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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.

Oscar E Ospina¹, Roberto Manjarres-Betancur², Guillermo Gonzalez-Calderon²

¹Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.

Biorxiv : the Preprint Server for Biology

|July 15, 2024

Summary

This summary is machine-generated.

Spatial transcriptomics (ST) analysis is simplified with spatialGE, a web app for researchers without programming skills. This tool democratizes the study of spatial gene expression in tissues, offering valuable insights into diseases.

Keywords:

Spatial biology bioinformatics data visualization spatial transcriptomics statistical analysis tumor microenvironment

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) offers powerful insights into tissue biology and disease mechanisms.
High-level data analysis and programming skills currently limit the widespread adoption of ST.
A user-friendly solution is needed to make ST data analysis accessible to a broader scientific audience.

Purpose of the Study:

To introduce spatialGE, a web application designed to simplify and democratize spatial transcriptomics data analysis.
To provide a user-friendly interface for various ST analysis pipelines, including quality control, normalization, domain detection, and phenotyping.
To enable comparative analysis across multiple samples and support diverse ST technologies.

Main Methods:

Development of a web application, spatialGE, featuring an intuitive graphical user interface.
Integration of comprehensive ST data analysis workflows: quality control, normalization, domain detection, phenotyping, and spatial analyses.
Implementation of comparative analysis capabilities and support for various ST data formats.

Main Results:

spatialGE successfully guides users through complex ST data analysis without requiring programming expertise.
The application facilitates the identification of spatial gene expression patterns and enrichments.
Demonstrated utility in analyzing the tumor microenvironment in melanoma brain metastasis and Merkel cell carcinoma.

Conclusions:

spatialGE significantly lowers the barrier to entry for spatial transcriptomics data analysis.
The tool empowers researchers to gain deeper insights into tissue architecture and disease mechanisms.
spatialGE democratizes ST analysis, fostering wider scientific community engagement and discovery.