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

Proteomics01:33

Proteomics

8.8K
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...
8.8K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.2K
4.2K
Interpreting R Charts01:22

Interpreting R Charts

203
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
203

You might also read

Related Articles

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

Sort by
Same author

Computationally Efficient Bayesian Estimation of Graphical Networks for Omics Data.

Journal of proteome research·2026
Same author

Comparative Analysis of Report-back of Research Results Strategies for Personal Chemical Exposure Data.

Journal of exposure science & environmental epidemiology·2026
Same author

Aggregation Methods for Quantifying PTM and Structural Changes in Bottom-Up Proteomics.

Journal of proteome research·2026
Same author

Human Coronavirus 229E Infection Alters Histone Proteoforms.

Journal of proteome research·2026
Same author

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data.

Journal of proteome research·2026
Same author

Unsupervised Semantic Segmentation Models for Region of Interest Identification.

Journal of the American Society for Mass Spectrometry·2026

Related Experiment Video

Updated: Nov 15, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

15.7K

PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.

David J Degnan, Lisa M Bramer, Amanda M White

    Journal of Proteome Research
    |March 4, 2021
    PubMed
    Summary

    PSpecteR is a new open-source R Shiny web application for visualizing liquid chromatography-mass spectrometry (LC-MS) data. It aids in assessing data quality, exploring results, and preparing publication-ready figures for proteomics research.

    Keywords:
    bottom-uppeptide database searchproteomics web applicationtandem mass spectrometrytop-down

    More Related Videos

    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
    07:28

    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

    Published on: October 19, 2021

    3.5K
    Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
    07:01

    Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

    Published on: August 19, 2025

    412

    Related Experiment Videos

    Last Updated: Nov 15, 2025

    Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
    09:51

    Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

    Published on: July 16, 2017

    15.7K
    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
    07:28

    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

    Published on: October 19, 2021

    3.5K
    Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
    07:01

    Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

    Published on: August 19, 2025

    412

    Area of Science:

    • Proteomics
    • Computational Biology
    • Data Visualization

    Background:

    • Visual examination of mass spectrometry (MS) data is crucial for quality assessment and data exploration in proteomics.
    • Existing visualization tools are often proprietary, workflow-specific, or lack open-source accessibility, limiting flexibility.

    Purpose of the Study:

    • To develop an open-source, interactive R Shiny web application for comprehensive visualization of liquid chromatography-mass spectrometry (LC-MS) data.
    • To provide a flexible platform supporting various stages of proteomics data processing and analysis.

    Main Methods:

    • Developed PSpecteR, an R Shiny application integrating functionalities for reading diverse MS files.
    • Incorporated tools for running open-source database search engines, labeling spectra, and testing post-translational modifications.
    • Enabled plotting of fragment mapping to reference sequences and visualization of algorithmic output and metadata.

    Main Results:

    • PSpecteR offers an interactive graphical user interface for visualizing LC-MS data and associated metadata.
    • All generated figures, tables, and spectra are exportable, facilitating publication and further analysis.
    • The application supports various proteomics data processing steps within a unified framework.

    Conclusions:

    • PSpecteR provides a flexible, modern, and open-source solution for LC-MS data visualization in proteomics.
    • The R framework facilitates the addition of new features, enhancing its utility for researchers.
    • Accessible code and a Docker container simplify installation and adoption for the scientific community.