BackgroundOne of the major goals of genomic medicine is the identification of causal genomic variants in a patient and their relation to the observed clinical phenotypes. Prioritizing the genomic variants by considering only the genotype information usually identifies a few hundred potential variants. Narrowing it down further to find the causal disease genes and relating them to the observed clinical phenotypes remains a significant challenge, especially for rare diseases.MethodsWe propose a phenotype-driven gene prioritization approach using heterogeneous networks in the context of rare diseases. Towards this, we first built a heterogeneous network consisting of ontological associations as well as curated associations involving genes, diseases, phenotypes and pathways from multiple sources. Motivated by the recent progress in spectral graph convolutions, we developed a graph convolution based technique to infer new phenotype-gene associations from this initial set of associations. We included these inferred associations in the initial network and termed this integrated network HANRD (Heterogeneous Association Network for Rare Diseases). We validated this approach on 230 recently published rare disease clinical cases using the case phenotypes as input.ResultsWhen HANRD was queried with the case phenotypes as input, the causal genes were captured within Top-50 for more than 31% of the cases and within Top-200 for more than 56% of the cases. The results showed improved performance when compared to other state-of-the-art tools.ConclusionsIn this study, we showed that the heterogeneous network HANRD, consisting of curated, ontological and inferred associations, helped improve causal gene identification in rare diseases. HANRD allows future enhancements by supporting incorporation of new entity types and additional information sources.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0372-8) contains supplementary material, which is available to authorized users.
Whole‐genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state‐of‐the‐art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.
TPX is a web-based PubMed search enhancement tool that enables faster article searching using analysis and exploration features. These features include identification of relevant biomedical concepts from search results with linkouts to source databases, concept based article categorization, concept assisted search and filtering, query refinement. A distinguishing feature here is the ability to add user-defined concept names and/or concept types for named entity recognition. The tool allows contextual exploration of knowledge sources by providing concept association maps derived from the MEDLINE repository. It also has a full-text search mode that can be configured on request to access local text repositories, incorporating entity co-occurrence search at sentence/paragraph levels. Local text files can also be analyzed on-the-fly.Availability http://tpx.atc.tcs.com/
Introduction Phenotype-driven rare disease gene prioritization relies on high quality curated resources containing disease, gene and phenotype annotations. However, the effectiveness of gene prioritization tools is constrained by the incomplete coverage of rare disease, phenotype and gene annotations in such curated resources. Methods We extracted rare disease correlation pairs involving diseases, phenotypes and genes from MEDLINE abstracts and used the information propagation algorithm GCAS to build an association network. We built a tool called PRIORI-T for rare disease gene prioritization that uses this network for phenotype-driven rare disease gene prioritization. The quality of disease-gene associations in PRIORI-T was compared with resources such as DisGeNET and Open Targets in the context of rare diseases. The gene prioritization performance of PRI-ORI-T was evaluated using phenotype descriptions of 230 real-world rare disease clinical cases collated from recent publications, as well as compared to other gene prioritization tools such as HANRD and Orphamizer. Results PRIORI-T contains qualitatively better associations than DisGeNET and Open Targets. Furthermore, the causal genes were captured within Top-50 for more than 40% of the realworld clinical cases and within Top-300 for more than 72% of the cases when PRIORI-T was used for gene prioritization. It outperformed other gene prioritization tools such as HANRD and Orphamizer that primarily rely on curated resources. Conclusions PRIORI-T exhibited improved gene prioritization performance without requiring high quality curated data. Thus, it holds great promise in phenotype-driven gene prioritization for rare disease studies.
<div><b>Background</b>: The COVID-19 pandemic has led to a massive and collective pursuit by the research community to find effective diagnostics, drugs and vaccines The large and growing body of literature present in MEDLINE and other online resources including various self-archive sites are invaluable for these efforts. MEDLINE has more than 30 million abstracts and an additional corpus related to COVID-19, SARS and MERS has more than 40,000 literature articles, and these numbers are growing. Automated extraction of useful information from literature and automated generation of novel insights is crucial for accelerated discovery of drug/vaccine targets and re-purposing drug candidates.</div><div><br></div><div><b>Methods</b>: We applied text-mining on MEDLINE abstracts and the CORD-19 corpus to extract a rich set of pair-wise correlations between various biomedical entities. We built a comprehensive pair-wise entity association network involving 15 different entity types using both text-mined associations as well as novel associations obtained using link prediction. The resulting network, which we call CoNetz, also contains a specialized COVID-19 subnetwork that provides a network view of COVID-19 related literature. Additionally, we developed a set of network exploration utilities and user-friendly network visualization utilities using NetworkX and PyVis.</div><div><br></div><div><b>Results</b>: CoNetz consisted of pair-wise associations involving 174,000 entities covering 15 different entity types. The specialized COVID-19 subnetwork consisted of 7.8 million pair-wise associations involving 43,000 entities. The network captured several of the well-known COVID-19 drug re-purposing candidates and also predicted novel candidates including ingavirin, laninamivir, nevirapine, paritaprevir, pranlukast and peficitinib.</div><div><br></div><div><b>Conclusions</b>: Our automated text and network-mining approach builds an up-to-date and comprehensive knowledge network from literature for COVID-19 studies. The wide range of entity types captured in CoNetz provides a rich neighborhood context around the relations of interest. The approach avoids multiple drawbacks associated with manual curation including cost and effort involved, lack of up-to-date information and limited coverage. Amongst the novel repurposing drugs predicted, laninamivir and paritaprevir are possible COVID-19 anti-viral drugs while pranlukast was postulated to be a candidate for managing severe respiratory symptoms in COVID-19 patients. CoNetz is available for download and use from https://web.rniapps.net/tcn/tcn.tar.gz</div>
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