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 microarray-based...

RNA Editing

RNA Editing

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...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

You might also read

Related Articles

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

Sort by

Same author

Transcriptomic responses to endurance exercise training in rats.

BMC genomic data·2026

Same author

Systems genetics reveals ITIH5 as a key mediator of adipocyte-Endothelial crosstalk.

Molecular metabolism·2026

Same author

The Rayleigh Quotient and Contrastive Principal Component Analysis II.

bioRxiv : the preprint server for biology·2026

Same author

Hybrid crosses reveal a cell-type-specific landscape of mouse regulatory variation.

bioRxiv : the preprint server for biology·2026

Same author

The impact of package selection and versioning on single-cell RNA-seq analysis.

Cell systems·2026

Same author

Uniform pre-processing of bacterial single-cell RNA-seq.

bioRxiv : the preprint server for biology·2026

Same journal

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

Bioinformatics (Oxford, England)·2026

Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026

Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026

Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026

Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 11, 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

Updating RNA-Seq analyses after re-annotation.

Adam Roberts¹, Lorian Schaeffer, Lior Pachter

¹Department of Computer Science, University of Calofornia Berkeley, Berkeley, CA 94720, USA.

Bioinformatics (Oxford, England)

|May 17, 2013

Summary

This summary is machine-generated.

Estimating RNA-Seq isoform abundances is time-intensive. This study introduces an efficient algorithm to update abundance estimates after transcriptome re-annotation without re-analyzing all data, streamlining analysis.

More Related Videos

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

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

Published on: November 7, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Published on: February 18, 2022

Related Experiment Videos

Last Updated: May 11, 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

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

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

Published on: November 7, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Published on: February 18, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA-Seq data analysis for isoform abundance estimation is computationally intensive.
Current methods require re-analysis from scratch upon transcriptome annotation updates.
This poses challenges for maintaining up-to-date abundance estimates and exploring annotation impacts.

Purpose of the Study:

To develop an efficient algorithm for updating RNA-Seq isoform abundance estimates following transcriptome re-annotation.
To enable rapid synchronization of abundance data with evolving genomic annotations.
To facilitate dynamic exploration of transcriptome changes on downstream analyses.

Main Methods:

A novel, efficient algorithm for updating abundance estimates from RNA-Seq data.
Utilizes a fast partitioning algorithm to identify affected transcripts.
Employs a rapid re-estimation approach for all transcripts.

Main Results:

The developed algorithm efficiently updates abundance estimates without full dataset re-analysis.
Demonstrated synchronization of RNA-Seq estimates with daily RefSeq database updates.
Provides a practical solution for maintaining current abundance databases.

Conclusions:

The novel algorithm significantly reduces the computational burden of updating RNA-Seq abundance estimates.
Enables continuous maintenance of abundance data alongside dynamic transcriptome annotations.
Offers a valuable tool for researchers working with constantly revised genomic data.