Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.

Bioinformatics advances·2026

Same author

RPI-PLMGNN: Enhancing RNA-Protein Interaction Prediction with the Pretrained Large Language Models and Graph Neural Networks.

ACS synthetic biology·2026

Same author

MPMFMol: Multitask Self-Supervised Pretraining with Multimodal Fine-Tuning for Molecular Property Prediction.

Journal of chemical information and modeling·2026

Same author

Quantum computing applications in drug discovery.

Briefings in bioinformatics·2026

Same author

MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.

Briefings in bioinformatics·2026

Same author

Deep learning the TF regulatory code for gene expression.

Genome research·2026

Search research articles

Related Experiment Video

Updated: Nov 26, 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

Goals and approaches for each processing step for single-cell RNA sequencing data.

Zilong Zhang¹, Feifei Cui², Chunyu Wang³

¹University of Electronic Science and Technology of China.

Briefings in Bioinformatics

|December 14, 2020

Summary

Single-cell RNA sequencing (scRNA-seq) analysis presents challenges due to noisy, high-dimensional data. This review covers essential bioinformatics tools for scRNA-seq data processing, aiding researchers in selecting appropriate methods.

Keywords:

dimension reduction feature selection imputation normalization quality control single-cell RNA sequencing

More Related Videos

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

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Related Experiment Videos

Last Updated: Nov 26, 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

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

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) offers unprecedented cellular-level gene expression insights.
scRNA-seq data is inherently noisy and high-dimensional due to low transcript levels and technical limitations.
A universal standard pipeline for scRNA-seq data analysis is currently lacking.

Purpose of the Study:

To provide an overview of computational challenges in scRNA-seq data analysis.
To review popular bioinformatics tools for key scRNA-seq data processing steps.
To guide researchers in selecting appropriate computational tools for their specific datasets.

Main Methods:

Literature review of existing scRNA-seq data analysis tools.
Categorization of tools based on analysis stages: quality control, normalization, imputation, feature selection, and dimension reduction.
Discussion of the goals and common methodologies for each analysis step.

Main Results:

Identification of critical computational steps in scRNA-seq analysis.
Summary of widely used bioinformatics tools for each processing stage.
Highlighting the importance of understanding computational issues for effective data analysis.

Conclusions:

Effective scRNA-seq data analysis requires careful consideration of bioinformatics tools.
A general understanding of computational methods facilitates the selection of optimal analysis pipelines.
This review serves as a guide for researchers navigating the complexities of scRNA-seq data processing.