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

Cell-type-resolved genetic variation shapes inflammatory bowel disease risk.

Nature·2026

Same author

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026

Same author

Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.

Statistics and computing·2026

Same author

Design and interpretation of eQTL-GWAS colocalisation studies: Lessons from a large-scale evaluation.

PLoS genetics·2026

Same author

Altered B cell activation contributes to the immunopathogenesis of childhood arthritis-associated uveitis.

Nature communications·2026

Same author

Common variation at 1q23.3, 2p23.3, 2q33.3, and 2p21 influences the risk of acute myeloid leukemia.

Blood·2026

Same journal

Computing inspired by the brain: a journey from algorithms to organoids.

Nature computational science·2026

Same journal

Systematic bottom-up coarse-graining of hydrated excess proton transport across scales.

Nature computational science·2026

Same journal

Gaining biological insights through supervised data visualization.

Nature computational science·2026

Same journal

The inequalities of GPU access.

Nature computational science·2026

Same journal

Social technologies need societal alignment.

Nature computational science·2026

Same journal

The Quantum Optimization Benchmarking Library.

Nature computational science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 7, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL.

Elena Vigorito¹, Wei-Yu Lin¹, Colin Starr¹

¹MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Nature Computational Science

|January 7, 2022

Summary

This summary is machine-generated.

BaseQTL, a novel Bayesian method, maps gene expression quantitative trait loci (eQTL) using allele-specific expression, even without genetic data. This approach increases power and reduces errors, offering new insights into gene regulation.

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

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Related Experiment Videos

Last Updated: Oct 7, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

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

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genomics
Statistical genetics
Molecular biology

Background:

Detecting quantitative trait loci (QTL) typically requires genotyped individuals.
Allele-specific expression (ASE) is a molecular phenotype influenced by genetic variants.

Purpose of the Study:

To introduce BaseQTL, a Bayesian method for mapping molecular QTL, specifically expression QTL (eQTL), using ASE from sequencing reads.
To evaluate BaseQTL's performance with and without genotype data compared to existing methods.
To demonstrate the utility of genotype-free eQTL mapping in scenarios where genotypes are unavailable.

Main Methods:

Developed BaseQTL, a Bayesian statistical framework leveraging ASE to infer eQTL.
Applied BaseQTL to simulated and real sequencing data, both with and without available genotype information.
Compared BaseQTL's power, error rates, and eQTL effect estimates against established QTL mapping techniques.

Main Results:

BaseQTL achieves lower error rates and increased power for eQTL mapping when genotypes are available.
Even without genotypes, BaseQTL effectively maps eQTLs by focusing on variants near gene bodies, identifying a significant portion of detectable eQTLs.
eQTL effect estimates remain consistent whether genotypes are used or not.
Identified a potential psoriasis-specific eQTL for GSTP1 in a dataset lacking genotypes, suggesting novel disease-gene regulatory insights.

Conclusions:

BaseQTL provides a powerful and flexible tool for eQTL mapping, adaptable to scenarios with or without genotype data.
Genotype-free eQTL mapping using BaseQTL is feasible and can uncover biologically relevant associations, particularly for variants proximal to genes.
The method offers new avenues for investigating gene regulation in diverse biological contexts, including differential expression studies without genetic information.