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

9.4K
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...
9.4K
Ribosome Profiling02:24

Ribosome Profiling

3.2K
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...
3.2K
RNA Editing02:23

RNA Editing

8.3K
RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
8.3K

You might also read

Related Articles

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

Sort by
Same author

Accurate detection of tumor clonality and ongoing expansion mode from genomic data.

bioRxiv : the preprint server for biology·2026
Same author

Reduced platelet formation associated with serine metabolic dysregulation in integrin αIIbβ3-deficient megakaryocytes.

Blood·2026
Same author

Past viral infections can shape inter-individual variability in anti-viral TLR responses.

bioRxiv : the preprint server for biology·2026
Same author

Single-nucleus multiome sequencing identifies candidate regulators of mouse gastric epithelial homeostasis.

bioRxiv : the preprint server for biology·2026
Same author

Biallelic variants in RNU2-2 cause the most prevalent known recessive neurodevelopmental disorder.

Nature genetics·2026
Same author

Thyroid hormones induce an acute platelet release mechanism via integrin αVβ3.

Haematologica·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

42.7K

Flexible analysis of RNA-seq data using mixed effects models.

Ernest Turro1, William J Astle, Simon Tavaré

  • 1Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK, Department of Haematology, University of Cambridge, NHS Blood and Transplant, Long Road, Cambridge CB2 0PT, UK and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal QC H3A 1A2, Canada.

Bioinformatics (Oxford, England)
|November 28, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method and a novel collapsing algorithm to improve RNA-seq expression analysis by accounting for read mapping ambiguities. These methods enhance the power and specificity of detecting gene expression patterns and improve transcript-level precision.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.9K

Related Experiment Videos

Last Updated: May 5, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

42.7K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.9K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • RNA sequencing (RNA-seq) analysis often faces challenges with short read mapping ambiguity.
  • Existing methods may not fully account for uncertainty in expression level estimation, limiting downstream analysis.
  • Transcript-level uncertainty can hinder meaningful comparisons between experimental groups.

Purpose of the Study:

  • To develop novel statistical approaches for more accurate and versatile RNA-seq expression analysis.
  • To address the limitations posed by read mapping ambiguities and transcript-level uncertainty.
  • To improve the power, specificity, and scope of differential gene expression detection.

Main Methods:

  • A Bayesian model selection approach using random effects to handle read mapping ambiguities.
  • A novel collapsing algorithm for grouping transcripts into inferential units based on posterior correlations.
  • Implementation within the open-source MMSEQ package (mmdiff and mmcollapse).

Main Results:

  • The Bayesian method enables identification of diverse gene expression patterns beyond simple differential expression, including imprinting and regulatory divergence.
  • The collapsing algorithm improves the precision of aggregate expression estimates, especially when uncertainty is high.
  • The developed methods offer enhanced power and specificity in RNA-seq data analysis.

Conclusions:

  • The proposed Bayesian and collapsing methods offer significant improvements for RNA-seq expression analysis.
  • These approaches effectively manage read mapping ambiguities and transcript-level uncertainty.
  • The MMSEQ package provides a versatile tool for advanced gene expression studies.