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

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV.

Nature communications·2026

Same author

[Effects of Inorganic Fertilization on Soil Inorganic Carbon in Northern China's Croplands: A Meta-analysis].

Huan jing ke xue= Huanjing kexue·2026

Same author

Design, Synthesis, and Mechanism of Action Study of Novel Tioxazafen-Derived Nematicides Bearing Benzoxazine Moiety.

Journal of agricultural and food chemistry·2026

Same author

Development and validation of interpretable machine learning models for predicting stroke in NVAF patients with CHA<sub>2</sub>DS<sub>2</sub>-VA scores ≤1.

Frontiers in cardiovascular medicine·2026

Same author

Is Chinese Queer a Void? Cui Zi'en, Leo Bersani, and the Representations of Voids in Peach-Colored Lips.

Journal of homosexuality·2026

Same author

Genetic evidence for causal relationship between general cognition and treatment resistance in schizophrenia.

Translational psychiatry·2026

Same journal

Genetic survey of biomarkers at early and mid-pregnancy identifies pregnancy-specialized immune regulation.

PLoS genetics·2026

Same journal

Argonaute proteins orchestrate Meiotic Sex Chromosome Inactivation and timing of the spermatogenic transcriptional program.

PLoS genetics·2026

Same journal

Genome wide association study meta-analysis of neuropathologic lesions of Alzheimer's disease and related dementias in a multi-site autopsy cohort.

PLoS genetics·2026

Same journal

Microtubule stiffening by the doublecortin-domain protein ZYG-8 contributes to mitotic spindle orientation during zygote division in Caenorhabditis elegans.

PLoS genetics·2026

Same journal

Multiple instance fine-mapping: Predicting causal regulatory variants with a deep sequence model.

PLoS genetics·2026

Same journal

Nuclear ubiquitin-conjugating enzyme TrUbc4 and F-box protein TrFwd1-mediated modification of Cre1 in Trichoderma reesei establishes a regulatory mechanism for carbon catabolite repression.

PLoS genetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 1, 2025

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

INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.

Kai Zhao¹, Sen Huang², Cuichan Lin³

¹Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.

|March 14, 2024

Summary

This summary is machine-generated.

INSIDER is a new statistical framework for analyzing RNA sequencing data. It effectively handles multiple biological variables and their interactions, enabling dimension reduction and revealing complex biological insights.

More Related Videos

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

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

Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Related Experiment Videos

Last Updated: Jul 1, 2025

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

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

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

Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

RNA sequencing (RNA-Seq) is crucial for understanding transcriptome dynamics.
Existing methods struggle to simultaneously analyze multiple biological variables and their interactions while performing dimension reduction.
There is a need for flexible frameworks to handle complex, high-dimensional RNA-Seq data.

Purpose of the Study:

To introduce INSIDER, a novel statistical framework for RNA sequencing data analysis.
To enable simultaneous analysis of multiple biological variables and their interactions with dimension reduction.
To provide a flexible and computationally efficient tool for uncovering biological insights from complex transcriptomic data.

Main Methods:

INSIDER utilizes a matrix factorization approach to decompose variation.
It incorporates an elastic net penalty for sparsity and gene grouping effects.
The framework supports dimension reduction for data with three or more dimensions and accommodates missing data.

Main Results:

INSIDER effectively decomposes variation from multiple biological variables and their interactions into a shared latent space.
It achieves dimension reduction for high-dimensional data, outperforming or matching competing methods like SDA in simulations.
The method successfully handles complex missing data and can be applied when data cannot be structured as a tensor.

Conclusions:

INSIDER offers a general and flexible framework for advanced RNA sequencing data analysis.
It enables the computation of adjusted expression profiles, controlling for unwanted variation.
Real-world applications demonstrate INSIDER's utility in disease subtyping, neuro-developmental trajectory analysis, and uncovering biological processes.