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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: Jul 15, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Methods for cell-type annotation on scRNA-seq data: A recent overview.

Konstantinos Lazaros¹, Panagiotis Vlamos¹, Aristidis G Vrahatis¹

¹Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.

Journal of Bioinformatics and Computational Biology

|September 24, 2023

Summary

This summary is machine-generated.

Single-cell gene expression analysis faces challenges in cell type annotation. This review highlights recent advancements and predicts graph neural networks will drive future cell annotation tools.

Keywords:

Single-cell RNA-seq cell-type annotation marker genes reference data

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Last Updated: Jul 15, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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Published on: March 29, 2024

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell technologies generate vast datasets, offering insights into complex diseases.
Cell type annotation in single-cell gene expression data remains a significant computational challenge.
The field has seen rapid evolution in data, resources, and annotation tools.

Purpose of the Study:

To review notable cell type annotation techniques developed in the last four years.
To provide an overview of current trends and advanced methods in single-cell data taxonomy.
To identify the need for biologically informed annotation tools.

Main Methods:

Comprehensive literature review of cell type annotation tools and methodologies.
Analysis of trends in single-cell data analysis and annotation strategies.
Evaluation of emerging computational approaches, including graph neural networks.

Main Results:

Identification of key advancements in cell type annotation over the past four years.
Showcasing of state-of-the-art methods for single-cell data taxonomy.
Highlighting the increasing demand for annotation tools with biological context integration.

Conclusions:

Existing cell type annotation tools require further development, particularly in incorporating biological context.
Graph neural network approaches are emerging as a promising trend for future cell annotation.
Continued innovation in single-cell analysis tools is crucial for advancing disease research.