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Related Concept Videos

RNA-seq03:21

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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Application of Open-Source Digital Resources for 3D Visualization of Clustered Transcriptomic Data.

Hunter F Strickland1,2, Andrew Shen3, Anna-Lisa Paul1,2,4

  • 1Plant Molecular and Cellular Biology Program, University of Florida, Gainesville, Florida, USA.

Physiologia Plantarum
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Summary

New open-source tools, HTMLview and Blenderview, simplify the visualization of high-dimensional transcriptomic data. These resources enable researchers to create engaging 3D models and animations from dimensionality reduction outputs, enhancing data exploration and sharing.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Visualization

Background:

  • High-throughput transcriptome sequencing generates large datasets.
  • Dimensionality reduction methods (e.g., t-SNE, UMAP) reduce data complexity for analysis and visualization.
  • Current visualization methods often require significant coding expertise, limiting accessibility.

Purpose of the Study:

  • To develop user-friendly, open-source digital resources for visualizing high-dimensional transcriptomic data.
  • To address the challenge of creating engaging and visually attractive data representations for researchers without extensive coding experience.

Main Methods:

  • Developed two digital data visualization resources: HTMLview and Blenderview.
  • HTMLview uses an HTML template for interactive 3D models.
  • Blenderview utilizes Blender software for high-quality animated models and videos.
  • Both resources leverage publicly available scripts from JsDelivr and GitHub.

Main Results:

  • Successfully generated interactive 3D models (HTMLview) and animated high-quality models (Blenderview) from transcriptomic data processed via dimensionality reduction.
  • Tested the visualization resources with processed transcriptomic datasets.
  • Demonstrated the utility of these tools in enhancing data examination and sharing.

Conclusions:

  • The developed resources provide accessible pathways for researchers to visualize and interpret complex transcriptomic data.
  • HTMLview and Blenderview promote the use of open-source technologies for advanced data visualization in bioinformatics.
  • These tools empower researchers to better examine, share, and communicate their findings from high-dimensional datasets.