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Related Experiment Video

Updated: Nov 28, 2025

Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

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A curated database reveals trends in single-cell transcriptomics.

Valentine Svensson1, Eduardo da Veiga Beltrame1, Lior Pachter1

  • 1Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, CA, 91125, USA.

Database : the Journal of Biological Databases and Curation
|November 28, 2020
PubMed
Summary
This summary is machine-generated.

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A new database offers a searchable catalog of over 1000 single-cell transcriptomics studies. This resource simplifies finding specific datasets and analyzing trends in single-cell RNA sequencing (scRNA-seq) research.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Over 1000 single-cell transcriptomics studies exist, forming a vast resource for biological discovery.
  • Accessing specific datasets from these studies often requires difficult manual literature searches.
  • Existing 'atlas' projects have only partially collated these valuable datasets.

Purpose of the Study:

  • To create a comprehensive, manually curated database of single-cell transcriptomics studies.
  • To facilitate easier discovery and access to published single-cell transcriptomics data.
  • To enable analysis of trends within the field of single-cell transcriptomics.

Main Methods:

  • Assembled a near-exhaustive database of single-cell transcriptomics studies.
  • Manually curated key information including data types, technologies, and biological systems.

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  • Included summarized analysis information from the papers.

    • Main Results:

    • The database provides detailed information on single-cell transcriptomics studies.
    • It allows for efficient searching based on tissue type, species, and other attributes.
    • Analysis of the database reveals trends, such as the relationship between cell numbers analyzed and cell types identified in scRNA-seq studies.

    Conclusions:

    • The curated database significantly enhances accessibility to single-cell transcriptomics data.
    • It serves as a valuable tool for researchers to explore and analyze existing datasets.
    • The database supports the identification of trends and facilitates new biological discoveries.