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

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.

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...

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability

You might also read

Related Articles

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

Sort by

Same author

PXN Unlocks the Power of Public Gene Expression Data Through Cross-Technology Integration.

bioRxiv : the preprint server for biology·2026

Same author

Evolutionary Dynamics of Chromatin Structure and Duplicate Gene Expression in Diploid and Allopolyploid Cotton.

Molecular biology and evolution·2024

Same author

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.

Database : the journal of biological databases and curation·2023

Same author

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.

Database : the journal of biological databases and curation·2022

Same author

Pre-trained models, data augmentation, and ensemble learning for biomedical information extraction and document classification.

Database : the journal of biological databases and curation·2022

Same author

The native cistrome and sequence motif families of the maize ear.

PLoS genetics·2021

Search research articles

Related Experiment Video

Updated: Dec 1, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Maximizing the reusability of gene expression data by predicting missing metadata.

Pei-Yau Lung¹, Dongrui Zhong¹, Xiaodong Pang²

¹Department of Statistics, Florida State University, Tallahassee, United States of America.

Plos Computational Biology

|November 6, 2020

Summary

This study developed a framework to predict missing metadata in gene expression datasets, enhancing data reusability. Using accurately predicted data subsets, not all predicted data, optimizes downstream analyses.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Related Experiment Videos

Last Updated: Dec 1, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Genomics
Bioinformatics
Data Science

Background:

Data reusability is crucial for the FAIR data principles.
Missing metadata significantly hinders the usability of public genomics data.
Current efforts focus on metadata inclusion to improve data reusability.

Purpose of the Study:

To develop a framework for predicting missing metadata in gene expression datasets.
To maximize the reusability of public genomics data by addressing missing metadata.
To optimize the use of predicted data in downstream analyses.

Main Methods:

Developed a machine learning pipeline to predict missing gene expression metadata.
Proposed a novel metric, Proportion of Cases Accurately Predicted (PCAP), for evaluating prediction accuracy.
Compared the proposed approach with standard metrics like F1-score.

Main Results:

The proposed framework effectively predicts missing metadata, improving data reusability.
Using only accurately predicted data subsets is optimal for downstream analyses.
The PCAP metric and optimized pipeline outperformed standard methods in maximizing data reusability.
Different variables may require distinct prediction methods and data processing.

Conclusions:

Accurate prediction of missing metadata enhances the reusability of gene expression datasets.
The PCAP metric and tailored machine learning approaches are effective for optimizing data utility.
Reliable downstream analyses, such as differential gene expression, are possible with accurately predicted data.