PuMA: PubMed Gene-/Cell type-Relation Atlas

Rapid extraction and visualization of cell-specific gene expression is important for automatic cell type 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 cell type 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/Cell type-Relation Atlas (PuMA) which provides a local, easy-to-use web-interface to facilitate automatic cell type annotation. It utilizes a pretrained large language model in order to extract gene and cell type concepts from PubMed and links biomedical ontologies to suggest gene to cell type 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. This work enables researchers to analyse and automatize cell type annotation based on PubMed articles. It complements manual curated marker gene databases and enables interactive visualizations. The software framework is freely available and enables regular article imports for incremental knowledge updates.GitLab: https://imigitlab.uni-muenster.de/published/PuMA/ Website: https://puma.uni-muenster.de