Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases

Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text- mining approach to identify the phenotypes (signs and symptoms) associated with over 8,000 diseases. We demonstrate that our method generates phenotypes that correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that share signs and symptoms cluster together, and we use this network to identify phenotypic disease modules

PCAN: phenotype consensus analysis to support disease-gene association

Comparison of genes within the “Anchoring of the basal body to the plasma membrane” pathway to HP terms describing Joubert syndrome. (a) Distribution of symmetric semantic similarity scores of genes for the 8 HP terms related to Joubert syndrome. The red bars correspond to the distribution of the scores of genes belonging to the pathway of interest. The grey bars correspond to the distribution of the scores for all the other genes. (The density of scores equal to 0 is truncated; its actual value is 12.8). (b) Symmetric semantic similarity scores of genes belonging to the pathway of interest. The gene candidate, CC2D2A, is highlighted. In the supplementary figure, the solid red line corresponds to the quantiles of the scores of all the genes. Dashed red lines show the value of three specific quantiles: 50, 75 and 95%. (c) Heatmap showing the best semantic similarity between each gene in the pathway of interest (columns) and each HP term under focus (rows). The red intensity of each square corresponds to the highest semantic similarity score between the HP term of interest and the gene associated HP terms (white: 0 and red: 5.2). The gene candidate, CC2D2A, is highlighted. (PPTX 198 kb

Unbiased functional clustering of gene variants with a phenotypic-linkage network

Groupwise functional analysis of gene variants is becoming standard in next-generation sequencing studies. As the function of many genes is unknown and their classification to pathways is scant, functional associations between genes are often inferred from large-scale omics data. Such data types—including protein–protein interactions and gene co-expression networks—are used to examine the interrelations of the implicated genes. Statistical significance is assessed by comparing the interconnectedness of the mutated genes with that of random gene sets. However, interconnectedness can be affected by confounding bias, potentially resulting in false positive findings. We show that genes implicated through de novo sequence variants are biased in their coding-sequence length and longer genes tend to cluster together, which leads to exaggerated p-values in functional studies; we present here an integrative method that addresses these bias. To discern molecular pathways relevant to complex disease, we have inferred functional associations between human genes from diverse data types and assessed them with a novel phenotype-based method. Examining the functional association between de novo gene variants, we control for the heretofore unexplored confounding bias in coding-sequence length. We test different data types and networks and find that the disease-associated genes cluster more significantly in an integrated phenotypic-linkage network than in other gene networks. We present a tool of superior power to identify functional associations among genes mutated in the same disease even after accounting for significant sequencing study bias and demonstrate the suitability of this method to functionally cluster variant genes underlying polygenic disorders

Improving Disease Gene Prioritization by Comparing the Semantic Similarity of Phenotypes in Mice with Those of Human Diseases

Despite considerable progress in understanding the molecular origins of hereditary human diseases, the molecular basis of several thousand genetic diseases still remains unknown. High-throughput phenotype studies are underway to systematically assess the phenotype outcome of targeted mutations in model organisms. Thus, comparing the similarity between experimentally identified phenotypes and the phenotypes associated with human diseases can be used to suggest causal genes underlying a disease. In this manuscript, we present a method for disease gene prioritization based on comparing phenotypes of mouse models with those of human diseases. For this purpose, either human disease phenotypes are “translated” into a mouse-based representation (using the Mammalian Phenotype Ontology), or mouse phenotypes are “translated” into a human-based representation (using the Human Phenotype Ontology). We apply a measure of semantic similarity and rank experimentally identified phenotypes in mice with respect to their phenotypic similarity to human diseases. Our method is evaluated on manually curated and experimentally verified gene–disease associations for human and for mouse. We evaluate our approach using a Receiver Operating Characteristic (ROC) analysis and obtain an area under the ROC curve of up to . Furthermore, we are able to confirm previous results that the Vax1 gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further potential candidates. Our method significantly outperforms previous phenotype-based approaches of prioritizing gene–disease associations. To enable the adaption of our method to the analysis of other phenotype data, our software and prioritization results are freely available under a BSD licence at http://code.google.com/p/phenomeblast/wiki/CAMP. Furthermore, our method has been integrated in PhenomeNET and the results can be explored using the PhenomeBrowser at http://phenomebrowser.net

An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations

Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations.Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches

To be or NOT to be: The Impact of Negative Annotation in Biomedical Semantic Similarity

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2022Classical Semantic Similarity Measures did not consider negative annotations in similarity compu tation, and the impact that these annotations can have in this data mining technique is not well studied. As such, this work aims to understand how the addition of negative annotations impacts semantic sim ilarity. To do so, two pairwise similarity measures, Best-Match Average and Resnik, were adapted to create the polar measures PolarBMA and PolarResnik. These were evaluated in two currently relevant scopes: protein-protein interaction prediction and disease prediction against the original measures. Pairs of proteins where the proteins were known to interact or not were taken from STRING and enriched with positive and negative annotations from the Gene Ontology. Synthetic patients were created as sets of annotations taken from the Mendelian diseases they were designed to have, as well as possible noise or imprecise annotations. Then semantic similarity was computed with both polar and non-polar measures between proteins in pairs and between patients and candidate diseases including the Mendelian diseases, as well as random diseases taken from the Human Phenotype Ontology. To evaluate if the polar measures performed well in comparison to the baseline, a ranking according to semantic similarity was made for each measure and scope for evaluation and the rank cumulative frequencies were plotted. ROC AUC and Precision-Recall curves were also determined for the Protein Protein interaction(PPI) prediction, as well as average precision for the disease prediction dataset. In PPI prediction, polar measures had an increased performance in the Molecular Function branch for both experiments where negative annotations were added and also in one of the experiments with the Cellular Component branch. In the disease prediction scope, polar measures had an improved performance of approximately ten percent. This improvement was verified in all disease prediction experiments, even with the addition of noise and imprecision. Considering the results obtained, this work concludes that negative annotations have an impact on semantic similarity, but the amplitude of this impact requires further study

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

Background: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods: Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge

Mouse model phenotypes provide information about human drug targets

Motivation: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. Results: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. Availability and implementation: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Hybrid approach for disease comorbidity and disease gene prediction using heterogeneous dataset

High throughput analysis and large scale integration of biological data led to leading researches in the field of bioinformatics. Recent years witnessed the development of various methods for disease associated gene prediction and disease comorbidity predictions. Most of the existing techniques use network-based approaches and similarity-based approaches for these predictions. Even though network-based approaches have better performance, these methods rely on text data from OMIM records and PubMed abstracts. In this method, a novel algorithm (HDCDGP) is proposed for disease comorbidity prediction and disease associated gene prediction. Disease comorbidity network and disease gene network were constructed using data from gene ontology (GO), human phenotype ontology (HPO), protein-protein interaction (PPI) and pathway dataset. Modified random walk restart algorithm was applied on these networks for extracting novel disease-gene associations. Experimental results showed that the hybrid approach has better performance compared to existing systems with an overall accuracy around 85%

A benchmark for biomedical knowledge graph based similarity

Tese de mestrado em Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2020Os grafos de conhecimento biomédicos são cruciais para sustentar aplicações em grandes quantidades de dados nas ciências da vida e saúde. Uma das aplicações mais comuns dos grafos de conhecimento nas ciências da vida é o apoio à comparação de entidades no grafo por meio das suas descrições ontológicas. Estas descrições suportam o cálculo da semelhança semântica entre duas entidades, e encontrar as suas semelhanças e diferenças é uma técnica fundamental para diversas aplicações, desde a previsão de interações proteína-proteína até à descoberta de associações entre doenças e genes, a previsão da localização celular de proteínas, entre outros. Na última década, houve um esforço considerável no desenvolvimento de medidas de semelhança semântica para grafos de conhecimento biomédico mas, até agora, a investigação nessa área tem-se concentrado na comparação de conjuntos de entidades relativamente pequenos. Dada a diversa gama de aplicações para medidas de semelhança semântica, é essencial apoiar a avaliação em grande escala destas medidas. No entanto, fazê-lo não é trivial, uma vez que não há um padrão ouro para a semelhança de entidades biológicas. Uma solução possível é comparar estas medidas com outras medidas ou proxies de semelhança. As entidades biológicas podem ser comparadas através de diferentes ângulos, por exemplo, a semelhança de sequência e estrutural de duas proteínas ou as vias metabólicas afetadas por duas doenças. Estas medidas estão relacionadas com as características relevantes das entidades, portanto podem ajudar a compreender como é que as abordagens de semelhança semântica capturam a semelhança das entidades. O objetivo deste trabalho é desenvolver um benchmark, composto por data sets e métodos de avaliação automatizados. Este benchmark deve sustentar a avaliação em grande escala de medidas de semelhança semântica para entidades biológicas, com base na sua correlação com diferentes propriedades das entidades. Para atingir este objetivo, uma metodologia para o desenvolvimento de data sets de referência para semelhança semântica foi desenvolvida e aplicada a dois grafos de conhecimento: proteínas anotadas com a Gene Ontology e genes anotados com a Human Phenotype Ontology. Este benchmark explora proxies de semelhança com base na semelhança de sequência, função molecular e interações de proteínas e semelhança de genes baseada em fenótipos, e fornece cálculos de semelhança semântica com medidas representativas do estado da arte, para uma avaliação comparativa. Isto resultou num benchmark composto por uma coleção de 21 data sets de referência com tamanhos variados, cobrindo quatro espécies e diferentes níveis de anotação das entidades, e técnicas de avaliação ajustadas aos data sets.Biomedical knowledge graphs are crucial to support data intensive applications in the life sciences and healthcare. One of the most common applications of knowledge graphs in the life sciences is to support the comparison of entities in the graph through their ontological descriptions. These descriptions support the calculation of semantic similarity between two entities, and finding their similarities and differences is a cornerstone technique for several applications, ranging from prediction of protein-protein interactions to the discovering of associations between diseases and genes, the prediction of cellular localization of proteins, among others. In the last decade there has been a considerable effort in developing semantic similarity measures for biomedical knowledge graphs, but the research in this area has so far focused on the comparison of relatively small sets of entities. Given the wide range of applications for semantic similarity measures, it is essential to support the large-scale evaluation of these measures. However, this is not trivial since there is no gold standard for biological entity similarity. One possible solution is to compare these measures to other measures or proxies of similarity. Biological entities can be compared through different lenses, for instance the sequence and structural similarity of two proteins or the metabolic pathways affected by two diseases. These measures relate to relevant characteristics of the underlying entities, so they can help to understand how well semantic similarity approaches capture entity similarity. The goal of this work is to develop a benchmark for semantic similarity measures, composed of data sets and automated evaluation methods. This benchmark should support the large-scale evaluation of semantic similarity measures for biomedical entities, based on their correlation to different properties of biological entities. To achieve this goal, a methodology for the development of benchmark data sets for semantic similarity was developed and applied to two knowledge graphs: proteins annotated with the Gene Ontology and genes annotated with the Human Phenotype Ontology. This benchmark explores proxies of similarity calculated based on protein sequence similarity, protein molecular function similarity, protein-protein interactions and phenotype-based gene similarity, and provides semantic similarity computations with state-of-the-art representative measures, for a comparative evaluation of the measures. This resulted in a benchmark made up of a collection of 21 benchmark data sets with varying sizes, covering four different species at different levels of annotation completion and evaluation techniques fitted to the data sets characteristics