Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

RNA-seq03:21

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 microarray-based...
Ribosome Profiling02:24

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 helps...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

You might also read

Related Articles

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

Sort by
Same author

Identification of the gene cluster for the dithiolopyrrolone antibiotic holomycin in Streptomyces clavuligerus.

Proceedings of the National Academy of Sciences of the United States of America·2010
Same author

Safety evaluation of tea (Camellia sinensis (L.) O. Kuntze) flower extract: assessment of mutagenicity, and acute and subchronic toxicity in rats.

Journal of ethnopharmacology·2010
Same author

Influences of soil properties and leaching on nickel toxicity to barley root elongation.

Ecotoxicology and environmental safety·2010
Same author

Effects of CO2 insufflation on cerebrum during endoscopic thyroidectomy in a porcine model.

Surgical endoscopy·2010
Same author

Plants' use of different nitrogen forms in response to crude oil contamination.

Environmental pollution (Barking, Essex : 1987)·2010
Same author

Overexpression of p35 in Min6 pancreatic beta cells induces a stressed neuron-like apoptosis.

Journal of the neurological sciences·2010
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 30, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.

Bo Li1, Colin N Dewey

  • 1Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.

BMC Bioinformatics
|August 6, 2011
PubMed
Summary
This summary is machine-generated.

RSEM quantifies gene and isoform abundances from RNA-Seq data without a reference genome. This tool provides guidance for cost-effective RNA-Seq experiment design, improving transcript quantification accuracy.

More Related Videos

AQRNA-seq for Quantifying Small RNAs
05:12

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Related Experiment Videos

Last Updated: May 30, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

AQRNA-seq for Quantifying Small RNAs
05:12

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA-Seq is a powerful tool for measuring transcript abundance.
  • Quantifying transcripts from RNA-Seq data is challenging due to reads mapping to multiple genes/isoforms.
  • Accurate quantification is difficult for species lacking sequenced genomes, especially with de novo transcriptome assemblies.

Purpose of the Study:

  • To introduce RSEM, a user-friendly software package for gene and isoform abundance quantification from RNA-Seq data.
  • To enable accurate transcript quantification for species without reference genomes, particularly when using de novo transcriptome assemblies.
  • To provide guidance on designing cost-efficient RNA-Seq experiments.

Main Methods:

  • RSEM quantifies gene and isoform abundances from single-end or paired-end RNA-Seq data.
  • The software does not require a reference genome, making it suitable for de novo transcriptome assemblies.
  • RSEM outputs abundance estimates, credibility intervals, and visualization files, and can simulate RNA-Seq data.

Main Results:

  • RSEM demonstrates superior or comparable performance to genome-dependent methods on simulated and real data.
  • Accurate gene-level abundance estimates are best achieved with numerous short, single-end reads.
  • Paired-end reads can improve isoform frequency estimates, depending on splice form complexity.

Conclusions:

  • RSEM is an accurate and user-friendly tool for RNA-Seq transcript abundance quantification.
  • Its ability to function without a reference genome makes it ideal for de novo transcriptome assembly applications.
  • RSEM offers valuable insights for designing cost-effective RNA-Seq experiments.