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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 12, 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

How to Get Started with Single Cell RNA Sequencing Data Analysis.

Michael S Balzer^1,2,3, Ziyuan Ma^1,2,3, Jianfu Zhou^1,2,3

¹Renal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.

Journal of the American Society of Nephrology : JASN

|March 16, 2021

Summary

This summary is machine-generated.

Single cell analysis methods are rapidly advancing, generating large datasets. This review simplifies analysis pipelines and tools for researchers new to single-cell data, addressing key challenges and common readouts.

Keywords:

analysis kidney single cell RNA-sequencing transcriptomics

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Last Updated: Nov 12, 2025

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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

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Single-cell technologies allow monitoring of gene expression, protein levels, and genetic/epigenetic changes in thousands of individual cells.
The cost reduction and improved measurement accuracy of these techniques have led to a rapid increase in dataset size.
Analyzing the vast information from single-cell experiments presents a significant bottleneck in the field.

Purpose of the Study:

To provide a simplified overview of current single-cell analysis pipelines.
To guide researchers new to single-cell analysis through common challenges and analytical tools.
To help researchers understand how single-cell data is typically presented in scientific literature.

Main Methods:

Review of current single-cell data analysis pipelines.
Identification and description of commonly used analytical tools.
Explanation of typical data readouts and their interpretation.

Main Results:

A simplified overview of single-cell analysis workflows is presented.
Key challenges in single-cell data analysis are highlighted.
Commonly employed computational tools and methods are discussed.

Conclusions:

This review aims to demystify single-cell data analysis for novice researchers.
Understanding analysis pipelines is crucial for effectively interpreting single-cell experimental results.
The review facilitates comprehension of published single-cell data readouts.