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Related Concept Videos

DNA Microarrays02:34

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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SpatialDB: a database for spatially resolved transcriptomes.

Zhen Fan1, Runsheng Chen1,2, Xiaowei Chen1

  • 1Center for High Throughput Sequencing, Core Facility for Protein Research, Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.

Nucleic Acids Research
|November 13, 2019
PubMed
Summary
This summary is machine-generated.

SpatialDB is a new database for spatial transcriptomics data, offering a user-friendly platform for biologists to analyze gene expression patterns in tissues. It aids in understanding tissue function and disease pathology.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cell Biology

Background:

  • Spatially resolved transcriptomic techniques are revolutionizing tissue studies by revealing cellular organization.
  • The rapid accumulation of spatial transcriptomic data presents challenges in analysis and accessibility for biologists.
  • Existing data resources lack comprehensive curation for diverse spatial transcriptomic strategies and datasets.

Purpose of the Study:

  • To introduce SpatialDB, the first manually curated database for spatial transcriptomic techniques and datasets.
  • To provide a centralized and accessible resource for researchers studying spatial gene expression.
  • To facilitate the exploration and comparison of spatially resolved transcriptomic data.

Main Methods:

  • Manual curation of spatial transcriptomic datasets and associated techniques.
  • Development of a user-friendly web interface for data visualization and comparison.
  • Integration of tools for identifying spatially variable genes and functional enrichment analysis.

Main Results:

  • SpatialDB currently houses 24 datasets (305 sub-datasets) across 5 species, utilizing 8 distinct techniques.
  • The database offers intuitive visualization and comparative analysis of spatial gene expression data.
  • Spatially variable genes and their functional annotations are provided for deeper biological insights.

Conclusions:

  • SpatialDB serves as a valuable repository for the research community to investigate tissue spatial organization.
  • The database simplifies the exploitation of complex spatial transcriptomic data.
  • SpatialDB has the potential to advance our understanding of cellular microenvironments in health and disease.