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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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Related Experiment Video

Updated: Nov 10, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

Critical downstream analysis steps for single-cell RNA sequencing data.

Zilong Zhang¹, Feifei Cui², Chen Lin³

¹University of Electronic Science and Technology of China.

Briefings in Bioinformatics

|April 6, 2021

Summary

This summary is machine-generated.

This review covers key single-cell RNA sequencing (scRNA-seq) analysis methods, including clustering and cell-type annotation. It offers guidance for selecting appropriate tools to interpret complex scRNA-seq data effectively.

Keywords:

cell type annotation clustering integrating datasets single-cell RNA sequencing trajectory inference

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Last Updated: Nov 10, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

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Published on: June 8, 2020

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Published on: October 3, 2025

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cellular heterogeneity.
Analysis of scRNA-seq data presents challenges due to inherent noise and large dimensionality.
Numerous computational tools have been developed to address these challenges.

Purpose of the Study:

To review and summarize widely adopted computational methods for scRNA-seq data analysis.
To discuss the advantages and limitations of various analytical approaches.
To provide recommendations for selecting appropriate methods based on specific research needs.

Main Methods:

Clustering algorithms for cell population identification.
Trajectory inference techniques for understanding cellular differentiation.
Cell-type annotation strategies using reference datasets.
Methods for integrating multiple scRNA-seq datasets.

Main Results:

Comprehensive overview of popular scRNA-seq analysis tools.
Comparative analysis of method performance, strengths, and weaknesses.
Guidance on method selection for diverse biological applications.

Conclusions:

Effective utilization of scRNA-seq data relies on careful selection of appropriate analytical methods.
Understanding the trade-offs between different tools is crucial for robust biological interpretation.
This review serves as a resource for researchers and developers in the scRNA-seq field.