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DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species.

Yanhui Hu1,2, Sudhir Gopal Tattikota1, Yifang Liu1,2

  • 1Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.

Computational and Structural Biotechnology Journal
|May 17, 2021
PubMed
Summary

A new database, DRscDB, enables cross-species searches for single-cell RNA sequencing (scRNA-seq) gene expression patterns. This tool aids researchers in comparing cell types and gene functions across different organisms, including Drosophila.

Keywords:
Cross-species analysisData miningModel organismssingle-cell RNA-seq

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Area of Science:

  • Genomics
  • Bioinformatics
  • Developmental Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research, generating vast datasets across various species.
  • Existing databases offer limited cross-species comparative analysis for scRNA-seq data, hindering orthologous gene exploration.
  • Discovering cell type-specific expression patterns across species is crucial for understanding conserved biological mechanisms.

Purpose of the Study:

  • To develop DRscDB, a novel search database for exploring single-cell RNA sequencing (scRNA-seq) data.
  • To provide a comprehensive repository of scRNA-seq datasets for Drosophila and related model organisms (zebrafish, mouse, human).
  • To facilitate the identification of orthologous genes and their cell type-specific expression patterns across species.

Main Methods:

  • Manual curation of published Drosophila scRNA-seq studies and analogous vertebrate tissues.
  • Integration of literature-derived marker genes to preserve original analytical findings.
  • Development of a web-based user interface for data mining and cross-species analysis.

Main Results:

  • DRscDB offers a curated collection of scRNA-seq datasets for Drosophila and other model organisms.
  • The database includes literature-derived marker genes, ensuring data integrity.
  • Users can perform gene expression mining and cell cluster enrichment analyses within and across species.

Conclusions:

  • DRscDB addresses the need for cross-species comparative analysis of scRNA-seq data.
  • The database empowers researchers to investigate conserved gene functions and cell types.
  • DRscDB facilitates a deeper understanding of evolutionary developmental biology through comparative genomics.