Biology subjects

Gaskell, A.

Publications and source records attributed to Gaskell, A..

3 recordsLinked to original sources

bioRxiv2024

The effects of biological knowledge graph topology on embedding-based link prediction

Due to the limited information available about rare diseases and their causal variants, knowledge graphs are often used to augment our understanding and make inferences about new gene-disease connections. Knowledge graph embedding methods have been successfully applied to various biomedical link prediction tasks but have yet to be adopted for rare disease variant prioritization. Here, we explore the effect of knowledge graph topology on knowledge graph embedding link prediction performance and challenge the assumption that massively aggregating knowledge graphs is beneficial in deciphering rare disease cases and improving prediction outcomes. We find that using a filtered version of the Monarch knowledge graph with only 11% of the original size results in notably improved model predictive performance. Additionally, these findings suggest that successful KG optimization depends on selecting high-quality information rather than simply maximizing the amount of data included.

bioinformatics↗

bioRxiv2024

Rapid, Reliable, and Interpretable CNV Curation Visualizations for Diagnostic Settings with SeeNV

Copy number variants (CNVs), structural alterations in the genome involving duplication or deletion of DNA segments, are implicated in various health conditions. Despite their clinical significance, accurate identification and interpretation of CNVs remain challenging, especially in the context of whole exome sequencing (WES), which is commonly used in clinical diagnostic laboratories. While WES offers economic advantages over whole genome sequencing (WGS), it struggles with CNV detection due to technical noise introduced by laboratory and analytic processes. Manual curation of CNV calls generated by these tools is labor-intensive and error-prone. To address this, we introduce SeeNV, a command-line tool designed to aid manual curation of CNVs at scale. SeeNV is one solution to these issues developed in collaboration with and used by the Precision Diagnostics Laboratory at Childrens Hospital Colorado. SeeNV generates static infographics for each CNV, incorporating sample and cohort sequencing coverage statistics, CNV population frequency, and more, facilitating rapid and precise assessment. Using CNVs calls identified in publicly available WES and WGS samples, we show users can rapidly and reliably curate CNV calls, needing only 4.3 seconds to curate a call, achieving 0.93 precision and 0.72 recall. SeeNV is freely available for download on GitHub: https://github.com/MSBradshaw/SeeNV.

genomics↗

bioRxiv2023

Hypothesis Generation For Rare and Undiagnosed Diseases Through Clustering and Classifying Time-Versioned Biological Ontologies

Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using advanced genome sequencing platforms to discover variants, if there is no connection between the variants found in the patients genome and their phe-notypes in the literature, then the patient will remain undiagnosed. When a direct variant-phenotype connection is not known, putting a patients information in the larger context of phenotype relation-ships and protein-protein-interactions may provide an opportunity to find an indirect explanation. Databases such as STRING contain millions of protein-protein-interactions and HPO contains the relations of thousands of phenotypes. By integrating these networks and clustering the entities within we can potentially discover latent gene-to-phenotype connections. The historical records for STRING and HPO provide a unique opportunity to create a network time series for evaluating the cluster sig-nificance. Most excitingly, working with Childrens Hospital Colorado we provide promising hy-potheses about latent gene-to-phenotype connections for 38 patients with undiagnosed diseases. We also provide potential answers for 14 patients listed on MyGene2. Clusters our tool finds significant harbor 2.35 to 8.72 times as many gene-to-phenotypes edges inferred from known drug interactions than clusters find to be insignificant. Our tool, BOCC, is available as a web app and command line tool.

bioinformatics↗