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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions that take up more dye are called heterochromatin. Heterochromatin is further classified into two forms – constitutive heterochromatin and facultative heterochromatin.
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Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
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Updated: Sep 13, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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PuMA: PubMed gene/cell type-relation Atlas.

Lucas Bickmann1, Sarah Sandmann1, Carolin Walter2

  • 1Institute of Medical Data Science, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.

BMC Bioinformatics
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the PubMed Gene/Cell type-Relation Atlas (PuMA), a tool for literature-driven cell type annotation. PuMA extracts gene and cell type information from PubMed, aiding researchers in automated cell type identification.

Keywords:
Cell type annotationDatabaseMachine learningNatural Language ProcessingPubMed

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Automated cell type annotation is crucial for single-cell analysis.
  • Existing tools rely on curated databases or machine learning, but incorporating literature knowledge is underexplored.

Purpose of the Study:

  • To develop a tool for literature-driven cell type annotation using PubMed.
  • To facilitate the extraction and visualization of gene-cell type relationships from scientific literature.

Main Methods:

  • Developed the PubMed Gene/Cell type-Relation Atlas (PuMA) with a web interface.
  • Utilized a machine learning-based named entity recognition model to extract gene and cell type concepts from PubMed.
  • Linked biomedical ontologies and developed a ranking score for gene-cell type relations.

Main Results:

  • PuMA provides a searchable interface with interactive graph visualizations for exploring gene-cell type relations.
  • Results are traceable to specific PubMed articles.
  • PuMA demonstrated competitive performance against a manually curated database across mouse and human datasets.

Conclusions:

  • PuMA enables automated cell type annotation using PubMed articles, complementing existing databases.
  • The software framework supports incremental knowledge updates through regular article imports.
  • PuMA offers researchers a valuable tool for analyzing and automating cell type annotation based on the latest scientific literature.