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

The Topological Regulatory Logic of noncoding RNA-mediated gene expression.

Genome research·2026

Same author

BINSEQ: A family of high-performance binary formats for nucleotide sequences.

PLoS computational biology·2026

Same author

Author Correction: MaizeCODE reveals bi-directionally expressed enhancers that harbor molecular signatures of maize domestication.

Nature communications·2025

Same author

Virtual Cell Challenge: Toward a Turing test for the virtual cell.

Cell·2025

Same author

MaizeCODE reveals bi-directionally expressed enhancers that harbor molecular signatures of maize domestication.

Nature communications·2024

Same author

A mucus production programme promotes classical pancreatic ductal adenocarcinoma.

Gut·2024

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 22, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Optimizing RNA-Seq Mapping with STAR.

Alexander Dobin¹, Thomas R Gingeras²

¹Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY, 11746, USA. dobin@cshl.edu.

Methods in Molecular Biology (Clifton, N.J.)

|April 27, 2016

Summary

This summary is machine-generated.

High-throughput sequencing generates vast RNA data. This study details optimizing the STAR aligner for accurate and fast mapping of RNA sequencing reads to reference genomes.

Keywords:

RNA-seq Reads mapping STAR Sequence alignment Spliced alignment Transcriptome

More Related Videos

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Related Experiment Videos

Last Updated: Mar 22, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

High-throughput sequencing enables comprehensive transcriptome analysis.
Accurate mapping of RNA sequencing reads to a reference genome is critical for downstream analysis.
The STAR aligner is a widely used tool for this purpose.

Purpose of the Study:

To describe key parameters and options for the STAR aligner.
To provide best practices for maximizing mapping accuracy and speed in RNA sequencing data analysis.

Main Methods:

Detailed description of STAR aligner's important user-defined parameters.
Explanation of algorithmic approaches for spliced alignment.
Guidelines for parameter tuning based on experimental needs.

Main Results:

Identification of critical STAR parameters influencing mapping performance.
Demonstration of how parameter choices affect accuracy and speed.
Best practices for optimizing STAR for diverse RNA-seq datasets.

Conclusions:

Proper configuration of STAR parameters is essential for reliable RNA-seq analysis.
Optimized STAR usage enhances the efficiency and accuracy of transcriptome profiling.
This guide facilitates effective utilization of STAR for researchers in genomics and bioinformatics.