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...

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 helps...

Leaky Scanning

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...

You might also read

Related Articles

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

Sort by

Same author

Genome-Wide Meta-Analysis for High Myopia Provides Insights into Disease Mechanisms and Reveals a Causal Link to Primary Open-Angle Glaucoma.

Ophthalmology science·2026

Same author

Clinical development of molecular residual disease (MRD) and multi-cancer early detection (MCED) using liquid biopsy multiomics with artificial intelligence (AI).

International journal of clinical oncology·2026

Same author

HLA-DQB103:01 and HLA-DQA105:05 as key genetic determinants of infliximab response and immunogenicity in Japanese patients with inflammatory bowel disease.

Journal of gastroenterology·2026

Same author

Orofacial granulomatosis in pediatric Crohn's disease: clinical outcomes and genetic background in the era of biologics: a retrospective study in Japan.

Intestinal research·2025

Same author

JoGo 1.0: the ACTG hierarchical nomenclature and database covering 4.7 million haplotypes across 19,194 human genes.

Nucleic acids research·2025

Same author

QTFPred: robust high-performance quantum machine learning modeling that predicts main and cooperative transcription factor bindings with base resolution.

Briefings in bioinformatics·2025

Search research articles

Related Experiment Video

Updated: May 10, 2026

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian

Naoki Nariai¹, Osamu Hirose, Kaname Kojima

¹Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan. nariai@megabank.tohoku.ac.jp

Bioinformatics (Oxford, England)

|July 4, 2013

Summary

This study introduces a novel statistical method for accurately estimating transcript isoform abundances from RNA sequencing (RNA-Seq) data. The new approach improves quantification accuracy and consistency, outperforming existing methods on simulated and real biological datasets.

More Related Videos

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

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

Related Experiment Videos

Last Updated: May 10, 2026

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

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

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Alternative splicing in human genes generates diverse protein functions.
Accurate transcript isoform abundance estimation from RNA sequencing (RNA-Seq) is challenging due to read mapping ambiguities.

Purpose of the Study:

To develop a robust statistical method for precise transcript isoform abundance estimation from RNA-Seq data.
To address challenges posed by similar sequences of transcript isoforms and paralogs.

Main Methods:

A statistical method employing variational Bayesian inference for iterative optimization of transcript isoform numbers.
Handling of gapped alignments to accommodate insertion/deletion errors within reads.
Guaranteed convergence through a defined stopping criterion.

Main Results:

Outperformed comparable quantification methods in inferring transcript isoform abundances on simulated datasets.
Demonstrated faster convergence rates compared to the expectation-maximization algorithm.
Showed improved consistency among technical replicates for human cell line RNA-Seq data.

Conclusions:

The proposed method offers a more accurate and reliable approach for transcript isoform quantification.
Enhanced consistency in results across replicates suggests improved robustness.
The method provides a valuable tool for analyzing complex transcriptomes.