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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: Nov 16, 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

Automated methods for cell type annotation on scRNA-seq data.

Giovanni Pasquini^1,2, Jesus Eduardo Rojo Arias³, Patrick Schäfer¹

¹Technische Universität Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Center for Regenerative Therapies Dresden (CRTD), Dresden 01307, Germany.

Computational and Structural Biotechnology Journal

|February 22, 2021

Summary

This summary is machine-generated.

Single-cell sequencing reveals cellular details, but manual cell type annotation is difficult. This review covers automated tools and methods for accurate cell type identification in single-cell RNA sequencing data.

Keywords:

Automatic annotation Cell state Cell type scRNA-seq

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Last Updated: Nov 16, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Genomics
Transcriptomics
Bioinformatics

Background:

Single-cell sequencing (scRNA-seq) has revolutionized transcriptomic and genomic research.
Understanding cellular heterogeneity and dynamics is crucial in biological studies.
Manual cell type annotation of scRNA-seq data is labor-intensive and subjective.

Purpose of the Study:

To provide an overview of automated cell type annotation tools for scRNA-seq data.
To discuss various strategies employed in automated cell type identification.
To guide researchers in selecting appropriate methods for their analyses.

Main Methods:

Review of existing computational tools and algorithms.
Categorization of annotation strategies based on underlying principles.
Comparison of approaches including marker gene databases, reference data correlation, and supervised classification.

Main Results:

Several automated tools and distinct strategies exist for cell type annotation.
Approaches vary in their reliance on prior biological knowledge and data integration.
The choice of method depends on the specific research question and dataset.

Conclusions:

Automated cell type annotation significantly enhances the efficiency and objectivity of scRNA-seq data analysis.
The development of diverse computational tools offers flexibility in addressing complex biological questions.
Further advancements in automated annotation are expected to accelerate discoveries in cell biology.