Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

4.4K
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...
4.4K
Proteomics01:33

Proteomics

10.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
10.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

5.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
5.0K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.6K
4.6K

You might also read

Related Articles

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

Sort by
Same author

Blood extracellular vesicles contribute to the exercise-mediated suppression of brain Aβ pathology in the App<sup>NL-G-F</sup> knockin mouse model of Alzheimer's disease.

Neurochemistry international·2026
Same author

Author Correction: Spatial fibroblast niches define Crohn's fistulae.

Nature·2025
Same author

Spatial fibroblast niches define Crohn's fistulae.

Nature·2025
Same author

Oral administration of arginine suppresses Aβ pathology in animal models of Alzheimer's disease.

Neurochemistry international·2025
Same author

Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture.

Neural computing & applications·2025
Same author

Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.

PloS one·2025

Related Experiment Video

Updated: Apr 15, 2026

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
08:23

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

Published on: February 18, 2022

4.3K

Outlier detection at the transcriptome-proteome interface.

Yawwani Gunawardana1, Shuhei Fujiwara2, Akiko Takeda2

  • 1School of Electronics and Computer Science, University of Southampton, Southampton, UK.

Bioinformatics (Oxford, England)
|March 31, 2015
PubMed
Summary

This study introduces two computational models to predict protein levels from mRNA data, identifying proteins likely regulated post-translationally. These methods systematically detect outliers, revealing insights into gene regulation.

More Related Videos

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
07:38

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

13.5K
Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.9K

Related Experiment Videos

Last Updated: Apr 15, 2026

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
08:23

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

Published on: February 18, 2022

4.3K
Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
07:38

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

13.5K
Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.9K

Area of Science:

  • Computational biology
  • Systems biology
  • Molecular biology

Background:

  • Transcriptome and proteome expression levels often lack strong correlation.
  • Messenger RNA (mRNA) levels are frequently used as proxies for protein levels.
  • Computational models are needed to bridge the gap between mRNA and protein levels due to regulatory mechanisms.

Purpose of the Study:

  • Develop data-driven computational models to predict protein levels from mRNA and translation efficiency.
  • Systematically identify outliers indicative of post-translational regulation.
  • Compare two novel formulations for protein concentration prediction.

Main Methods:

  • Developed an outlier-rejecting regression approach using a difference of convex functions algorithm (DCA).
  • Implemented a second method using quantile regression with an asymmetric loss function to identify low-concentration outliers.
  • Validated both methods on a yeast transcriptome and proteome dataset.

Main Results:

  • Both novel formulations successfully predicted protein concentrations and identified outliers.
  • Detected outliers were statistically validated as post-translationally regulated genes.
  • The methods demonstrated high confidence in identifying post-translationally regulated genes.

Conclusions:

  • The developed computational models effectively bridge the gap between transcriptome and proteome data.
  • Systematic outlier detection is a reliable strategy for identifying post-translational regulation.
  • These methods advance the understanding of gene expression regulation beyond mRNA levels.