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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Harnessing computational spatial omics to explore the spatial biology intricacies.

Zhiyuan Yuan1, Jianhua Yao2

  • 1Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

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Summary

Spatially resolved transcriptomics (SRT) generates complex data. This review explores gene and tissue spatial pattern recognition (GSPR/TSPR) methods to analyze SRT data and understand tissue architecture.

Keywords:
Spatially resolved transcriptomics

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Spatially resolved transcriptomics (SRT) offers unprecedented insights into tissue architecture.
  • The large, diverse datasets from SRT require advanced computational tools for pattern discovery.
  • Gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR) are key methodologies.

Purpose of the Study:

  • To provide a comprehensive review of SRT data modalities and resources.
  • To address challenges in developing GSPR and TSPR methodologies using heterogeneous data.
  • To propose optimal workflows and explore future directions in SRT data analysis.

Main Methods:

  • Review of existing literature on SRT data modalities and computational strategies.
  • Analysis of gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR) approaches.
  • Discussion of data heterogeneity challenges and proposed optimal workflows.

Main Results:

  • SRT data analysis necessitates sophisticated computational strategies like GSPR and TSPR.
  • Heterogeneous SRT data presents challenges for developing robust GSPR and TSPR methods.
  • Interrelationships between GSPR and TSPR are examined, with future perspectives outlined.

Conclusions:

  • Effective analysis of SRT data relies on advanced computational methods like GSPR and TSPR.
  • Standardized workflows are needed to handle heterogeneous SRT data for biological insights.
  • The field of SRT analysis is rapidly evolving with promising future directions.