Cross-Disorder Analysis of De Novo Mutations in Neuropsychiatric Disorders

The clinical similarity among different neuropsychiatric disorders (NPDs) suggested a shared genetic basis. We catalogued 23,109 coding de novo mutations (DNMs) from 6511 patients with autism spectrum disorder (ASD), 4,293 undiagnosed developmental disorder (UDD), 933 epileptic encephalopathy (EE), 1022 intellectual disability (ID), 1094 schizophrenia (SCZ), and 3391 controls. We evaluated that putative functional DNMs contribute to 38.11%, 34.40%, 33.31%, 10.98% and 6.91% of patients with ID, EE, UDD, ASD and SCZ, respectively. Consistent with phenotype similarity and heterogeneity in different NPDs, they show different degree of genetic association. Cross-disorder analysis of DNMs prioritized 321 candidate genes (FDR < 0.05) and showed that genes shared in more disorders were more likely to exhibited specific expression pattern, functional pathway, genetic convergence, and genetic intolerance.

The Case for Retaining Natural Language Descriptions of Phenotypes in Plant Databases and a Web Application as Proof of Concept

ABSTRACTMotivationFinding similarity across phenotypic descriptions is not straightforward, with previous successes in computation requiring significant expert data curation. Natural language processing of free text phenotype descriptions is often easier to apply than intensive curation. It is therefore critical to understand the extent to which these techniques can be used to organize and analyze biological datasets and enable biological discoveries.ResultsA wide variety of approaches from the natural language processing domain perform as well as similarity metrics over curated annotations for predicting shared phenotypes. These approaches also show promise both for helping curators organize and work through large datasets as well as for enabling researchers to explore relationships among available phenotype descriptions. Here we generate networks of phenotype similarity and share a web application for querying a dataset of associated plant genes using these text mining approaches. Example situations and species for which application of these techniques is most useful are discussed.AvailabilityThe dataset used in this work is available at https://git.io/JTutQ. The code for the analysis performed here is available at https://git.io/JTutN and https://git.io/JTuqv. The code for the web application discussed here is available at https://git.io/Jtv9J, and the application itself is available at https://quoats.dill-picl.org/.

DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model

MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.

Automated methods enable direct computation on phenotypic descriptions for novel candidate gene prediction

1AbstractNatural language descriptions of plant phenotypes are a rich source of information for genetics and genomics research. We computationally translated descriptions of plant phenotypes into structured representations that can be analyzed to identify biologically meaningful associations. These repre-sentations include the EQ (Entity-Quality) formalism, which uses terms from biological ontologies to represent phenotypes in a standardized, semantically-rich format, as well as numerical vector representations generated using Natural Language Processing (NLP) methods (such as the bag-of-words approach and document embedding). We compared resulting phenotype similarity measures to those derived from manually curated data to determine the performance of each method. Computationally derived EQ and vector representations were comparably successful in recapitulating biological truth to representations created through manual EQ statement curation. Moreover, NLP methods for generating vector representations of phenotypes are scalable to large quantities of text because they require no human input. These results indicate that it is now possible to computationally and automatically produce and populate large-scale information resources that enable researchers to query phenotypic descriptions directly.

Linking Phenotypes and Genotypes with Matrix Factorizations (Preprint)

BACKGROUND Background: Phenotype is defined as the composite of an organism’s observable characteristics or traits, such as human’s eye colors, behaviors and disease symptoms. Genotype is the genetic makeup of a cell, an organism, or an individual usually with reference to a specific characteristic under consideration. Thus phenotype can be regarded as the macroscopic description of an organism while genotype is its microscopic expression. OBJECTIVE Objective: Identification of phenotype-genotype associations is the primary step explaining the pathogenesis of human complex diseases. It is also of key importance for the development of Genomic medicine, sometimes also known as personalized medicine, which is a way to customize medical care to an individual body’s unique genetic makeup. METHODS Methods: In this paper, we propose a unified computational framework, called PheGe , to bridge phenotypes and genotypes. PheGe utilizes phenotype similarity network, genotype similarity network and known phenotype-genotype associations to explore the potential associations among other unlinked phenotypes and genotypes. RESULTS Results: As by-products, PheGe can also discover the phenotype and genotype groups, such that the phenotypes or genotypes within the same group are highly correlated with each other. We also validate the effectiveness of PheGe on a real-world data set, where we discover some interesting phenotype-genotype associations and phenotype/genotype groups. CONCLUSIONS Conclusions: Our method can reveal potential phenotype clusters and genotype clusters and their unknown associations through a variety of phenotype similarities, genotype similarities, as well as known phenotype-genotype associations.

DeepPVP: phenotype-based prioritization of causative variants using deep learning

BackgroundPrioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient’s phenotype.ResultsWe have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp.ConclusionsDeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

Best Paper Selection

Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data 2016;3:160026 https://www.nature.com/articles/sdata201626 Bauer CR, Ganslandt T, Baum B, Christoph J, Engel I, Lobe M, Mate S, Staubert S, Drepper J, Prokosch HU, Winter A, Sax U. Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data. Methods Inf Med 2016;55(2):125-35 https://methods.schattauer.de/en/contents/archivestandard/issue/2324/manuscript/25160.html Greene D, NIHR BioResource, Richardson S, Turro E. Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases. Am J Hum Genet 2016;98(3):490-9 https://linkinghub.elsevier.com/retrieve/pii/S0002-9297(16)00014-8 Sarntivijai S, Vasant D, Jupp S, Saunders G, Bento AP, Gonzalez D, Betts J, Hasan S, Koscielny G, Dunham I, Parkinson H, Malone J. Linking rare and common disease: mapping clinical diseasephenotypes to ontologies in therapeutic target validation. J Biomed Semantics 2016;7-8 https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0051-7