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

Amino Acid Biosynthetic Pathways01:29

Amino Acid Biosynthetic Pathways

401
Amino acid biosynthesis is essential for cell growth, protein synthesis, and metabolic regulation. Cells generate essential and non-essential amino acids from metabolic intermediates to sustain vital biological functions. These intermediates originate from key metabolic pathways: glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway. Important precursors include α-ketoglutarate, pyruvate, oxaloacetate, phosphoenolpyruvate, and erythrose-4-phosphate, which...
401

You might also read

Related Articles

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

Sort by
Same author

Combined mesenchymal stem cells and metformin therapy modulates key macromolecular pathways in pulmonary fibrosis based on evidence from untargeted metabolomics.

Scientific reports·2026
Same author

Cohort profile Davos Alzheimer's Collaborative DAC Egypt Cohort.

npj aging·2026
Same author

Functional microbial shifts and host-microbiome crosstalk in colorectal cancer: insights from a metaproteomic approach.

BMC microbiology·2026
Same author

STRIKER: a spectral metadata repairing tool for expanding the comprehensiveness of spectral libraries.

Journal of cheminformatics·2026
Same author

A fin-loop-like structure in GPX4 underlies neuroprotection from ferroptosis.

Cell·2025
Same author

Single-cell analysis reveals shared and distinct molecular signatures in brain organoid models of neurodegeneration and neuroinflammation.

Alzheimer's research & therapy·2025

Related Experiment Video

Updated: Nov 2, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.1K

Xconnector: Retrieving and visualizing metabolites and pathways information from various database resources.

Ali Mostafa Anwar1, Eman Ali Ahmed2, Mohamed Soudy1

  • 1Proteomics and Metabolomics research program, Basic research department, Children's Cancer Hospital 57357 (CCHE-57357), Cairo, Egypt.

Journal of Proteomics
|June 10, 2021
PubMed
Summary

Xconnector simplifies metabolomics data retrieval and visualization by integrating diverse databases. This tool aids researchers in medical diagnostics and biomarker discovery by consolidating complex information.

Keywords:
DatabaseMetabolomicsPythonSoftware

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.9K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.2K

Related Experiment Videos

Last Updated: Nov 2, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.9K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.2K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Metabolomics

Background:

  • Metabolomics databases offer vital biological data but vary in format, complicating retrieval and analysis for researchers.
  • The heterogeneity of data types and sources across multiple metabolomics databases necessitates manual investigation, hindering efficient research workflows.

Purpose of the Study:

  • To develop Xconnector, a software package for seamless retrieval and visualization of metabolomics data from diverse public databases.
  • To provide researchers with a unified platform for accessing and analyzing metabolomics information, facilitating biomarker discovery and personalized medicine.

Main Methods:

  • Xconnector parses data from multiple metabolomics databases including HMDB, LMDB, YMDB, T3DB, ReSpectDB, The Blood Exposome Database, Phenol-Explorer, KEGG, and SMPDB.
  • The software utilizes Python to connect to databases, retrieve specified metabolites, reformat data into a single CSV file, and generate automated graphical outputs.
  • Xconnector is available as an executable application and a Python package for cross-platform compatibility.

Main Results:

  • Successfully integrated and processed data from nine major metabolomics databases.
  • Generated a unified Excel CSV file containing consolidated metabolomics information from various sources.
  • Automated the creation of publication-ready graphical data visualizations, significantly reducing analysis time.

Conclusions:

  • Xconnector effectively addresses the challenge of data fragmentation in metabolomics research.
  • The tool empowers researchers with efficient access to comprehensive metabolomics data, supporting advancements in medical diagnostics and personalized medicine.
  • The availability of Xconnector as both an application and a Python package enhances its accessibility and utility for the scientific community.