基于语义的疾病表型相似性

已知一种药物可用于治疗某疾病,则该药物可能对与该疾病具有相似表型的其他疾病有疗效。因此,大规模地计算疾病表型相似性可辅助发现的疾病新的治疗方法。我们从OMIM下载了3742种疾病的表型信息,从Mesh词库下载13721个关联解剖学和疾病症状的注释词。我们将以上的Mesh词逐一在3742种疾病的表型信息文本中搜索,得到每种疾病涉及的Mesh词汇列表,进而基于语义分析的方法系统地计算了疾病表型的两两相似性矩阵。我们发现疾病关联生物通路最多的有肿瘤生物通路,胰岛素信号通路,肥大心肌病通路和细胞粘附通路等。随疾病对表型相似度的增加,其更涉及相同KEGG生物通路的概率亦增加,证明了本文方法的可靠性。疾病表型相似性可作为疾病在基因水平相似性的补充,有望为药物发现研究提供一条新途径。     

The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease

There are many thousands of hereditary diseases in humans, each of which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by lack of adequate computational data structures. Therefore, we have developed a Human Phenotype Ontology (HPO) with over 8000 terms representing individual phenotypic anomalies and have annotated all clinical entries in Online Mendelian Inheritance in Man with the terms of the HPO. We show that the HPO is able to capture phenotypic similarities between diseases in a useful and highly significant fashion.     

Predicting Novel Human Gene Ontology Annotations Using Semantic Analysis

The correct interpretation of many molecular biology experiments depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are meant to act as repositories for our biological knowledge as we acquire and refine it. Hence, by definition, they are incomplete at any given time. In this paper, we describe a technique that improves our previous method for predicting novel GO annotations by extracting implicit semantic relationships between genes and functions. In this work, we use a vector space model and a number of weighting schemes in addition to our previous latent semantic indexing approach. The technique described here is able to take into consideration the hierarchical structure of the Gene Ontology (GO) and can weight differently GO terms situated at different depths. The prediction abilities of 15 different weighting schemes are compared and evaluated. Nine such schemes were previously used in other problem domains, while six of them are introduced in this paper. The best weighting scheme was a novel scheme, n2tn. Out of the top 50 functional annotations predicted using this weighting scheme, we found support in the literature for 84 percent of them, while 6 percent of the predictions were contradicted by the existing literature. For the remaining 10 percent, we did not find any relevant publications to confirm or contradict the predictions. The n2tn weighting scheme also outperformed the simple binary scheme used in our previous approach.     

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease–lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.     

HPOSim: An R Package for Phenotypic Similarity Measure and Enrichment Analysis Based on the Human Phenotype Ontology

BackgroundPhenotypic features associated with genes and diseases play an important role in disease-related studies and most of the available methods focus solely on the Online Mendelian Inheritance in Man (OMIM) database without considering the controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized and controlled vocabulary covering phenotypic abnormalities in human diseases, and becomes a comprehensive resource for computational analysis of human disease phenotypes. Most of the existing HPO-based software tools cannot be used offline and provide only few similarity measures. Therefore, there is a critical need for developing a comprehensive and offline software for phenotypic features similarity based on HPO.ResultsHPOSim is an R package for analyzing phenotypic similarity for genes and diseases based on HPO data. Seven commonly used semantic similarity measures are implemented in HPOSim. Enrichment analysis of gene sets and disease sets are also implemented, including hypergeometric enrichment analysis and network ontology analysis (NOA).ConclusionsHPOSim can be used to predict disease genes and explore disease-related function of gene modules. HPOSim is open source and freely available at SourceForge (https://sourceforge.net/p/hposim/).     

Focus: Rare Disease: Neuromonitoring in Rare Disorders of Metabolism

Inborn errors of metabolism (IEM) are a unique class of genetic diseases due to mutations in genes involved in key metabolic pathways. The combined incidence of IEM has been estimated to be as high as 1:1000. Urea Cycle disorders (UCD), one class of IEM, can present with cerebral edema and represent a possible target to explore the utility of different neuromonitoring techniques during an hyperammonemic crisis. The last two decades have brought advances in the early identification and comprehensive management of UCD, including further understanding of neuroimaging patterns associated with neurocognitive function. Nonetheless, very important questions remain about the potential acute neurotoxic effects of hyperammonemia to better understand how to treat and prevent secondary brain injury. In this review, we describe existing neuromonitoring techniques that have been used in rare metabolic disorders to assess and allow amelioration of ongoing brain injury. Directions of future research should be focused on identifying new diagnostic approaches in the management of metabolic crises to optimize care and reduce long term morbidity and mortality in patients with IEM.     

A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease

The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.     

The Mammalian Phenotype Ontology as a tool for annotating,analyzing and comparing phenotypic information

The Mammalian Phenotype (MP) Ontology enables robust annotation of mammalian phenotypes in the context of mutations, quantitative trait loci and strains that are used as models of human biology and disease. The MP Ontology supports different levels and richness of phenotypic knowledge and flexible annotations to individual genotypes. It continues to develop dynamically via collaborative input from research groups, mutagenesis consortia, and biological domain experts. The MP Ontology is currently used by the Mouse Genome Database and Rat Genome Database to represent phenotypic data.     

Focus: Rare Disease: Patients as Partners in Rare Disease Diagnosis and Research

There is great value in understanding the patient perspective in rare disease diagnosis and research, and in partnering actively with patients and their families throughout the process. Meaningful and respectful interaction between patients and researchers leads to learning on both sides, and ultimately, to better research outcomes. Researchers can help patients understand how research is conducted and what the latest advances and perceived gaps in research are, and patients, who have direct experience living with their health conditions, can impart to researchers what is most important to them. We describe our engagement with patients in the Undiagnosed Diseases Network (UDN) program, as well as the lessons we have learned to date. In the UDN, patients have been instrumental in bringing meaning to the work of clinicians and researchers, building patient communities, making the network aware of unmet patient needs, advocating for additional research funding, and disseminating UDN research findings. Although patient engagement in the UDN has already had a significant positive impact on our work, we continue to strive to involve patients earlier in the process, in the research design itself, and in addressing power dynamics that may arise between clinicians, researchers, and patients.