVs contribute to developmental defects in non-neuronal tissues, we assessed 79 homologs of CNV and known neurodevelopmental genes in Drosophila models. We found that most homologs showed developmental defects when knocked down in the adult fly wing, ranging from mild size changes to severe wrinkled wings or lethality. Although a majority of tested homologs showed defects when knocked down specifically in wings or eyes, we found no correlation in the severity of the observed defects in these two tissues. A subset of the homologs showed disruptions in cellular processes in the developing fly wing, including alterations in cell proliferation, apoptosis, and cellular signaling pathways. Furthermore, human CNV genes also showed differences in gene expression patterns and interactions with signaling pathway genes across multiple human tissues. Our findings suggest that genes within CNV disorders affect global developmental processes in both neuronal and non-neuronal tissues.

Disrupted minor intron splicing is prevalent in Mendelian disorders

Background

Splicing is crucial for proper gene expression, and is predominately executed by the major spliceosome. Conversely, 722 introns in 699 human minor intron‐containing genes (MIGs) are spliced by the minor spliceosome. Splicing of these minor introns is disrupted in diseases caused by pathogenic variants in the minor spliceosome, ultimately leading to the aberrant expression of a subset of these MIGs. However, the effect of variants in minor introns and MIGs on diseases remains unexplored.

Methods

Variants in MIGs and associated clinical manifestations were identified using ClinVar. The HPO database was then used to curate the related symptoms and affected organ systems. Results: We found pathogenic variants in 211 MIGs, which commonly resulted in intellectual disability, seizures and microcephaly. This revealed a subset of MIGs whose aberrant splicing may contribute to the pathogenesis of minor spliceosome‐related diseases. Moreover, we identified 51 pathogenic variants in minor intron splice sites that reduce the splice site strength and can induce alternative splicing.

Conclusion

These findings highlight that disrupted minor intron splicing has a broader impact on human diseases than previously appreciated. The hope is that this knowledge will aid in the development of therapeutic strategies that incorporate the minor intron splicing pathway.

A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.

Using conceptual modeling to improve genome data management

With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. These data must be accessible, informative and reliable enough to extract valuable knowledge in the context of the identification of evidence supporting the relationship between DNA variants and disease. The approach presented in this paper provides a solution that help researchers to organize, store and process information focusing only on the data that are relevant and minimizing the impact that the information overload has in clinical and research contexts. A case-study (epilepsy) is also presented, to demonstrate its application in a real context.

A novel mosaic variant on SMC1A reported in buccal mucosa cells, albeit not in blood, of a patient with Cornelia de Lange-like presentation

Mosaicism in Cornelia de Lange syndrome (CdLS) has been reported in clinically diagnosed CdLS patients with negative molecular testing using blood as the specimen, particularly in the NIPBL gene. Here we report a novel mosaic variant in SMC1A identified in the buccal swab DNA of a patient with a mild CdLS phenotype. Our patient presented with global developmental delay, dysmorphic features, microcephaly, and short stature, with no limb defect. Face2Gene, a digital tool that analyzes facial morphology, demonstrated a 97% match between our patient and the CdLS gestalt. An initial next-generation sequencing (NGS)-based CdLS panel test, including NIPBL, HDAC8, RAD21, SMC1A, and SMC3, completed using DNA isolated from leukocytes, was negative, and subsequent trio exome sequencing was nondiagnostic. The exome identified biallelic variants of uncertain significance in a candidate gene, NSMCE2. In the pursuit of a molecular diagnosis, a second NGS-based CdLS panel test was ordered on a buccal swab specimen and a novel variant, c.793_795delGAG (p.Glu265del) in SMC1A, was detected at 60% mosaicism. Retrospective analysis of the former panel and exome data revealed the SMC1A variant at 4% and 2%, respectively, both far below standard reporting thresholds. Given that mosaicism has been frequently reported in CdLS, we suggest selecting a different tissue for testing in clinically suspected CdLS cases, even after negative molecular results via blood specimen.

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

The DisGeNET knowledge platform for disease genomics: 2019 update

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

Gene4Denovo: an integrated database and analytic platform for de novo mutations in humans

De novo mutations (DNMs) significantly contribute to sporadic diseases, particularly in neuropsychiatric disorders. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) provide effective methods for detecting DNMs and prioritizing candidate genes. However, it remains a challenge for scientists, clinicians, and biologists to conveniently access and analyse data regarding DNMs and candidate genes from scattered publications. To fill the unmet need, we integrated 580 799 DNMs, including 30 060 coding DNMs detected by WES/WGS from 23 951 individuals across 24 phenotypes and prioritized a list of candidate genes with different degrees of statistical evidence, including 346 genes with false discovery rates <0.05. We then developed a database called Gene4Denovo (http://www.genemed.tech/gene4denovo/), which allowed these genetic data to be conveniently catalogued, searched, browsed, and analysed. In addition, Gene4Denovo integrated data from >60 genomic sources to provide comprehensive variant-level and gene-level annotation and information regarding the DNMs and candidate genes. Furthermore, Gene4Denovo provides end-users with limited bioinformatics skills to analyse their own genetic data, perform comprehensive annotation, and prioritize candidate genes using custom parameters. In conclusion, Gene4Denovo conveniently allows for the accelerated interpretation of DNM pathogenicity and the clinical implication of DNMs in humans.

Advances in the diagnosis of inherited neuromuscular diseases and implications for therapy development

Advances in DNA sequencing technologies have resulted in a near doubling, in under 10 years, of the number of causal genes identified for inherited neuromuscular disorders. However, around half of patients, whether children or adults, do not receive a molecular diagnosis after initial diagnostic workup. Massively parallel technologies targeting RNA, proteins, and metabolites are being increasingly used to diagnose these unsolved cases. The use of these technologies to delineate pathways, biomarkers, and therapeutic targets has led to new approaches entering the drug development pipeline. However, these technologies might give rise to misleading conclusions if used in isolation, and traditional techniques including comprehensive neurological evaluation, histopathology, and biochemistry continue to have a crucial role in diagnostics. For optimal diagnosis, prognosis, and precision medicine, no single ruling technology exists. Instead, an interdisciplinary approach combining novel and traditional neurological techniques with computer-aided analysis and international data sharing is needed to advance the diagnosis and treatment of neuromuscular disorders.

Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases

Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPacket descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed and acts as a real-time phenotype-driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients' de-identified HPO term data from the Children's Hospital of Philadelphia.

Revealing the Common Mechanisms of Scutellarin in Angina Pectoris and Ischemic Stroke Treatment via a Network Pharmacology Approach

OBJECTIVE

To investigate the shared mechanisms of scutellarin in angina pectoris (AP) and ischemic stroke (IS) treatment.

METHODS

A network pharmacology approach was used to detect the potential mechanisms of scutellarin in AP and IS treatment by target prediction, protein-protein interaction (PPI) data collection, network construction, network analysis, and enrichment analysis. Furthermore, molecular docking simulation was employed to analyze the interaction between scutellarin and core targets.

RESULTS

Two networks were established, including a disease-target network and a PPI network of scutellarin targets against AP and IS. Network analysis showed that 14 targets, namely, AKT1, VEGFA, JUN, ALB, MTOR, ESR1, MAPK8, HSP90AA1, NOS3, SERPINE1, FGA, F2, FOXO3, and STAT1, might be the therapeutic targets of scutellarin in AP and IS. Among them, NOS3 and F2 were recognized as the core targets. Additionally, molecular docking simulation confifirmed that scutellarin exhibited a relatively high potential for binding to the active sites of NOS3 and F2. Furthermore, enrichment analysis indicated that scutellarin might exert a therapeutic role in both AP and IS by regulating several important pathways, such as coagulation cascades, mitogen-activated protein kinase (MAPK) signaling pathway, phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway, Toll-like receptor signaling pathway, hypoxia inducible factor-1 (HIF-1) signaling pathway, forkhead box O (FoxO) signaling pathway, tumor necrosis factor (TNF) signaling pathway, adipocytokine signaling pathway, insulin signaling pathway, insulin resistance, and estrogen signaling pathway.

CONCLUSIONS

The shared underlying mechanisms of scutellarin on AP and IS treatment might be strongly associated with its vasorelaxant, anticoagulant, anti-inflammatory, and antioxidative effects as well as its effect on improving lipid metabolism.

Doc2Hpo: a web application for efficient and accurate HPO concept curation

We present Doc2Hpo, an interactive web application that enables interactive and efficient phenotype concept curation from clinical text with automated concept normalization using the Human Phenotype Ontology (HPO). Users can edit the HPO concepts automatically extracted by Doc2Hpo in real time, and export the extracted HPO concepts into gene prioritization tools. Our evaluation showed that Doc2Hpo significantly reduced manual effort while achieving high accuracy in HPO concept curation. Doc2Hpo is freely available at https://impact2.dbmi.columbia.edu/doc2hpo/. The source code is available at https://github.com/stormliucong/doc2hpo for local installation for protected health data.

PhenoModifier: a genetic modifier database for elucidating the genetic basis of human phenotypic variation

From clinical observations to large-scale sequencing studies, the phenotypic impact of genetic modifiers is evident. To better understand the full spectrum of the genetic contribution to human disease, concerted efforts are needed to construct a useful modifier resource for interpreting the information from sequencing data. Here, we present the PhenoModifier (https://www.biosino.org/PhenoModifier), a manually curated database that provides a comprehensive overview of human genetic modifiers. By manually curating over ten thousand published articles, 3078 records of modifier information were entered into the current version of PhenoModifier, related to 288 different disorders, 2126 genetic modifier variants and 843 distinct modifier genes. To help users probe further into the mechanism of their interested modifier genes, we extended the yeast genetic interaction data and yeast quantitative trait loci to the human and we also integrated GWAS data into the PhenoModifier to assist users in evaluating all possible phenotypes associated with a modifier allele. As the first comprehensive resource of human genetic modifiers, PhenoModifier provides a more complete spectrum of genetic factors contributing to human phenotypic variation. The portal has a broad scientific and clinical scope, spanning activities relevant to variant interpretation for research purposes as well as clinical decision making.

RegulationSpotter: annotation and interpretation of extratranscriptic DNA variants

RegulationSpotter is a web-based tool for the user-friendly annotation and interpretation of DNA variants located outside of protein-coding transcripts (extratranscriptic variants). It is designed for clinicians and researchers who wish to assess the potential impact of the considerable number of non-coding variants found in Whole Genome Sequencing runs. It annotates individual variants with underlying regulatory features in an intuitive way by assessing over 100 genome-wide annotations. Additionally, it calculates a score, which reflects the regulatory potential of the variant region. Its dichotomous classifications, ‘functional’ or ‘non-functional’, and a human-readable presentation of the underlying evidence allow a biologically meaningful interpretation of the score. The output shows key aspects of every variant and allows rapid access to more detailed information about its possible role in gene regulation. RegulationSpotter can either analyse single variants or complete VCF files. Variants located within protein-coding transcripts are automatically assessed by MutationTaster as well as by RegulationSpotter to account for possible intragenic regulatory effects. RegulationSpotter offers the possibility of using phenotypic data to focus on known disease genes or genomic elements interacting with them. RegulationSpotter is freely available at https://www.regulationspotter.org.

HPOLabeler: improving prediction of human protein–phenotype associations by learning to rank

Motivation

Annotating human proteins by abnormal phenotypes has become an important topic. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities encountered in human diseases. As of November 2019, only &lt;4000 proteins have been annotated with HPO. Thus, a computational approach for accurately predicting protein–HPO associations would be important, whereas no methods have outperformed a simple Naive approach in the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2).

Results

We present HPOLabeler, which is able to use a wide variety of evidence, such as protein–protein interaction (PPI) networks, Gene Ontology, InterPro, trigram frequency and HPO term frequency, in the framework of learning to rank (LTR). LTR has been proved to be powerful for solving large-scale, multi-label ranking problems in bioinformatics. Given an input protein, LTR outputs the ranked list of HPO terms from a series of input scores given to the candidate HPO terms by component learning models (logistic regression, nearest neighbor and a Naive method), which are trained from given multiple evidence. We empirically evaluate HPOLabeler extensively through mainly two experiments of cross validation and temporal validation, for which HPOLabeler significantly outperformed all component models and competing methods including the current state-of-the-art method. We further found that (i) PPI is most informative for prediction among diverse data sources and (ii) low prediction performance of temporal validation might be caused by incomplete annotation of new proteins.

Availability and implementation

http://issubmission.sjtu.edu.cn/hpolabeler/.

Contact

zhusf@fudan.edu.cn

Supplementary information

Supplementary data are available at Bioinformatics online.

Applying knowledge-driven mechanistic inference to toxicogenomics

When considering toxic chemicals in the environment, a mechanistic, causal explanation of toxicity may be preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This mechanistic inference framework is an advantageous tool for predictive toxicology, and the first of its kind to produce a mechanistic explanation for each prediction. MechSpy can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes

PURPOSE

Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments.

METHODS

We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data.

RESULTS

Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs.

CONCLUSION

Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

Diagnostic yield of panel-based genetic testing in syndromic inherited retinal disease

Thirty percent of all inherited retinal disease (IRD) is accounted for by conditions with extra-ocular features. This study aimed to establish the genetic diagnostic pick-up rate for IRD patients with one or more extra-ocular features undergoing panel-based screening in a clinical setting. One hundred and six participants, tested on a gene panel which contained both isolated and syndromic IRD genes, were retrospectively ascertained from the Manchester Genomic Diagnostics Laboratory database spanning 6 years (2012-2017). Phenotypic features were extracted from the clinical notes and classified according to Human Phenotype Ontology; all identified genetic variants were interpreted in accordance to the American College of Medical Genetics and Genomics guidelines. Overall, 49% (n = 52) of patients received a probable genetic diagnosis. A further 6% (n = 6) had a single disease-associated variant in an autosomal recessive disease-relevant gene. Fifty-two percent (n = 55) of patients had a clinical diagnosis at the time of testing. Of these, 71% (n = 39) received a probable genetic diagnosis. By contrast, for those without a provisional clinical diagnosis (n = 51), only 25% (n = 13) received a probable genetic diagnosis. The clinical diagnosis of Usher (n = 33) and Bardet-Biedl syndrome (n = 10) was confirmed in 67% (n = 22) and 80% (n = 8), respectively. The testing diagnostic rate in patients with clinically diagnosed multisystemic IRD conditions was significantly higher than those without one (71% versus 25%; p value < 0.001). The lower pick-up rate in patients without a clinical diagnosis suggests that panel-based approaches are unlikely to be the most effective means of achieving a molecular diagnosis for this group. Here, we suggest that genome-wide approaches (whole exome or genome) are more appropriate.

Genomic Data Sharing for Novel Mendelian Disease Gene Discovery: The Matchmaker Exchange

In the last decade, exome and/or genome sequencing has become a common test in the diagnosis of individuals with features of a rare Mendelian disorder. Despite its success, this test leaves the majority of tested individuals undiagnosed. This review describes the Matchmaker Exchange (MME), a federated network established to facilitate the solving of undiagnosed rare-disease cases through data sharing. MME supports genomic matchmaking, the act of connecting two or more parties looking for cases with similar phenotypes and variants in the same candidate genes. An application programming interface currently connects six matchmaker nodes-the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER), GeneMatcher, PhenomeCentral, seqr, MyGene2, and the Initiative on Rare and Undiagnosed Diseases (IRUD) Exchange-resulting in a collective data set spanning more than 150,000 cases from more than 11,000 contributors in 88 countries. Here, we describe the successes and challenges of MME, its individual matchmaking nodes, plans for growing the network, and considerations for future directions.

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients' sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein-protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients' HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

Evaluation of the quality of clinical data collection for a pan-Canadian cohort of children affected by inherited metabolic diseases: lessons learned from the Canadian Inherited Metabolic Diseases Research Network

BACKGROUND

The Canadian Inherited Metabolic Diseases Research Network (CIMDRN) is a pan-Canadian practice-based research network of 14 Hereditary Metabolic Disease Treatment Centres and over 50 investigators. CIMDRN aims to develop evidence to improve health outcomes for children with inherited metabolic diseases (IMD). We describe the development of our clinical data collection platform, discuss our data quality management plan, and present the findings to date from our data quality assessment, highlighting key lessons that can serve as a resource for future clinical research initiatives relating to rare diseases.

METHODS

At participating centres, children born from 2006 to 2015 who were diagnosed with one of 31 targeted IMD were eligible to participate in CIMDRN's clinical research stream. For all participants, we collected a minimum data set that includes information about demographics and diagnosis. For children with five prioritized IMD, we collected longitudinal data including interventions, clinical outcomes, and indicators of disease management. The data quality management plan included: design of user-friendly and intuitive clinical data collection forms; validation measures at point of data entry, designed to minimize data entry errors; regular communications with each CIMDRN site; and routine review of aggregate data.

RESULTS

As of June 2019, CIMDRN has enrolled 798 participants of whom 764 (96%) have complete minimum data set information. Results from our data quality assessment revealed that potential data quality issues were related to interpretation of definitions of some variables, participants who transferred care across institutions, and the organization of information within the patient charts (e.g., neuropsychological test results). Little information was missing regarding disease ascertainment and diagnosis (e.g., ascertainment method - 0% missing).

DISCUSSION

Using several data quality management strategies, we have established a comprehensive clinical database that provides information about care and outcomes for Canadian children affected by IMD. We describe quality issues and lessons for consideration in future clinical research initiatives for rare diseases, including accurately accommodating different clinic workflows and balancing comprehensiveness of data collection with available resources. Integrating data collection within clinical care, leveraging electronic medical records, and implementing core outcome sets will be essential for achieving sustainability.

Triple diagnosis of Wiedemann-Steiner, Waardenburg and DLG3-related intellectual disability association found by WES: A case report

BACKGROUND

The development of whole-exome sequencing (WES) and whole-genome sequencing (WGS) for clinical purposes now allows the identification of multiple pathogenic variants in patients with a rare disease. This occurs even when a single causative gene was initially suspected. We report the case of an 8-year-old patient with global developmental delays and dysmorphic features, with a possibly pathogenic variant in three distinct genes.

METHODS

Trio-based exome sequencing was performed by IntegraGen SA (Evry, France), on an Illumina HiSeq4000 (Illumina, San Diego, CA, USA). Sanger sequencing was performed to confirm the variants that were found.

RESULTS

WES showed the presence of three possibly deleterious variants: KMT2A: c.9068delA;p.Gln3023Argfs*3 de novo, PAX3: c.530C>G;p.Ala177Gly de novo and DLG3: c.127delG;p.Asp43Metfs*22 hemizygous inherited from the mother. KMT2A pathogenic variants are involved in Wiedemann-Steiner syndrome, and PAX3 is the gene responsible for Waardenburg syndrome. DLG3 variants have been described in a non-syndromic X-related intellectual disability.

CONCLUSIONS

Considering the dysmorphic features and intellectual disability presented by this patient, these three variants were imputed as pathogenic and their association was considered responsible for his phenotype. Dual molecular diagnoses have already been found by WES in several cohorts with an average of diagnostic yield of 7%. This case demonstrates and reminds us of the importance of analyzing exomes rigorously and exhaustively because, in some cases (< 10%), it can explain superimposed traits or blended phenotypes.

Genetic landscape of autism spectrum disorder in Vietnamese children

Autism spectrum disorder (ASD) is a complex disorder with an unclear aetiology and an estimated global prevalence of 1%. However, studies of ASD in the Vietnamese population are limited. Here, we first conducted whole exome sequencing (WES) of 100 children with ASD and their unaffected parents. Our stringent analysis pipeline was able to detect 18 unique variants (8 de novo and 10 ×-linked, all validated), including 12 newly discovered variants. Interestingly, a notable number of X-linked variants were detected (56%), and all of them were found in affected males but not in affected females. We uncovered 17 genes from our ASD cohort in which CHD8, DYRK1A, GRIN2B, SCN2A, OFD1 and MDB5 have been previously identified as ASD risk genes, suggesting the universal aetiology of ASD for these genes. In addition, we identified six genes that have not been previously reported in any autism database: CHM, ENPP1, IGF1, LAS1L, SYP and TBX22. Gene ontology and phenotype-genotype analysis suggested that variants in IGF1, SYP and LAS1L could plausibly confer risk for ASD. Taken together, this study adds to the genetic heterogeneity of ASD and is the first report elucidating the genetic landscape of ASD in Vietnamese children.