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RNA-seq

RNA-seq

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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Updated: Jun 19, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

SpatialQC: automated quality control for spatial transcriptome data.

Guangyao Mao^1,2, Yi Yang¹, Zhuojuan Luo^1,2,3,4

¹Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China.

Bioinformatics (Oxford, England)

|July 25, 2024

Summary

SpatialQC is a new pipeline for quality control (QC) of spatial transcriptomics data. It provides comprehensive reports and clean data, essential for analyzing tissue heterogeneity and biological mechanisms.

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Last Updated: Jun 19, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Spatial transcriptomics offers novel insights into tissue heterogeneity and biological processes.
Quality control (QC) is crucial for spatial transcriptomics data analysis.
Existing tools lack a comprehensive, one-stop solution for spatial transcriptome QC.

Purpose of the Study:

To introduce SpatialQC, a specialized pipeline for comprehensive quality control of spatial transcriptomics data.
To address the need for integrated QC tools in spatial transcriptomics research.

Main Methods:

Development of a one-stop QC pipeline named SpatialQC.
Implementation of features for generating comprehensive QC reports.
Inclusion of functionalities for producing clean, analysis-ready data.

Main Results:

SpatialQC provides a unified approach to spatial transcriptome QC.
The pipeline generates detailed QC reports for data assessment.
SpatialQC facilitates the production of clean data for downstream analyses.
The tool is applicable across various spatial transcriptomic techniques.

Conclusions:

SpatialQC enhances the reliability and efficiency of spatial transcriptomics data analysis.
The pipeline supports deeper understanding of cellular and molecular mechanisms in tissues.
SpatialQC is a valuable resource for researchers utilizing spatial transcriptomics.