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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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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 take up more dye, appearing darker, while the less-compact areas take up less dye and appear lighter. Based on the compaction level, chromatins are classified into two primary forms – euchromatin and heterochromatin.
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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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Updated: Jun 10, 2025

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SpatialRef: a reference of spatial omics with known spot annotation.

Ting Cui1,2,3, Yan-Yu Li1,2,4,5,3,6, Bing-Long Li1,2,3

  • 1The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.

Nucleic Acids Research
|October 17, 2024
PubMed
Summary
This summary is machine-generated.

SpatialRef is a new database of manually curated spatial omics data. It provides high-quality, annotated spot labels across multiple species and tissue types for advanced spatial omics analysis.

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Area of Science:

  • Spatial biology and genomics
  • Bioinformatics and data science
  • Molecular and systems biology

Background:

  • Spatial omics technologies generate detailed molecular maps of tissues, but accurate spot annotation is challenging.
  • Understanding spatial organization is crucial for deciphering tissue function and biological processes.
  • Existing datasets lack comprehensive, manually curated annotations for diverse spatial omics studies.

Purpose of the Study:

  • To develop SpatialRef, a manually curated database of spatial omics data with reliable spot labels.
  • To provide a high-quality resource for researchers analyzing spatial transcriptomics and other spatial omics data.
  • To facilitate downstream analyses by offering standardized annotations across multiple species and technologies.

Main Methods:

  • Aggregated and manually curated >9 million spots from >17 tissue types across Human, Mouse, and Drosophila.
  • Ensured data quality through extensive review and strict quality control protocols.
  • Integrated data from multiple spatial sequencing technologies and >400 spot/domain types.

Main Results:

  • SpatialRef provides comprehensive, high-quality spatial omics data with known spot labels.
  • The database covers diverse species, tissue types, and spatial omics technologies.
  • It supports various downstream analyses including differential gene expression, pathway annotation, and spatial communication.

Conclusions:

  • SpatialRef is a valuable resource for advancing spatial omics research.
  • The database simplifies the annotation process and enhances the reliability of spatial omics analyses.
  • It aids in understanding tissue organization, functional relevance of spatial domains, and potential biological effects.