PuMA: PubMed Gene-Celltype-Relation Atlas

Abstract

Rapid extraction and visualization of cell-specific gene expression is important for automatic celltype annotation, e.g. in single cell analysis. There is an emerging field in which tools such as curated databases or Machine Learning methods are used to support celltype annotation. However, complementing approaches to efficiently incorporate latest knowledge of free-text articles from literature databases, such as PubMed are understudied. This work introduces the PubMed Gene/Celltype-Relation Atlas (PuMA) which provides a local, easy-to-use web-interface to facilitate automatic celltype annotation. It utilizes pretrained large language models in order to extract gene and celltype concepts from Pub-Med and links biomedical ontologies to suggest gene to celltype relations. It includes a search tool for genes and cells, additionally providing an interactive graph visualization for exploring cross-relations. Each result is fully traceable by linking the relevant PubMed articles. The software framework is freely available and enables regular article imports for incremental knowledge updates. GitLab: imigitlab.uni-muenster.de/published/PuMA